Department of Computer Science, University of Manchester, Manchester, UK and
Department of Computer Science, University of Oxford, Oxford, UK
jiaoyan.chen@manchester.ac.ukhttps://orcid.org/0000-0003-4643-6750supported by
the EPSRC project ConCur (EP/V050869/1). Department of Computer Science,
University of Oxford, Oxford,
UKhang.dong@cs.ox.ac.ukhttps://orcid.org/0000-0001-6828-6891supported by the
EPSRC project ConCur (EP/V050869/1). Institute for Implementation Science in
Health Care, University of Zurich, Switzerland and School of Medicine,
University of St. Gallen,
Switzerlandjanna.hastings@uzh.chhttps://orcid.org/0000-0002-3469-4923supported
by the School of Medicine of the University of St. Gallen. City, University of
London, UK and SIRIUS, University of Oslo, Norway and
https://www.city.ac.uk/about/people/academics/ernesto-jimenez-
ruizernesto.jimenez-
ruiz@city.ac.ukhttps://orcid.org/0000-0002-9083-4599supported by the SIRIUS
Centre for Scalable Data Access (Research Council of Norway, project 237889).
IBM Research Europe,
Irelandvanlopez@ie.ibm.comhttps://orcid.org/0000-0002-0674-5324 Université
Côte d’Azur, Inria, CNRS, I3S, France and
https://pmonnin.github.iopierre.monnin@inria.frhttps://orcid.org/0000-0002-2017-8426
LASIGE, Faculdade de Ciências, Universidade de Lisboa,
Portugalclpesquita@fc.ul.pthttps://orcid.org/0000-0002-1847-9393funded by the
FCT through LASIGE Research Unit (ref. UIDB/00408/2020 and ref.
UIDP/00408/2020), and also partially supported project 41, HfPT: Health from
Portugal, funded by the Portuguese Plano de Recuperação e Resiliência.
Department of Software Engineering, Faculty of Mathematics and Physics,
Charles University, Prague,
Czechiapetr.skoda@matfyz.cuni.czhttps://orcid.org/0000-0002-2732-9370
Department of Computer Science, University of Liverpool,
UKv.tamma@liverpool.ac.ukhttps://orcid.org/0000-0002-1320-610X Jiaoyan Chen,
Hang Dong, Janna Hastings, Ernesto Jimenez-Ruiz, Vanessa López, Pierre Monnin,
Catia Pesquita, Petr Škoda, and Valentina Tamma [500]Information systems
Graph-based database models [500]Computing methodologies Knowledge
representation and reasoning [500]Applied computing Life and medical sciences

###### Acknowledgements.

We would like to thank Uli Sattler (University of Manchester) for proposing
the topic of this paper and Terry Payne (University of Liverpool) for the
useful comments on a previous draft. We would also like to thank the TGDK
editors in chief for organizing this inaugural issue.1 1 42 Date of submission
Date of acceptance Date of publishing TGDK section area editor

#  Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and
Opportunities111Authors are listed in alphabetic order with authors’
contributions presented at the end of the article. Jiaoyan Chen, Hang Dong,
Janna Hastings, and Valentina Tamma are corresponding authors.

Jiaoyan Chen  Hang Dong  Janna Hastings  Ernesto Jiménez-Ruiz  Vanessa López
Pierre Monnin  Catia Pesquita  Petr Škoda  Valentina Tamma

###### Abstract

The term life sciences refers to the disciplines that study living organisms
and life processes, and include chemistry, biology, medicine, and a range of
other related disciplines. Research efforts in life sciences are heavily data-
driven, as they produce and consume vast amounts of scientific data, much of
which is intrinsically relational and graph-structured.

The volume of data and the complexity of scientific concepts and relations
referred to therein promote the application of advanced knowledge-driven
technologies for managing and interpreting data, with the ultimate aim to
advance scientific discovery.

In this survey and position paper, we discuss recent developments and advances
in the use of graph-based technologies in life sciences and set out a vision
for how these technologies will impact these fields into the future. We focus
on three broad topics: the construction and management of Knowledge Graphs
(KGs), the use of KGs and associated technologies in the discovery of new
knowledge, and the use of KGs in artificial intelligence applications to
support explanations (explainable AI). We select a few exemplary use cases for
each topic, discuss the challenges and open research questions within these
topics, and conclude with a perspective and outlook that summarizes the
overarching challenges and their potential solutions as a guide for future
research.

###### keywords:

Knowledge graphs; Life science; Knowledge discovery; Explainable AI

###### category:

Position

##  1 Introduction

The term life sciences refers to those disciplines that study living organisms
and life processes, and include chemistry, biology, medicine, and a range of
other related areas. Research efforts in life sciences are increasingly data-
driven, as they produce and consume vast amounts of scientific data, much of
which is intrinsically relational and graph-structured.

Much of this data is large-scale, complex, and presents many
interrelationships and dependencies, thus being well suited to be represented
in graph structures. For this reason, graph-based technologies are frequently
used in the life sciences, and these disciplines have been drivers and early
adopters of innovative methods and associated technologies.

In this brief survey and position paper we discuss recent developments and
advances in the use of graph-based technologies in life sciences, and set out
a vision for how these technologies will impact these fields in future. We
illustrate the contribution in this paper in Figure 1.

We consider Knowledge Graphs (KGs) and their associated technologies to
broadly include  (i) different forms of graph-based representations,  (ii) the
logical languages that assign explicit semantics to such representations, and
their associated automated reasoning technologies, and  (iii) machine learning
approaches that ingest data in graph-based representations and that process
these graph-based representations to perform some task, e.g., data analytics.

These different forms of graph-based representations can be further
categorized based on the type of content represented. We therefore distinguish
schema-less and schema-based Knowledge Graphs. More specifically, a typical KG
contains either or both a schema part (terminologies or TBox222We introduce a
list of key terms relevant to Knowledge Graphs and Life Sciences in Appendix
A.) and a data part (facts, assertions, or ABox). The formal semantics of KGs
can be expressed with the OWL ontology language333Web Ontology Language:
https://www.w3.org/OWL/.

In the remainder of this paper we will focus on three broad topic areas in
which graph-based technologies have been used extensively, and we illustrate
each area with some specific projects or use cases that guide our discussion
and summary of the challenges that have been encountered.

  * •

The construction and management of KGs to represent life science knowledge;

  * •

The use of KGs and associated technologies in the discovery of new knowledge;

  * •

The use of KGs in artificial intelligence applications to support explanations
(eXplainable AI or XAI).

Figure 1: An overview illustration of definitions (upper right, in grey),
topics (left column, in blue), use cases (middle), and challenges (bottom
right, in green) for the research of KGs in the life sciences.

We then provide a summary of the general challenges across the topics, that
include intrinsic characteristics of KGs (e.g., scalability, evolution,
heterogeneity) and their operational aspects in the real world (e.g., human
interaction, personalization, distributed setting, and representation
learning). We present the challenges by means of use cases and the current
research efforts that address them. It is worth mentioning that while we aim
to focus on the life sciences, many of the topics and challenges discussed in
this work, especially those of KG construction and management in Section 3,
are general and applicable to KGs in other domains such as finance,
e-commerce, material, and urban management [113, 32], etc. The KG-based
problem modeling and solving approaches in life science knowledge discovery
could be applicable for addressing many other use cases and problems in a
broader domain of AI for scientific discovery [177, 61].

In the next section, we introduce several different categories of KGs as they
have been used in life sciences. Thereafter in Sections 3-5, each of the above
topics is described in a dedicated section together with a survey of recent
advances. Finally, in Section 6 we synthesize the overarching challenges and
trends into a perspective on the outlook for the future.

##  2 Knowledge Graphs in the Life Sciences

KGs represent semantically-described real-world entities, typically through
ontologies (vocabularies or schemas) [69, 62] and the data instantiating them,
and thus provide descriptions of the entities of interest and their
interrelations, by means of links to ontology classes describing them,
organized in a graph [160]. KGs have been widely adopted in the life sciences,
as can be seen in the composition of the Linked Open Data
Cloud444http://cas.lod-cloud.net, where life sciences represent one of the
largest subdomains. A prominent example is the KG representing annotations
regarding proteins by means of terms in the Gene Ontology describing different
protein functions [4].

Whilst KGs are becoming increasingly popular in different domains including
the life sciences, there is no single accepted definition of KG [44]. A KG can
be formally described as a directed, edge-labeled graph $\mathcal{G}=(V,E)$,
where $V$ refers to the _vertices_ or _nodes_ , representing real-world
entities of interest (e.g., proteins, genes, compounds, cellular components,
but also pathways, biological processes and molecular functions, to name a
few) while $E$ refers to the edges in the graph, representing relationships or
links between the entities in $V$ (e.g., binds, associates, etc.). These may
be represented as statements about entities in the form of RDF555Resource
Description Framework: https://www.w3.org/RDF/ triples: (subject, predicate,
object).

However, this formal definition only focuses on the components of KGs, but
does not pose any constraint on what a KG should model or represent, and how.
This is particularly true in life sciences, where the term _Knowledge Graph_
has been used to refer to diverse graph data structures, typically
interconnected, but often isolated.

Many of the everyday tasks faced by researchers in this domain require the
systematic processing and integration of data and knowledge from data sources
that are characterized by heterogeneous syntaxes and structures, formats,
entity notation, schemas and scope, e.g., ranging from molecular mechanisms to
phenotypes. Researchers in this area have been early adopters of Semantic Web
and linked data approaches as a means to facilitate knowledge integration and
processing to support tasks including semantic search, clinical decision
support, enrichment analysis, data annotation and integration. However, a
recent analysis of life science open data has identified several stand-alone
data sources that exist in isolation, are not interlinked with other sources,
and are schema-less (or use unpublished schemas), with limited reuse or
mappings to other data sources [89]. Therefore, we can define a life sciences
KG, following [132], as a data resource integrating one or more possibly
curated sources of information into a graph whose nodes represent entities and
edges represent relationships between two entities. This definition is
consistent with other definitions found in the literature, e.g., [137].

These considerations underlie the reasons why KGs in life sciences can be of
different types, and can be categorized across different dimensions. One of
the most critical dimensions (in terms of support for complex queries and
integration) is the categorization of KGs into schema-based and schema-less
knowledge bases. In turn, the expressivity of the schema provides a further
categorization criterion, depending on whether schemas are modelled as simple
taxonomies (e.g., the NCBI taxonomy [156] included in the UMLS Metathesaurus
[10]), RDFS666RDF Schema: https://www.w3.org/TR/rdf-schema/ vocabularies or
(fully axiomatized) OWL ontologies. In particular, this paper refers to this
broad definition of KGs, which we then divide into:

  * •

Schema-less KGs composed of only relational facts in the form of RDF triples.
Examples include the PharmaGKB dataset, an integrated online knowledge
resource capturing how genetic variation contributes to variation in drug
response [182]. Note that many semantic networks (defined in Appendix A) could
be assigned to this category as their triples form a multi-relational graph.

  * •

Schema-based KGs composed of relational facts and their schema (meta
information) in e.g., RDFS, OWL, and constraint languages such as
SHACL777https://www.w3.org/TR/shacl/. Examples include Wikidata with its
property constraints, and DBpedia with its DBpedia ontology. Whilst Wikidata
and DBpedia are general-purpose KGs, they also include large-scale life
science knowledge.

  * •

Simple ontologies representing taxonomies. Notable examples include the tree
structure of the UMLS Semantic
Network888https://uts.nlm.nih.gov/uts/umls/semantic-network/root and the
International Classification of Diseases, version 10 (ICD-10) [184].

  * •

Expressive OWL ontologies, with complex axioms beyond simple taxonomies. OWL
ontologies may be composed of a TBox and an ABox. Depending on the
expressivity of the axioms modeled in the ontology, i.e., the basic statements
that an OWL ontology expresses, OWL ontologies can fall into one of the
previous categories: for instance, an OWL ontology with just an ABox can be
seen as the case above of a KG composed of relational facts alone. In this
final category we include fully axiomatized OWL ontologies, e.g., with complex
classes and property restrictions. Notable examples of these ontologies
include SNOMED CT [snomed], the Gene Ontology [4, 29], and the Food Ontology
(FoodOn)999http://foodon.org.

##  3 Knowledge Graph Construction and Management

The adoption of KGs in the life sciences is motivated by the need for
standardisation of taxonomies and vocabularies to support the integration,
exchange and analysis of data. More recently, richly annotated data is also
being used in combination with machine learning methods for many applications,
including helping to overcome issues related to the sparsity of data and
helping to select promising candidates for reducing expensive and time-
consuming physical experiments [65]. Graph-based machine learning approaches
such as Graph Neural Networks have been applied to a number of life science
tasks [50], including drug repurposing [122] and predicting polypharmacy side
effects [198].

Given the diverse nature of the knowledge and tasks supported by KGs, the
focus of state-of-the-art approaches has been the description of how
individual KGs are developed within the specific domain [192], typically in
terms of the specific approaches used for the development of the KG (e.g.,
data extraction process, relation extraction and entity discovery), rather
than on the overall development process. More recently, some efforts have
focused on providing an overview of development approaches and pipelines for
the construction of KGs in the life sciences, and beyond [132, 166]. The
process of constructing a KG depends heavily on:

  * •

The type of data sources integrated and annotated by the KG, e.g., CSV files,
public and proprietary data sources, structured databases, full-text
publications, etc.

  * •

The granularity of the KG to be constructed, e.g., schema-less KG, simple or
expressive ontology.

  * •

The usability expectations in downstream applications, e.g., the ability to
customize and manipulate the graph to support different use cases, or the ease
of consumption as input to machine learning methods [52].

A recent systematic review [166] surveyed different KG development approaches
to determine a general development framework. The review identified six main
phases that are common across different KG development approaches:

  * 1)

Data source selection.

  * 2)

Ontology construction.

  * 3)

Knowledge extraction.

  * 4)

Knowledge ingestion and validation.

  * 5)

KG storage and inspection.

  * 6)

KG maintenance and evolution.

In the remainder of this section we will present the individual phases and the
role they play in a KG development process by means of two use cases, where we
illustrate the construction of KGs and discuss how these support knowledge
integration and validation (Section 3.2). We then present some recent
technical developments in Section 3.3, while Section 3.4 discusses open
challenges for the construction and management of KGs.

###  3.1 Knowledge Graph Construction Phases

This section provides more details on the phases involved in the KG
construction process, with the aim of identifying recent trends, rather than
providing an exhaustive literature survey. These phases are discussed in order
of execution, however the ontology construction phase can occur either
together with the data source selection (if an ontology covering the domain of
interest already exists or can be constructed through a set of given
requirements) or as part of the knowledge ingestion and validation phase,
where an ontology is built semi-automatically from the available data or
through modularization and alignment of existing ontologies.

####  3.1.1 Data source selection

This phase identifies the data sources that are to be integrated by the KG,
which in turn affects the choice of knowledge extraction techniques.
Generally, life science KGs ingest knowledge from structured, semi-structured
and unstructured data sources. By structured we refer to data modeled
according to an existing structure, e.g., data in tables or public or
proprietary reference (relational) databases such as UniProt [30] or ChEMBL
[51]. Semi-structured data refer to, e.g., XML documents [118], whereas
unstructured data refer to data that do not conform to a given structure,
i.e., free-text sources, such as scientific publications from
PubMed101010https://pubmed.ncbi.nlm.nih.gov. Data ingested from manually
curated databases [132] and semi-structured sources constitute the foundation
of a KG [52], generally defining the entities and some of the relations in the
KG. This data is then further enriched by performing text mining on large-
scale free text sources, in order to extract relationships, which is the
objective of the knowledge extraction phase.

####  3.1.2 Ontology construction

The aim of this phase is to define a common, consensus-based, controlled
vocabulary to describe the data in an _ontology_ [148]. The existence of a
common structure, or schema, supports querying, integration and reasoning
tasks over the KG.

Traditional ontology engineering approaches are divided into top-down or
bottom-up. Top-down approaches are based on more or less formal ontology
engineering methodologies [46, 97, 133] or common practices [3] to build
ontologies from a description of the domain elicited from domain experts
[131], and/or by reusing or extending existing ontologies [83]. Ontology
engineering methodologies define the ontology development process in terms of
requirement analysis, entity and property definitions, ontology reuse,
validation and population. In contrast, bottom-up approaches utilize semi-
automatic data driven techniques, e.g., ontology learning from text [112], and
can be used to refine and validate an ontology. These approaches are discussed
in more detail when presenting the knowledge ingestion and validation phase.

Whilst general purpose ontology engineering methodologies have evolved to be
used in the development of KGs [141], a considerable number of ontologies in
the life science domain have been built as part of the Open Biological and
Biomedical Ontologies (OBO) Foundry effort,111111https://obofoundry.org which
defines a set of development principles for biological and biomedical
ontologies and provides a suite of high-quality, interoperable, free and open
source tools that support ontology development [117].

####  3.1.3 Knowledge extraction

Knowledge extraction refers to the identification of entities and their
relations from the data sources, which is a crucial step in the development of
a KG [166]. Entity extraction identifies entities from the various data
sources selected using Natural Language Processing (NLP) approaches and text
mining techniques to analyse and extract relevant information from large text
corpora [180, 105, 72]. Named entity recognition (NER) supports the
identification of named entities in text, such as drug names, diseases, or
chemical compounds, and their classification according to pre-defined entity
types [129]. NER approaches in the life sciences are typically based on labour
intensive tasks such as the definition of generic (e.g., orthographic,
morphological, or dictionary-based) and specific rules that are typically
defined by experts, and are not easily applicable to other corpora [197].
There are a number of issues hindering these approaches:  a) the pace of
scientific discovery and the identification of new entities;  b) the large
number of synonyms and term variations associated with an entity; and  c)
entity identifiers that are composed of a mixture of letters, symbols and
punctuation, often in large sentences [103].  More recent approaches have
proposed the use of supervised machine learning methods (e.g., conditional
random fields, or Support Vector Machines, SVMs, neural networks, and neural
language models in particular) [114, 87, 36] either in isolation, or combined
in hybrid approaches to improve accuracy [151].

Entity recognition generates entities that are isolated and not linked [166].
The goal of Relation extraction is to discover relationships of interest
between a pair of entities, thus describing their interaction. Relation
extraction is a necessary step for entities defined in semi-structured or
unstructured sources, whereas structured data sources are characterized by
explicitly identifiable relationships. Typical approaches for relation
extraction include rule-based [76, 147, 146], supervised [108, 49] and
unsupervised approaches [100, 132]. Rule-based relation extraction identifies
keywords (based on existing ontologies or expert defined dictionaries) and
grammatical patterns to discover relations between entities. Supervised
relationship extraction methods utilize publicly available pre-labelled
datasets (e.g., BioInfer [143] or BioCreative II [99]) to construct
generalized patterns that separate positive examples (sentences implying the
existence of a relationship) from negative ones. Supervised approaches include
SVMs, Recurrent Neural Networks (RNNs) and Convolutional Neural Networks
(CNNs) [7, 132]. Unsupervised relation extraction methods [115] have emerged
to address the lack of scalability of supervised relation extraction methods,
due to the high cost of human annotation. Unsupervised methods involve some
form of clustering or statistical computation to detect the co-occurrence of
two entities in the same text [132].

More recently, end-to-end approaches (End-to-End Relation Extraction – RE)
have been used to tackle both tasks simultaneously. In this scenario, a model
is trained simultaneously on both the NER and Relation Extraction objectives
[75]. Furthermore, rule-based approaches can be combined with relation
classification using specialized pre-trained language models adapted for life
science domains, e.g., BioBERT [104], SapBERT [110], and RoBERTa-PM [106], to
name a few. There is also a recent trend to probe and prompt pre-trained
language models to extract relations (e.g., disease-to-disease, disease-to-
symptoms) [189, 165].

####  3.1.4 Knowledge ingestion and validation

The aim of this phase is to ingest the entities and relationships extracted in
a previous phase, which models knowledge from different sources. These
entities and relations can be incomplete, ambiguous or redundant, and need to
be appropriately aligned and integrated, and finally annotated according to
the ontology constructed in phase 2.

Knowledge integration or fusion can critically improve the quality of data by
performing entity resolution, i.e., the detection of different descriptions of
the same real-world entity (also called entity matching, deduplication, entity
linkage or entity canonicalization), prior to ingesting them in the KG. This
reconciliation step is particularly crucial in the life sciences, where
duplication can be caused by data modelled using different vocabularies or
ontologies, or when data is extracted from literature sources that are rapidly
changing. The severity of the ambiguity depends on the number of ontologies
available for the domain. For instance, the number of gene vocabularies is far
smaller than the number of disease vocabularies that could be present in the
ingested datasets. Linking these entities requires costly alignment
processing; in particular the alignment of disease entities is especially
problematic given the number of different coding systems, whose conversion is
often not trivial [52]. We further explore this issue in two of the use cases
presented in Section 3.3, where we explore the problem of aligning
vocabularies and ontologies through the use of mapping repositories and
instance matching in automated clinical coding.

Entities are assigned unique identifiers (URI or IRI) that support the
definition of bespoke namespaces, and support integration by reusing
identifiers in related namespaces. Entity resolution is based on clustering
similar entities together in a block, where similarity measures are used to
detect duplicates [166]. Typical methods include sorted neighborhoods and
traditional blocking; and machine learning methods are commonly used for
similarity computation, e.g., feature vector computation [95].

This phase may also include the bottom-up construction of the ontology for
those applications where a top-down approach is not feasible. Bottom-up
approaches extract the relevant knowledge first, and then they construct the
data schema / ontology based on the extracted data, typically using
(semi-)automated methods, based on machine learning. Ontologies define the
structure of the knowledge graph, which supports querying and data analytics.
In bottom-up ontology development the structure of the knowledge graph is
determined based on the extracted knowledge, thus providing a structure for
this knowledge [70].

Often the construction of ontologies (either bottom-up or top-down) relies on
the ability to correctly align and reuse entities defined across different
domains and KGs. Furthermore, reuse of (or conformance to) existing upper
level ontologies, e.g., BFO (Basic Formal Ontology) [3] provides the basis for
the consistent and unambiguous formal definition of entities and relations
that prevents errors in coding and annotation. The alignment of ontologies in
life sciences and other domains is an active area of research, and we provide
an overview of recent technical developments and challenges in Section 3.3.

Whilst bottom-up approaches, especially those based on alignment, are becoming
more viable, especially given the support of language models, such as BERT
[64], their performance is not always adequate for the task, as discussed in
the second challenge in Section 3.4.

Knowledge enrichment and completion improve the KG quality by performing
reasoning (KG materialization), inference [57] and optimization. Reasoning and
inference support the assertion of new relations based either on logical
reasoning (e.g., [130, 172]) or machine learning techniques (e.g., statistical
relational learning or through embedding based link predictors for new
concepts [35, 36, 67, 77] and node classifiers, also called KG refinement
[137]). The extent and type of logical inferences depends on the expressivity
of the ontology built in phase 2, or in a bottom-up fashion in this phase,
together with any associated mappings. Description Logic formalisms, such as
OWL, use logic-based reasoning for detecting and correcting incorrect
assertions and ontology alignments [25].

####  3.1.5 KG storage and inspection

KGs need to be accessible to support a variety of different tasks, beyond the
mere integration of different knowledge sources, and thus KG storage
management [166, 144, 179] is an active area of research. Current KG storage
mechanisms are divided into relation based stores (e.g., [1]) and native graph
stores (e.g., [199]). Relational KG stores, either based on relational
databases or through NOSQL databases and / or triple stores such as Jena
TDB121212https://jena.apache.org/documentation/tdb/index.html, have reached a
considerable level of maturity and have been optimized in order to avoid
common problems, e.g., a large number of null values in columns or optimized
query performance [144]. Graph databases store nodes, edges and properties of
graphs natively, and support query and graph mining tasks. Examples of state
of the art implementations include Neo4J131313https://neo4j.com,
GraphDB141414https://graphdb.ontotext.com, and
RDFox151515https://www.oxfordsemantic.tech/product. The evolution of the
performance of these systems has been the object of systematic studies [9],
whereas [170] explicitly focuses on biomedical use cases.

Storage management has implications on the ways KGs support expressive queries
for nodes and edges and visualization, to support data analysis, navigation
and discovery of related knowledge [95, 164]. Graph databases often provide
built-in tools for visualization, e.g., Neo4J, whereas different Javascript
libraries (e.g., SigmaJS161616https://github.com/jacomyal/sigma.js) are
available for developing visualization front ends. Support for complex queries
is also either built in a graph database or a triple store by supporting the
SPARQL query language [142, 199], or proprietary query languages such as
Cypher [48], supported by Neo4J.

####  3.1.6 Knowledge maintenance and evolution

Given the rapid scientific development in the life sciences, and the
consequent continuous update of ontologies for this domain, artefacts
annotated with these ontologies can become outdated very quickly, and require
some form of update (also called ontology extension). These update mechanisms
need to be automated to ensure that they scale to the size of KGs. Automatic
update approaches are based on the periodical detection and extraction of new
knowledge that is then mapped to existing entities and relations in the KG
[185].

Update mechanisms are typically based on the detection of changes [123] that
can affect an ontology, e.g., addition, removal or modification of meta-
entities (i.e., entities, relations and their definitions). These changes
include renaming concepts and properties, setting domain and range
restrictions, or setting a subsumption relation. To date, the most
comprehensive account of ontology change is given in [47], where change is
described for different sub-fields, e.g., ontology alignment, matching and
mapping, morphisms, articulation, translation, evolution, debugging,
versioning, integration and merging; each with different requirements and
implications. The study [139] further investigates the impact of biomedical
ontology evolution on materialization.

Currently available tools and methodologies use (semi)-automated methods to
perform many of the operations that trigger a change in an ontology and the
consequent creation of a new version [55, 64]. Different ontology management
platforms and portals mandate different principles and frameworks for handling
ontology versioning (e.g., OBO
foundry171717http://www.obofoundry.org/principles/fp-004-versioning.html or
BioPortal181818https://bioportal.bioontology.org), but these are typically
implemented by ontology developers with limited tool support. Section 3.3
presents an example of automated ontology extension that relies on machine
learning to cope with the scale of data.

###  3.2 Examples of Life Science KG Construction

In this section we provide two examples of life science KGs that illustrate in
practice the phases composing the generic KG construction process discussed in
Section 3; namely a KG for Pharmacogenomics, PGxLOD [120], and one for
Ecotoxicological Analysis, TERA [126, 127].

Alignment for Knowledge Validation: An Example of Pharmacogenomics. As
mentioned in Section 3, the task of aligning knowledge in KGs supports several
downstream applications and domains. For instance, pharmacogenomics studies
the influence of genetic factors on drug response phenotypes (e.g., expected
effect, side effect). Hence, pharmacogenomics is of interest for personalized
medicine. The atomic knowledge unit in pharmacogenomics is a ternary
relationship between a drug, a genetic factor, and a phenotype. Such a
relationship states that a patient being treated with the specified drug while
having the specified genetic factor may experience the described phenotype.
Semantic Web and KG technologies have been employed in this application
domain, for example by building ontologies in which patients and
pharmacogenomic knowledge are represented, and then using deductive reasoning
mechanism to conditionally recommend genetic testing before drug prescription
[155]. However, the knowledge relevant to pharmacogenomics is scattered across
several sources including reference databases such as PharmGKB, and the
biomedical literature. Additionally, this knowledge may lack sufficient
validation to be implemented in clinical practice. For example, some
relationships may have only been observed in smaller cohorts of patients or in
non-replicated studies. Hence, there is a need to align different sources of
pharmacogenomic knowledge to detect additional evidence validating (or
moderating) a knowledge unit. To this aim, the PGxLOD KG was proposed [120].
Automatic knowledge extraction approaches were applied on semi-structured and
unstructured data from PharmGKB and the biomedical literature to represent
their knowledge in the KG. Then, matching approaches were developed to align
knowledge units from various sources [119, 121]. The resulting alignments
outlined some agreements between PharmGKB and the biomedical literature, which
was expected since PharmGKB is manually completed by experts after reviewing
the literature. Interestingly, this automatic knowledge extraction pipeline
could guide the manual review process by automatically pointing out studies
confirming or mentioning a pharmacogenomic knowledge unit.

Knowledge Integration: An Example of Ecotoxicological Analysis. In
ecotoxicological analysis, data and knowledge from different domains such as
chemistry and biology are often needed. These are usually located in different
sources such as spreadsheets or CSV files for local experimental results, open
databases for public research results, and ontologies for domain knowledge.
Thus knowledge integration becomes a critical and fundamental challenge before
real analysis can be conducted. In the study by Myklebust et al. [126, 127],
which aims to predict adverse biological effects of chemicals on species, a
toxicological effect and risk assessment KG named TERA was constructed for
knowledge integration. TERA includes three sub-KGs: (i) the Chemical sub-KG,
which is constructed by integrating the vocabulary MeSH (Medical Subject
Headings) with selective knowledge from two chemical databases PubChem and
ChEMBL utilizing the chemical mappings in Wikidata; (ii) the Taxonomy sub-KG,
which is constructed by integrating EOL (Environment Ontology for Livestock)
and the NCBITaxon ontology utilizing NIBI-EOL mappings in Wikidata; and (iii)
the ECOTOX sub-KG, which is composed of RDF triples transformed from
experimental risk results and is aligned with the other two sub-KGs by the
ontology alignment system LogMap [81] and the chemical mappings in Wikidata.
Another example of knowledge integration is for drug repurposing, where the KG
Hetionet191919https://github.com/hetio/hetionet is created by integrating 29
public resources, including biomedical KGs and other types of data [68].

###  3.3 What has been done: recent technical developments

Given the many existing ontologies in life sciences, e.g., ontologies
available in the OBO Foundry collection or in BioPortal [134], KG construction
usually involves the reuse, alignment, and enrichment of state-of-the-art
ontologies. The existing ontologies in life sciences need to be updated given
the new discoveries in the field. This is broadly a key issue in the
management, maintenance, and evolution of ontologies. We select a few
promising use cases below to highlight some recent developments that support
the KG construction in the life sciences.

Repositories of Ontologies and Mappings. Ontologies and their mappings play a
central role in semantically enabled products and services consumed by life
science companies, academic institutions and universities, as highlighted by
the Pistoia Alliance ontology mapping project
[59].202020https://www.pistoiaalliance.org/projects/current-
projects/ontologies-mapping/ Ontology mappings are essential in knowledge
graph construction tasks to bridge the knowledge provided by different
ontologies and expand their coverage. Ontology mappings can also play a key
role when identifying the right ontologies to be reused as they will enable
the retrieval of the relevant (overlapping) ontologies for the domain of
interest. For this reason, a number of notable efforts in life sciences have
created large repositories of ontologies and mappings to serve the research
within the community. Prominent examples include the UMLS Metathesaurus [10],
BioPortal [134, 154], MONDO [174], and the EBI services: OLS [176], OxO [85]
and the RDF platform [86]. The UMLS Metathesaurus is a comprehensive effort
for integrating biomedical ontologies through mappings. In its 2023AA version,
it integrates more than two hundred vocabularies, with more than 3 million
unique concepts and more than 15 million concept names. BioPortal is a
repository containing more than 1,000 biomedical ontologies and more than 79
million lexically computed mappings among them (as of July 13, 2023). The
Mondo Disease Ontology (MONDO) is a manually curated effort to harmonize and
integrate disease conceptualizations and definitions across state-of-the-art
ontologies (e.g., HPO [98], DO [157], ICD, SNOMED CT, etc.). The services
provided by the European Bioinformatics Institute (EBI) also deserve a special
mention. The Ontology Lookup Service (OLS) has become a reference to explore
the latest versions of more than two hundred ontologies via its graphical
interface or programmatically via its API. OxO is a repository of ontology
mappings and cross-references extracted from the OLS and UMLS. OxO allows
users to visually traverse the graph of mappings to identify additional
potential mappings beyond direct ones (i.e., multi-hop mappings). Finally, the
EBI RDF platform provides a unified KG with all the RDF resources at the EBI.
Complementary to the efforts from the life sciences, the Semantic Web has also
contributed to the systematic evaluation of mappings in public repositories
(e.g., [82, 45]) and mappings produced by automated ontology mapping systems
(e.g., the Ontology Alignment Evaluation Initiative (OAEI) [140]).
Automatically generated mappings of high quality have the potential to be
integrated within the aforementioned repositories and hence, the OAEI has
always had a special focus on life science test cases with evaluation tracks
like Anatomy [40], LargeBio [84], Phenotype [60] and the newly created track
BioML [65]. The Simple Standard for Sharing Ontological Mappings (SSSOM) [116]
represents a joint effort between the life sciences and Semantic Web
communities to facilitate the exchange of mappings across different parties
and repositories, while keeping the provenance and other relevant
characteristics of the mappings.

Ontology Extension. Ontology extension in life sciences aims to connect new
concepts and their relations to an ontology from updated sources, e.g.,
scientific papers in PubMed and chemical information in
PubChem212121https://pubchem.ncbi.nlm.nih.gov/. Manual ontology extension,
while essential for the development of gold standard resources, is not
scalable to the full scope of large domains due to its high cost and low
efficiency, and sometimes is even unfeasible as human beings may not be able
to review the quantities of new information at the rate they become available.
Thus machine-learning-based, automated methods are needed. One recent example
is the use of deep learning, specifically a Transformer-based model, to
categorize new chemical entities within the ChEBI
ontology222222https://www.ebi.ac.uk/chebi/ [54]. In addition, recent studies
have explored enriching SNOMED CT by mining new concepts from texts [36] and
placing them into the ontology [111, 35]. A new concept can be identified by
NIL entity linking, i.e., exploring unlinkable mentions, usually through
setting a “linkable” score threshold or through classification [36].
Resolution and disambiguation of NIL mentions with clustering can help to
represent NIL entities [67, 93]. For concept placement, similar to the
aforementioned CHEBI ontology extension [54], machine learning, especially in
the form of Transformer-based deep learning, has been applied to predict
subsumption relations between a new concept and the existing concepts. Complex
concepts in OWL ontologies that contain logical operators (e.g., existential
quantifier and conjunction in SNOMED CT) can be supported in subsumption
prediction [24] and new concept placement [35]. Another group of studies use
post-coordination or formalising a new term with existing concepts and
attributes [17, 94], which is similar to composing subsumption axioms with
complex concepts. The methods include using lexical features [94], word
embeddings and KG embeddings [17]. Pre-trained and Large Language Models,
through fine-tuning, zero-shot and few-shot prompting have the potential to
support the mining [36] and placement of new concepts (e.g., by subsumption
prediction [24, 66]).

Instance Matching: Automated Clinical Coding. A main source for patients’ KG
construction is Electrical Health Records (EHR). Using medical ontologies as
backbones, it is possible to add a layer of data by instance matching (or
patient matching) through Clinical Coding. Clinical coding is the task of
transforming medical information in EHR into structured codes described in
medical ontologies [37], e.g., ICD and SNOMED CT. Recent approaches mainly
formulate the problem as a multi-label classification problem. Various neural
network architectures have been proposed and knowledge plays a key role to
enhance the neural architectures [37, 80]. Pre-trained language models, e.g.,
BERT [33], have been applied to clinical coding and gradually achieved better
results with adapted modelling methods and more advanced language models,
e.g., PLM-ICD [71] with RoBERTa-PM [106], according to studies [37, 43, 79].
Other studies formulate the task as a Named Entity Recognition and Linking
(NER+L) problem, by extraction of concepts and linking them with the
ontologies [37]. Overall, the recent progress in clinical coding, along with
the advent of Large Language Models (LLMs) suggests a trend in this area for
patients’ KG construction from EHR. However, there is still room for
improvement in knowledge integration to better address explainability (see
Section 5 for more details) and in zero-shot learning problems, i.e., for
classifying into rare codes or concepts [37, 43, 80]. There are also further
recent examples of instance matching with EHR data, including the works [16,
168].

###  3.4 What are the challenges?

KG construction and management often play a fundamental role in supporting
life sciences with computation. There are still quite a few technical
challenges, and many of the current tools and algorithms can be improved by
modern machine learning and AI techniques. Here we present some critical and
fundamental technical challenges.

  * •

How to construct a customized KG? For a specific application, we often need to
extract relevant data and knowledge from multiple sources, and at the same
time integrate extracted knowledge from different sources. Considering a case
study of personal health assistance, a customized KG with knowledge of at
least exercise (sports), food, disease and medicine are required, while fine-
grained knowledge of these aspects will lie in different domain KGs. The key
challenge for integrating different ontology modules lies in estimating the
semantic similarity and discovering the equivalence of two knowledge elements
with their contexts considered, as well as the subsequent refinement like KG
completion and knowledge representation canonicalization. Adequate tool
support to minimize manual curation but enabling the user involvement when
required is also paramount (e.g., [107]).

  * •

How to ensure adequate performance using machine learning based approaches for
automated KG construction? At the TBox level, the state-of-the-art alignment
between classes (especially for subsumption relations) seems to not yet be
achieving good enough performance, as reflected in recent biomedical ontology
alignment benchmarking [65]. At the ABox level, predicting missing facts for
practical KG construction expects high precision (e.g., beyond 90% or 95%) but
only a few relations can be populated with a precision above 80% using prompt
learning with BERT as evaluated in [175]. This is also the case to associate
patients’ EHR (as a part of ABox) with clinical codes or concepts in medical
ontologies, where a micro $F_{1}$ score is below 60% [37]. Learning
subsymbolic representations (see defined in Appendix A) of KG and data sources
may help address the challenge. Transformer-based language models have
achieved great performance in recent years. Among them, pre-trained language
models such as BERT have been applied for KG construction with a promising
performance achieved (see e.g., the package DeepOnto [64]), while the more
recent and more powerful generative language models like GPT series [14] have
not been well applied at the time of writing, especially in the life science
domain.

  * •

How to ensure reliable semi-automated deep learning-based KG construction with
human interaction? Many tasks in the KG life cycle unavoidably rely on human
experts to achieve consensus on reliable knowledge; on the other hand, as the
automated KG construction process is growing opaque with deep learning
methods, it is important to ensure trustworthiness and reliability [193].
Apart from enhancing performance metrics with novel methods, results with
certain explainability are needed, for example, highlighting key parts in the
data input when they are used as sources for KG construction. We discuss other
aspects of explainability with KG, on life science knowledge discovery and
healthcare decision making, in Section 5. Human-in-the-loop learning design
for explainable KG construction may ensure the use of experts’ knowledge for
the task across the KG life cycle, which still remains a challenge for future
research [193].

##  4 Life Science Knowledge Discovery

Research into AI technologies – including machine learning and KG-based
reasoning – to accelerate the pace of scientific discovery is an emerging and
rapidly developing field. The challenge lies in assisting scientists to
uncover new knowledge and solutions, such as discovering novel therapeutic
opportunities, identifying candidate molecular drugs to treat complex diseases
or alternatively new uses for existing drugs, and supporting more personalized
predictions.

Knowledge Graphs are powerful tools for representing complex biomedical
knowledge, including molecular interactions, signalling pathways, disease co-
morbidities, and more. Overviews of graph representation learning in
biomedicine for healthcare applications and polypharmacy tasks are presented
in [109] and [53] respectively. In graph representation learning, the graph’s
topology is leveraged to create compact vector embeddings. Through nonlinear
transformations, high-dimensional information about a node’s graph
neighborhood is distilled into low-dimensional vectors, where similar nodes
are embedded close together in the vectorial space. Embeddings have been shown
to be valuable for handling numerous relations in a KG while efficiently
exploiting relation sparsity using vector computations. These optimized
representations are subsequently used to train downstream models for various
tasks, such as predicting property values of specific nodes (e.g., protein
function), predicting links between nodes (e.g., binding affinity between
molecules and protein targets), or performing classification tasks (e.g.,
predicting the toxicity profile of a candidate drug, or risk of readmission
for a patient).

It is worth mentioning that among the existing works for life science
knowledge discovery, different kinds of KGs have been exploited. The schema-
less KG can be used to model different kinds of interaction between instances
such as proteins and drugs; the taxonomy alike simple ontology is often used
to represent concepts and their hierarchy such as protein functions defined in
the gene ontology, chemical compounds, species, and diseases; expressive OWL
ontologies and schema-based KGs can be used to model complex logical
relationships between concepts, besides simple interaction between instances.
Such diverse knowledge representation capabilities make KGs more flexible in
modeling the input data and prediction targets of different knowledge
discovery tasks, than graphs and tabular data that are widely used in previous
pure machine learning-based methods.

In the following, we present some typical use cases, where machine learning
techniques (including graph representation learning and language models) are
applied over KGs built from diverse sources and domain ontologies, to
facilitate life science discovery.

###  4.1 What has been done: use cases and their recent developments

Therapeutics and Drug Discovery: Learning a representation using multi-modal
and heterogeneous knowledge. Drug discovery entails exploring an extremely
large space of potential drug candidates. AI can help to accelerate this
process by narrowing down the most promising candidates before expensive
experimentation. The key to leveraging predictive and generative models for
candidate solution generation lies in learning an effective multi-modal
representation of protein targets, molecules and diseases among others. Recent
research has focused on applying language models over large databases of
proteins or molecules for self-supervised representation learning, such as ESM
[150] and ProteinBERT [11] for protein sequences, or Molformer for the
molecule simplified molecular-input line-entry system (SMILES) [153]. These
models have exhibited remarkable success in tasks such as predicting protein
interactions, binding affinity between drugs and targets, and protein
functions and structures. However, these existing pre-trained sequence-based
models often neglect to incorporate background knowledge from diverse sources,
for example, biological structural knowledge.

Nonetheless, recent research indicates that incorporating existing expressive
factual knowledge can improve results in downstream machine learning tasks. To
enhance Protein Language Models (PLM), approaches such as OntoProtein [194]
and KeAP [196] use a KG of protein sequences augmented with textual
annotations from the Gene Ontology (GO). OntoProtein was the first to inject
gene ontology descriptions into a PLM for sequences to predict protein
interactions, function and contact prediction. OntoProtein proposes to
reconstruct masked amino acids while minimizing the embedding distance between
the contextual representation of proteins and associated knowledge terms.
Similarly, ProtST [188] uses a dataset of protein sequences augmented with
textual property descriptions from biomedical texts and jointly trains a PLM
with a biomedical language model.

Knowledge Graphs are suitable data models for expressing heterogeneous
knowledge and facilitating end-to-end learning [183]. An entity in a KG can
have multiple attributes with different modalities - where each modality
provides extra information about the entity - as well as relations to and from
entities in other sources. Graph Neural Networks (GNN) have been used to
capture inter-dependencies and diverse types of interactions between
heterogeneous entity types and multimodal attributes in KGs [102]. They
achieve this by iteratively aggregating information from neighbouring nodes
(through a process called message passing) and employing scoring functions to
optimize the learned embeddings for downstream tasks. Otter-Knowledge [102]
incorporates a heterogeneous KG (schema-based, containing concepts and their
attributes) from diverse sources and modalities, i.e., each node has a
particular mode that qualifies its type (text, image, protein sequence,
molecule, etc.) and initial embeddings for each node are computed based on
their modality. A GNN is then used to enrich protein and molecule
representations and train a model to produce final node embeddings. The model
is able to produce representations for entities that were not seen during
training and achieve state-of-the-art results in the Therapeutic Data Commons
(TDC) benchmarks [74] for drug-target binding affinity prediction. TxGNN [73]
uses a GNN pre-trained on a large heterogeneous, multi-relational KG of
diseases and therapeutic candidates constructed from various knowledge bases.
TxGNN obtains a signature vector for each disease based on its neighboring
proteins, exposure and other biomedical entities to compute a disease
similarity and predict drug indication/contraindication for poorly
characterized diseases.

Protein Function Prediction with the Gene Ontology. Conducting physical
experiments for identifying protein functions is time and resource consuming.
With the development of machine learning, protein function prediction (which
is the task of predicting a given protein with multiple and potentially
hierarchical classes – functions – defined in GO) has been widely investigated
in recent years [195, 173]. A large part of these works such as GOLabler [191]
focus on exploring feature extraction, feature ensemble, and automatic feature
learning of the proteins. For example, GOLabler [191] utilizes five kinds of
different protein sequence information while DeepGraphGO [190] builds a
network of proteins and learns protein features via a Graph Neural Network.
Recent methods attempt to further exploit inter-function (class) relationships
that are defined in GO for better performance. For example, DeepGOZero [101]
and HMI [187] use formal semantics including the class hierarchy, class
disjointness axioms and complex class restrictions in OWL as additional
constraints for training the multi-label classifier for protein function
prediction. Protein function prediction is a representative multi-label
classification problem where complex relationships of the labels are defined
in a KG and can be used for performance augmentation. It is quite common in
machine learning applications in the life sciences, such as the above
mentioned automated clinical coding where the codes’ semantics are modeled by
the ICD ontology, and ecotoxicological effect prediction where the multiple
affected species of a chemical to predict form a taxonomy.

Predictions for Healthcare using Ontologies with Clinical Data. Digital
Healthcare involves predictions using clinical data and ontologies, including
diagnosis (e.g., rare diseases) and procedure predictions (e.g., ICU
readmissions). A related concept is personalized medicine, which is achieved
through the matching and fusion of knowledge from diverse sources, and plays a
significant role in the prediction tasks. This often involves matching
multiple ontologies [158], integrating curated databases (e.g.,
pharmacogenomics, molecules and proteins knowledge bases), mining knowledge
from scientific literature [186] and person-centered clinical knowledge
extracted from EHR or claim data, with distinguishing risk factors or cohorts’
demographics (e.g., age and gender), which could enhance predictions related
to adverse effects [125] or rare diseases for which there are not enough
labeled datasets [2]. For example, SHEPHERD [2] incorporates a multi-
relational KG (extracted from PrimeKG [20]) of diseases, phenotypes and genes,
and leverages patient simulated data to discover novel connections between
patients’ clinical, phenotype and gene information to accelerate the diagnoses
of rare diseases. Knowledge-guided learning is achieved by training a GNN to
represent each patient’s subgraphs of phenotypes in relation to other gene,
phenotype, and disease associations within the KG, such that embeddings are
informed by all of the existing biomedical knowledge captured in the network
topology.

The approach in [16] constructs a KG (using expressive OWL ontologies) to
predict ICU (intensive care units) readmission risk by enriching EHR data with
semantic annotations from various biomedical ontologies in BioPortal. These
predictions are based on KG embedding, such as RDF2vec, OPA2vec, and TransE,
and classical machine learning methods, such as Logistic Regression, Random
Forest, Naive Bayes and Support Vector Machines. Drawing from the Health &
Social Person-centric Ontology (HSPO) [167], which focuses on multiple
clinical, social and demographic facets for a patient or cohort, the approach
presented in [168] builds a person-centric KG (expressive OWL ontology with
TBox and ABox) from structured and unstructured data in EHR). Subsequently, a
representation learning approach using GNNs is used to predict readmissions to
the ICU.

###  4.2 What are the challenges?

We present four of the open challenges to unlock the full potential of methods
to advance knowledge discovery for the life sciences using KGs, based on the
use cases above.

  * •

How to incorporate the semantics from a KG in machine learning? Many life
science knowledge discovery tasks are modeled as a machine learning
classification problem, whose input and output labels have additional valuable
information in one or multiple external KGs. The challenge lies in extracting
this information, optionally encoding it into vector representations, and
injecting that knowledge into machine learning and pre-trained language
models. Doing this effectively remains an important open challenge especially
for protein-related pre-trained language models [194, 188, 196]. Besides
improving the accuracy in knowledge discovery, injecting semantics from KGs
can also contribute to making the model more explainable (see Section 5), but
to this end, much research is still required.

  * •

How to deal with the long-tail phenomenon in machine learning with KGs? In
machine learning classification for real-world life science knowledge
discovery, the candidate labels often exhibit a long-tailed distribution,
i.e., a small ratio of them are common with a large number of training samples
available, while most of them are infrequent or even have never appeared
before. For example, imbalance in training data may occur for rare diseases or
adverse drug effects that affect only a small portion of the population [2,
73, 38]. KGs sometimes have encoded the relationships of the labels, and could
be used to help train the model for predicting those long-tailed labels or
enable the inference of such labels.

  * •

How to create an efficient multi-modal representation of knowledge to enable
discovery? Most current state-of-the-art methods build learned graph
representations based on isolated modalities. Multimodal KGs can explicitly
capture labelled nodes and edges, each with well-defined meanings, across
heterogeneous node types, relations and modalities (such as text, images,
protein sequences, molecules fingerprints, diseases and more) [20, 102].
Incorporating KGs with multiple modalities for representation learning
requires computationally scalable methods to compute the initial embeddings
for each modality, as a preliminary step to learn computable representations
of large knowledge. Furthermore, robust learning techniques are needed for
generalizing the learned representations to nodes with unseen or missing
modalities, thereby enabling the discovery of new knowledge. An example would
be inferring properties of proteins for which only the sequence is known.

  * •

How to efficiently utilize and fuse heterogeneous datasets, such as human-
curated domain knowledge bases, scientific literature and person-centered
health records, for knowledge discovery? State of the art shows that
representations can be enhanced by incorporating richer information available
across different sources [73, 102, 158]. Bringing in more data during training
is needed to learn representations that can be applied to a broader range of
downstream prediction tasks. However, learning from large and diverse KGs
requires addressing challenges such as alignment, noise handling, balancing
rich expressive knowledge with scalability and dealing with knowledge
inconsistency. Moreover, more robust learning methods are needed for
generalizing the learned representation to multiple downstream tasks (e.g.,
knowledge-aware transfer, zero-shot and few-shot learning [23]). An important
aspect in this regard is addressing the disparity between all of the knowledge
accessible during pre-training and the knowledge accessible or relevant for
the downstream fine-tuning [73, 102].

##  5 Knowledge Graphs for Explainable AI

Machine Learning (ML) and Artificial Intelligence (AI) methods are widely
employed to tackle complex problems in many domains, including life sciences
such as chemistry or biomedicine. Yet many of those methods operate as a
“black-box”, not enabling domain experts to understand the reasoning behind
their predictions [92]. This is a major concern, especially for applications
in areas with a potential impact on human lives, or areas with legally
enforced accountability or transparency [145]. Moreover, understanding the
workings of AI methods is also crucial in the context of scientific
applications, such as those described in Section 4, where explaining the
prediction process can help elucidate natural phenomena [41].

One way to address this issue is to employ the methods of eXplainable
Artificial Intelligence (XAI). Although this is a topic long explored in the
AI research community, there is still no widely-accepted definition of
explainability, with many terms being used interchangeably, such as
interpretability, comprehensibility, understandability and transparency [8].
Barredo et al. define explainability as the ability of a model to make its
functioning clearer to an audience [8]. A slightly different definition is
given in the previous survey [56]: “an interface between humans and a decision
maker that is at the same time both an accurate proxy of the decision maker
and comprehensible to humans”. Both definitions focus on the audience, for
whom is the model explainable, but the second suggests an explanation is
another artefact produced by a model or alongside the model.

There are two distinguishable audiences in the context of the life sciences:
scientists (researchers) and healthcare practitioners [169]. For the first
group, the explanation is used as a guide to understanding within life
sciences research for scientific discovery. As a result, the explanation may
exist in a well-bounded context of a hypothesis or research project. On the
other hand, practitioners are involved directly in decisions with impact on
healthcare. They need to consider the output of the model in an open context,
and sometimes also to explain the output to a patient who is not a domain
expert.

A number of approaches for XAI emerge from the literature and broadly contain
two parts: (1) transparent box design, which includes algorithms such as
decision trees, where models can be directly interpreted by users and
therefore an explanation of an output results in simply following the decision
paths that relate input to output; (2) post hoc interpretability, which
provides an explanation to a black-box model using additional methods such as
probing, perturbing, or by constructing surrogate models for general ML or AI
methods [92, 169].

Utilization of KGs can greatly enhance XAI qualities as KGs are ideal for
improving the model’s interpretability, explainability, and understandability.
Some methods are directly built around KGs and thus take full advantage of
them. Examples of those methods may include methods that are using paths
[163], predicting links, or performing reasoning [34]. Other methods can be
enhanced using the KG (e.g., [128]). Yet the enhancement effect greatly
depends on the place where KGs are employed and iteratively applied: pre-model
(e.g., KG construction, potentially multi-modal), in-model (e.g., integrating
KG with machine learning models), and post-model (e.g., reviewing and updating
KG by domain experts to be applied in the next iteration to enhance machine
learning models and their explanability) [145]. For example in in-model use, a
model can be pre-trained using a KG, and an example of a pre-trained language
model is SapBERT [110], which utilises synonyms in the UMLS Metathesaurus to
further pre-train a BERT language model. This can not only be beneficial for
performance [194], but can also potentially enhance post-model explanation
since the trained features are aligned with the KG [145].

###  5.1 What has been done: use cases and recent developments

Explainable AI for Healthcare Practice. The utilization of AI in healthcare
practice raises the concern of leaving life-critical decisions to black-box
models [145, 169]. For example, in the field of precision medicine which aims
at tailoring drug treatments and dosages to each patient, clinicians require
more information from a model than a simple binary decision [8]. The
interpretability and explainability of AI models is thus an essential
characteristic to make outputs understandable and transparent. This would
enforce both clinicians’ and patients’ trust in models by complementing (and
not substituting) clinicians’ explanations [21, 145, 169].

To illustrate, this direction has been envisioned for several healthcare
scenarios. Explainable AI models could support the experts in finding clinical
trials that are appropriate based on patient history [169]. Counterintuitive
or unreliable predictions that could have serious consequences could be
explained, and thus prevented [169, 15, 91]. Some also envision such models to
be used to explain and debunk healthcare-related misinformation [145]. As
aforementioned, it is noteworthy that different kinds of explanations should
be employed depending on the target audience, e.g., scientific explanations
for evidence or trace-based explanations for treatment [21].

Explainable AI for Knowledge Discovery. As introduced in Section 4, KGs can
support knowledge discovery in life science, including the explainability of
the process and the discovered units. In this view, Ritoski and Paulheim [149]
explain that ontologies, linked data, and KGs are used in the interpretation
step of a data mining process, e.g., for interpreting sequential patterns in
patient data [78], or to describe subgroups in a semantic subgroup discovery
process [171]. KGs can also serve both as the basis for knowledge discovery
processes and the interpretation process. For example, Linked Open Data
connecting drugs and adverse reactions can be analyzed with Hidden Conditional
Random Fields to predict adverse drug reactions, where the paths from selected
drugs to outcomes visually explain the prediction [88]. Similarly, Bresso et
al. [13] leverage features extracted from KGs (interpretable features such as
paths, neighbors, path patterns) and white box models (e.g., decision trees)
to reproduce expert classifications of drugs causing or not specific adverse
drug reactions. The rules extracted from the decision trees contain features
that provide explanations for the molecular mechanisms behind these adverse
reactions according to experts. Sousa et al. [161] employ KGs to explain both
protein-protein interaction predictions and gene-disease association
predictions based on shared semantic aspects.

Explainable AI for KG Construction The final use case considers the situation
that XAI is applied to KGs themselves. We discussed the challenge to support
human intervention in KG construction in Section 3.4. Recent KG construction
gradually relies on data-driven, deep learning based methods to automatically
induce knowledge from data. The deep learning models are opaque, and thus the
process requires explainability. The resulting KG may not be accountable to be
used for downstream applications. Trustworthy KG engineering is proposed in
[193] to highlight the importance of embedding explainable AI and human
intervention in the KG life cycle. XAI methods have been applied in many NLP
related tasks (entity and relation extraction, entity resolution, link
prediction, etc.) in KG construction from texts. The XAI methods rely either
on feature-based explanations or knowledge-based explanations. While feature-
based explanations try to infer explanations from the data or the models’
interpretation of the data, knowledge-based explanations aim to interpret the
process with rules, reasoning paths, and structured contextual information.
Rules and paths have mainly been used for explanation, especially for link
prediction, a task comprehensively surveyed in [193].

###  5.2 What are the challenges?

  * •

How to integrate KGs for better XAI, especially with recent deep learning and
language model based methods? KG may provide better data provenance for the
model output. This can ensure explainability for communicating the model to
domain experts in data science applications [8]. In terms of recent generative
LLMs, life science KGs, with careful curation based on scientific
publications, may help to provide provenance data to the answers generated by
LLMs. Studies need to understand to what extent, and how, LLMs can be applied
to induce knowledge (e.g., by probing LLMs with biomedical ontologies [66]),
which then may provide a foundation to create better approaches to integrate
KGs with LLMs. Another area is neuro-symbolic methods which may provide models
that are inherently more interpretable (see further discussions in Section
6.1). Also, regarding language models (especially LLMs), they are capable of
generating fluent texts, which can potentially serve as textual explanation
generators from symbolic knowledge for XAI. Meanwhile, a key issue is the
hallucination of LLMs, and KGs may support better prompting, fine-tuning and
interpretable inference of LLMs for higher decisiveness and trustfulness
[136].

  * •

How to evaluate XAI methods that involve KG? How to measure the quality of
explanations, to ensure they are corresponding to users? The majority (around
70%) of XAI studies for KG construction do not evaluate the quality of the
explanations or only informally visualize or comment on a limited number of
cases to show the intuitive outcome [193]. Also, an XAI method needs to
consider the target audience, as the explainability is to be finally received
by a group of humans [8]. For instance, only a small number of current
approaches to XAI for KG construction involve a user study, human evaluation
or task-specific metrics [193]. Evaluating the quality of explanations
requires some expert evaluation performed as ex-post evaluation, and well-
defined metrics are needed for this task. An example is in [58] to use a
combination of users’ scores for each predicted explanation in a KG link
prediction task, where there are multiple possible explanations. More expert
validated and automated evaluation methods and associated metrics are required
for KG-related XAI.

##  6 Discussion and Conclusion

In this work, we have summarized the recent developments of KG research in
life science on three important topics – KG Construction and Management, Life
Science Knowledge Discovery, and KG for XAI. While each topic has its specific
challenges, there are some common challenges and trends for the life science
KG research in general.

###  6.1 Overall challenges and trends

Meanwhile, more scalable and efficient knowledge retrieval, query and
reasoning systems, including life science KGs and mapping repositories, are
still worthy of investigation and development.

Evolution and Quality Assurance of KGs. KGs need to be updated as new data and
knowledge are emerging, and the schema and facts can easily become outdated or
less useful for existing applications in life sciences. In terms of KG
construction, we discussed ontology extension as a use case to address the
evolution issue or emergence of new concepts and relations, and also instance
matching to extend new instances for the KG. Updating KGs is also a
prerequisite for life science knowledge discovery and knowledge discovery
methods should be able to support the evolution of KGs with e.g., the
capabilities of continuous learning and zero-shot learning. Quality assurance
is another issue for KGs, including the tasks of knowledge error detection and
correction, knowledge completion, knowledge canonicalization, etc. On the one
hand, more effective KG quality assurance methods and systems should be
developed, including schema and constraint languages for quality verification
and learning-based models for prediction (e.g., [25] combines both for fact
correction); on the other hand, knowledge discovery methods should be robust
to noisy KGs by investigating e.g., robust KG embeddings and multi-modal
representation learning.

Heterogeneity in KGs: Multi-domain and Multi-modality. KGs contain
heterogeneous information, which brings challenges to their construction,
representation, and reasoning. Different schema and data in KGs can have
different focuses in their scopes and domains. Integrating data of different
domains for building multi-domain KGs is difficult with challenges in e.g.,
ontology and data matching. Besides, recent studies have explored integrating
different modalities to construct Multi-modal KGs [27, 124, 178], for instance
text [135], images [181], etc. One challenge to address is how to learn
effective machine learning models over multi-modal KGs fused from different
sources (patients’ records, curated knowledge bases, and scientific
literature) to support scientific discovery as well as KG construction and
management. Another challenge is developing accurate and efficient knowledge
representation approaches for texts and images in multi-modal KG construction.
For example, careful consideration should be given to when to simply use an
annotation property to associate an image with an entity, and when to use a
property with specific semantics to connect an image and an entity.

Human Interaction and Explainability with KGs. In KG construction, human
experts are required for many sub-tasks of KG construction and provide
oversight [193]. In life science knowledge discovery, human experts are
necessary to finally validate the predicted new knowledge. The whole process
of interacting with KG in life sciences requires explainability, especially
when sub-symbolic models (e.g., pre-trained language models) are used. How to
generate clear explanations for human interaction and how to evaluate the
quality of explanations remains a challenge, as well as how to achieve
consensus regarding scientific understanding with automatically discovered
knowledge when organizing knowledge in life science [131]. The recent growth
of Neuro-Symbolic methods suggests their support for explainability [90, 91,
152]. A recent survey [91] summarizes XAI in bioinformatics with a chapter on
knowledge-based explanations, whereas Karim [90, Chapter 8] provides a neuro-
symbolic framework for KG construction and utilisation for medical experts’
decision making in the cancer domain. The approach presented in [152] is
another recent example of neuro-symbolic integration for image classification
with KG-based XAI in the cultural heritage domain.

Personalized and Customized KGs. A key challenge for KG construction is
customisation, as we discussed in Section 3, to construct application-oriented
KGs, where relevant sub-KGs have to be extracted for large-scale KGs (a.k.a.
modualization) and integrated with other knowledge and data from different
sources. Besides, many life science KGs are about individuals, e.g., patients
in healthcare applications, where Personal Health KG enables the integration
of instance-level (or patient-level) information and their computation is
required [124]. An example is the Personal Health KG in [22] that supports the
dietary recommendation for users, where the construction and population of the
KG requires reusing and integrating existing ontologies, dietary guidelines,
and time-series patient data. The other examples of KGs integrating patients’
EHR data [168, 16] are presented in Section 4.1. In personal KG construction,
personal data should be protected. KG scalability should also be considered in
order to be used on small devices such as cellphones. This is still a big
challenge that has been rarely considered in using KGs in the life sciences.

Distributed KGs. The value of healthcare data for improving clinical knowledge
and standard of care and the potential of semantic technologies to further
enhance it are well recognized. However, a responsible use of healthcare data
at the global level (beyond each healthcare provider and even each country)
must take into account both legal and ethical issues in data sharing, privacy
and security. Distributed knowledge graphs can mitigate these issues, by
allowing for access control and privacy protection. Furthermore, distributed
knowledge graphs can also address the challenges of scientific data ownership
and stewardship by enabling the decentralized publishing of high quality data.
Several approaches for federated querying and embedding of knowledge graphs
have been proposed in recent years [26, 138, 159], however a wide adoption of
semantic technologies in healthcare is still lacking, with a proliferation of
terminological standards and a disconnection between data and meaning.

Representation Learning with KGs: Symbolic and Sub-symbolic Integration.
Across the topics and use cases, we see the importance of transforming
symbolic knowledge into sub-symbolic representations or combining both
representations. The combination of both the neural and the traditional
symbolic representation methods leads to a trend in neural-symbolic approaches
in the field [12]. Recently, Pre-trained and Large Language Models provide new
methods to transfer self-supervised learning from a vast amount of corpora to
support KG construction, e.g., OntoGPT [18] and OntoLAMA [66]. LLMs are
especially good at representing texts of life science publications in sub-
symbolic spaces for semantic understanding. KGs may also provide a layer of
explainability by validating the output of LLMs. A recent survey [136]
proposes a roadmap for integrating LLMs and KGs. OntoProtein [194] is a recent
example of how to integrate KGs into the process of pre-training LLMs in the
bioinformatic domain, thus achieving improved results on protein-related
knowledge discovery tasks. Also, geometry-informed representations of more
formal KGs, especially in hyperbolic spaces or using complex geometric
structures, e.g., [19, 101], can usually represent the structure of the KG
with low dimensional vectors. Graph Neural Networks may also support the
encoding of KG structures in a more explainable way with logical rules [31].

###  6.2 Conclusion

Knowledge Graphs have become a popular and effective method to represent
heterogeneous concepts, relations, and data in life sciences. They require
scalable solutions to represent and reason with heterogeneous data and require
constant updates. Throughout this work, we covered the main topics and their
corresponding use cases of KGs in multiple life science domains such as
protein analysis, drug discovery, ecotoxicology, and healthcare, and
summarized the corresponding challenges. As new methods in knowledge
representation appear, for instance the recent trends of human-in-the-loop,
sub-symbolic knowledge representations, pre-trained and large language models,
and neuro-symbolic integration, we envisage deeper applications of KGs to life
science processes, that support the construction of more applicable KGs and
the discovery of more reliable scientific knowledge, with explainability and
human interaction better supported. KGs in combination with other modern
machine learning and natural language processing techniques will become a
foundation for AI for the life sciences.

##### Appendix A: Terms in Knowledge Graphs and Life Sciences

Below we provide a list of key terms used in this paper, as well as their
definitions and explanations. Note we mainly use the original sentences in the
sources that are referenced as the definitions.

Description Logics: a family of knowledge representation languages that can be
used to represent knowledge of an application domain. DLs differ from their
predecessors, such as semantic networks and frames, in that they are equipped
with logic-based semantics, the same semantics as that of classical first-
order logic. Most ontologies are implemented in OWL, whose semantics are given
by the Description Logic $\mathcal{SROIQ}$. [6]

TBox and ABox: the two components of domain knowledge in Description Logics,
i.e., a terminological part called the TBox and an assertional part called the
ABox, with the combination of a TBox and an ABox being called a knowledge base
(KB). The TBox represents knowledge about the structure of the domain (similar
to a database schema), while the ABox represents knowledge about a concrete
situation (similar to a database instance). [6]

Semantic Networks: a graph structure for representing knowledge in patterns of
interconnected nodes and arcs [162]. We use the term to denote a graph of
concepts and relations without formal semantics.

Gene Ontology: The Gene Ontology (GO) knowledgebase provides a comprehensive,
structured, computer-accessible representation of gene function, for genes
from any cellular organism or virus [5, 29].

SNOMED-CT: Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is
a structured clinical vocabulary. It has a general and comprehensive coverage
of clinical terms to support electronic healthcare systems and clinical
applications. [39, 28]

UMLS (UMLS Metahesaurus and UMLS Semantic Networks): Unified Medical Language
System (UMLS) is a repository of biomedical vocabularies developed by the US
National Library of Medicine. The UMLS is composed of three “knowledge
sources”, a Metathesaurus, a semantic network, and a lexicon. The UMLS
Metathesaurus is a comprehensive effort for integrating biomedical ontologies
through mappings. The UMLS Semantic Networks define the types or categories,
or Semantic Types, of all Metathesaurus concepts and their relationships, or
Semantic Relations. [10, 28]

ChEBI: Chemical Entities of Biological Interest (ChEBI) is a database and
ontology containing information about chemical entities of biological
interest. [63]

Symbolic vs. subsymbolic representations: Rooted in cognitive science,
symbolic systems of human cognition are related to the representation and
manipulation of symbols; sub-symbolic or connectionist systems are most
generally associated with the metaphor of a neuron, e.g., perceptrons as an
early system [96]. In terms of AI, symbolic systems contain logic-based and
knowledge representations, while subsymbolic systems typically contain neural
networks and deep learning based methods [42]. Neural language models and pre-
trained language models [87] are also classified under subsymbolic systems.

Pre-trained and Large Language Models: Neural language modelling is the task
of using neural network approaches to predict words from prior their contexts
in a sequence. Pre-training is the process of learning some sort of
representation (usually neural embedding based) of meaning for words or
sentences by processing very large amounts of text (or other data in a
sequence form, e.g., proteins and KG facts). This results in pre-trained
language models. The dominating architecture for neural language modeling is
Transformer-based models, including BERT, its domain specific versions, and
later large variants, like the GPT series. The pre-trained language models of
very large sizes are recently coined Large Language Models (LLMs). [87]

Neuro-symbolic representations: refers to the integration of neural networks
and symbolic representations to design AI models that base their prediction on
both data and knowledge. [42]

##### Appendix B: Authors’ Contributions

All authors participated in the planning and discussions of this work. JH and
HD finished the abstract and “Introduction”. VT, JC and EJR contributed to
“Knowledge Graphs in the Life Sciences”. VT contributed to the main part of
“Knowledge Graph Construction and Management”, with contributions of use cases
from JC, HD, PM, EJR, and JH. VL and JC contributed to “Life Science Knowledge
Discovery”. PM, PS, HD, and CP contributed to “Knowledge Graphs for
Explainable AI”. HD, JC, and CP contributed to “Discussion and Conclusion”
based on discussions with other team members. All authors contributed to the
final revision of this paper.

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