# Extracting Domain-specific Concepts from Large-scale Linked Open Data

Satoshi Kume
Faculty of Information and Communication Engineering
Osaka Electro-Communication University
Neyagawa, Osaka, Japan
<EMAIL_ADDRESS>
Kouji Kozaki
Faculty of Information and Communication Engineering
Osaka Electro-Communication University
Neyagawa, Osaka, Japan
<EMAIL_ADDRESS>

###### Abstract

We propose a methodology for extracting concepts for a target domain from
large-scale linked open data (LOD) to support the construction of domain
ontologies providing field-specific knowledge and definitions. The proposed
method defines search entities by linking the LOD vocabulary with technical
terms related to the target domain. The search entities are then used as a
starting point for obtaining upper-level concepts in the LOD, and the
occurrences of common upper-level entities and the chain-of-path relationships
are examined to determine the range of conceptual connections in the target
domain. A technical dictionary index and natural language processing are used
to evaluate whether the extracted concepts cover the domain. As an example of
extracting a class hierarchy from LOD, we used Wikidata to construct a domain
ontology for polymer materials and physical properties. The proposed method
can be applied to general datasets with class hierarchies, and it allows
ontology developers to create an initial model of the domain ontology for
their own purposes.

_Keywords_ Ontology construction $\cdot$ Domain ontology $\cdot$ Linked open
data $\cdot$ Wikidata $\cdot$ Graph analysis

##  1 Introduction

An ontology is an explicit description of concepts (Gruber, 1993), and it is a
core technology for systematising and processing target knowledge. In recent
years, domain ontologies that provide specialised knowledge and definitions
(Niles and Pease, 2001; Jean et al., 2007) have been constructed for various
fields, such as life sciences (Masuya et al., 2021; Kume et al., 2017; Jupp et
al., 2016), biomedicine (Stenzhorn et al., 2007; Luciano et al., 2011),
chemistry (Hastings et al., 2012; Kushida et al., 2019; Fu et al., 2015) and
engineering (Ahmed et al., 2006). Domain-specific ontologies are used to
construct user-interface services for information retrieval and visualisation,
as well as for integrating data from different fields. However, there is still
no standard methodology for constructing a domain ontology (Yun et al., 2011).
Most domain ontologies are constructed manually and curated by experts
(Kobayashi et al., 2018). This conventional approach has several issues:
ambiguous coverage of the target vocabulary, lack of reproducibility and of
the validity of the ontology construction method, and lack of interoperability
with other ontologies. Addressing these issues increases the complexity of
ontology design in the early stages, especially for developers unfamiliar with
semantic relations. Thus, a methodology is desired for developing an initial
design of a domain ontology in an automatic and objective manner.

Linked open data (LOD) (Cyganiak and Jentzsch, 2011; Alahmari et al., 2012) is
structured in accordance with the Resource Description Framework (RDF), which
describes resources on the Web (Berners-Lee et al., 2001). RDF-based LOD
possesses structural connections between concepts based on triples consisting
of a subject, predicate and object (Tomaszuk and Hyland-Wood, 2020). Large-
scale LODs—such as Wikidata (Vrandečić and Krötzsch, 2014; Turki et al., 2019)
and DBpedia (Auer et al., 2007)—have been developed, and various ontologies
are available in RDF format (Ong et al., 2016; Salvadores et al., 2013; Côté
et al., 2006). While some researchers have focused on the reuse of existing
LODs, a general method for building a domain ontology from structural
knowledge of LOD has not yet been proposed.

Our aim was to develop a reproducible method for extracting conceptual
relations for a target domain from LOD and for constructing an initial model
of the ontology while ensuring interoperability with the LOD or other
ontologies. We developed a workflow for extracting graph regions, which we
applied to Wikidata as a case study. We also developed an objective method for
evaluating the obtained vocabulary.

##  2 Data Collection

###  2.1 Requirements for Ontology Construction

The construction of a domain ontology requires designing the class hierarchy
(i.e. is-a hierarchy) and defining each class (i.e. relations other than the
is-a hierarchy) (Boyce and Pahl, 2007). In this study, we considered the
construction of a class hierarchy from LOD that is closely related to the
target domain. This requires addressing two issues: identifying
entities/concepts that are common junction points in LOD graph and determining
the search range (i.e. the number of search steps) from these entities.

We propose an algorithm for analysing superordinate concept graphs (i.e.
conceptual graph structures obtained by searching upward from the entities at
the search starting points) to obtain a subset of the class hierarchy that is
related to the target domain. The vocabulary (i.e. the search entities) for
the target domain is given, and the common upper-level entities are identified
according to the number of occurrences of the lowered search entities. Then, a
graph analysis is performed based on the paths of related links, and the
branch points and search ranges from each branch point are determined.

###  2.2 Wikidata

For verification, we applied the proposed method to Wikidata and selected
polymer material science as the target domain. Wikidata is a collaboratively
edited database provided by the Wikimedia Foundation, and its data are linked
to the Web and are freely available (Vrandečić and Krötzsch, 2014). As of
November 2020, Wikidata has at least 90.68 million entries, and dump data are
available in RDF format. Wikidata includes information on humans, taxons,
administrative divisions, structures/buildings, events, chemical compounds,
movies, astronomical objects and scientific articles. Thus, Wikidata can be
regarded as a collection of knowledge in various domains.

Figure 1 shows the data model for the class hierarchy assumed in Wikidata.
Entities in Wikidata are given unique Q numbers called QIDs. The property
relations of the representative label name (rdfs:label) and alias
(skos:altLabel) are used to link label information. In the class hierarchy,
conceptual relationships between entities are represented by the subClassOf
(wdt:P279) and instanceOf (wdt:P31) properties. In Wikidata’s user-editable
data model, there is no restriction preventing an entity from having or being
referenced by both subClassOf and instanceOf relations. Alternatively,
Wikidata allows the same entity to have both class and instance attributes,
which is sometimes expressed as a loose way on the LOD cloud. For example, the
entity polymer (wd:Q81163) relates with instanceOf to ’group or class of
chemical substances’ (wd:Q17339814), while that same entity is also under
subClassOf relations with mixture (wd:Q169336) and material (wd:Q214609).
Therefore, in the present study, we assumed a data model with both property
relations (Figure 1). For other datasets, the flexibility of the data model is
considered in the program implementation for applying the proposed method to
various data models.

We conducted the experiment using a Wikidata datadump as of September 2021.
There were 100,193,046 triples in the class hierarchy, and the dataset
included 3,188,633 and 97,004,413 triples with the subClassOf (wdt:P279) and
instanceOf (wdt:P31) properties, respectively. There were 3,813,879 triples
with label information in Japanese, and the labels included 2,733,815 and
1,080,064 triples of the representative label name (rdfs:label) and alias
(skos:altLabel), respectively.

Figure 1: Data model for the class hierarchy assumed in Wikidata.

###  2.3 Collection of Vocabulary Related to Polymer Materials

PoLyInfo is a polymer database in Japan that systematically organises various
data related to designing polymer materials, such as polymer names, physical
properties of polymers, chemical structures, measurement conditions and
polymerisation methods (PoLyInfo S. Iimuro, 2007; Ishii et al., 2019). We used
the Japanese description text in PoLyInfo to obtain technical terms related to
polymer materials such as polymer names, physical properties and
polymerisation methods.

##  3 Related works

As preliminary research (Kume and Kozaki, 2020), we extracted class
hierarchies using a Wikidata datadump of October 2020, which contained
94,327,098 triples in the class hierarchy. We tried to search the class
hierarchies via SPARQL queries, but we found some problems. When the lower-
level hierarchical structure contained more than tens of thousands of
entities, searches via SPARQL queries took a considerable amount of time, and
they could not completely extract all lower-level entities because of
communication errors. Thus, in this study, we constructed a new workflow using
text search and trimming algorithms for RDF files and then conducted
experiments and evaluations using this new version of the Wikidata RDF
dataset.

While previous attempts to build domain-specific ontologies and extend the
concept of LOD to Wikipedia resources have been re-ported (Zhang et al., 2017;
Küçük and Arslan, 2014; Khalatbari, 2015; Shibaki et al., 2010; Xavier and De
Lima, 2010), this study is the first to use Wikidata and perform a graph
analysis on the class hierarchy. In an additional related work, Lalithsena et
al (Lalithsena et al., 2017) presented an approach for extracting domain-
specific hierarchical subgraphs from LOD by identifying the domain-specificity
of the categories in the hierarchy. These were determined by combining
different evidence using a probabilistic framework, and indicated a use case
for a recommendation of movie and book domains . Aljamel et al (Aljamel et
al., 2015) collected distant-supervision training data from LOD for machine-
learning techniques to apply information extraction for the financial domain.
Unlike these methods, the proposed method provides a simple way to produce an
initial model for domain-specific ontology without prior learning and
categorization.

##  4 Proposed Method

###  4.1 Overview of the Proposed Workflow

Figure 2 shows the workflow of the proposed method. The workflow was built in
an R language/RStudio environment. The programme code was implemented by using
R packages such as igraph, SPARQL, purrr and plotly. An example of the
workflow execution was published on GitHub as part of the agGraphSearch
package (https://github.com/kumeS/agGraphSearch).

Figure 2: Overview of the proposed workflow.

###  4.2 Gathering of Domain Terms and Entity Linking with LOD

Step 1 consists of collecting vocabulary related to the target domain in order
to create a set of technical terms. The vocabulary can be extracted from
domain-related documents domain or using existing keywords for the domain
(e.g. domain-related web resources, indices of dictionaries and textbooks,
articles, brief descriptions and keyword lists created by experts). In this
study, vocabulary considered to be technical terms—such as katakana vocabulary
and compound words—were picked up extensively in the manual from the Japanese
description text related to the domain, which is provided as a public web
resource. Further, it is possible to extract such terms using natural language
processing, such as compound-word identification, but we were not too
concerned about the vocabulary-gathering step. The quality of the set of
search vocabulary was ensured by LOD-filtering in the next step. In practice,
determining the scope of the target domain knowledge is sometimes difficult
because systematic expertise is required. To address this, the proposed method
selects a limited number of technical terms as described above, which are
mapped to the LOD.

In step 2, the collected vocabulary is linked with entities in the LOD.
Although various methods of entity linking have been proposed, for our
experiment we linked domain-related terms to the LOD by complete lexical
matching between Japanese terms with the representative label name or alias of
entities. However, this method may yield multiple entities with the same
representative label name or alias. To exclude entities that are weakly
related or unrelated to the target domain, we excluded QIDs adjacent to a
particular QID (X), as shown in Figure 3A. For example, we excluded entities
related to entities such as human (wd:Q5), female name (wd:Q11879590), music
term (wd:Q20202269) and movie (wd:Q11424), where ‘wd:’ is a prefix of
<http://www.wikidata.org/entity/>. For example, ester (wd:Q101487) is a
chemical compound consisting of a carbonyl adjacent to an ether linkage,
whereas Estelle (wd:Q37080997) is a female name. However, in searching in
Japanese words, these terms have the same string representation, and both
entities are searched (i.e. they are homonyms in Japanese). Another example of
a homonym in Japanese is silane (wd:Q410572), a chemical compound name, and
Bletilla striata (wd:Q159003), a plant taxon. As previously described,
Wikidata allows users to write information in a variety of domains. In several
cases, this may be language-dependent. Since these representations cannot be
assumed in advance, the instanceOf relation provides useful information for
distinguishing them.

Another refinement is that QIDs with a particular property can be excluded, as
shown in Figure 3B. For example, entities with a specific property
relation—such as an administrative division (wdt:P131) or gender (wdt:P21)—can
be excluded, where ‘wdt:’ is a prefix of
<http://www.wikidata.org/prop/direct/>. Here silane (wd:Q410572) is also a
homonym in Japanese with Şiran (wd:Q390578), a district of Turkey so we used
the administrative division (wdt:P131) relation to distinguish them. In
addition, when entities are the subject, only entities that have a property
relation of subClassOf (wdt:P279) or instanceOf (wdt:P31) are selected. The
list of entities so obtained is then defined as the set of search entities.

Figure 3: Exclusion model for entities. (A) Exclusion of QIDs adjacent to a
particular QID (X). (B) Exclusion of QIDs with a particular property (Y).

###  4.3 Retrieval of Upper-level Concepts and Identification of Common Upper-
level Entities

Step 3 consists in searching for relations linking each search entity to
upper-level concepts based on the data model shown in Figure 4. Here, the
relation subClassOf (wdt:P279) or instanceOf (wdt:P31) is used to search for
upper-level concepts adjacent to the search entity. For subsequent searches,
only subClassOf is used to retrieve the link path recursively.

Figure 4: Upper-level concept search with the data model.

This yields paths linking upper-level concepts to related search entities, as
shown in Figure 5A. These paths can be integrated to produce a consolidated
structure of upper-level concepts with the individual graphs, as shown in
Figure 5B. In this graph structure, CU entities are identified by counting the
number of unique paths from the search entities using the individual graphs.
The number of search entities below a CU entity is consistent with the number
of neighbouring links concatenated from the search entities (i.e. the values
in the nodes shown in Figure 5B).

CU entities are defined as those with two or more search entities appearing in
their corresponding lower-level concepts. Thus, a CU entity emerging in the
graph and its superordinate concept can be a CU entity (i.e. a chain of CU
entities). The path among those CU entities is called a common path. The
common path is defined as the directed connections between CU entities
identified in the integrated graph; this is the linkage where path duplication
was observed along with the graph integration.

Figure 5: Overview of the algorithm for upper-level concept graph integration
and analysis. (A) Individual graph diagrams. (B) Integrated graph diagram. (C)
Subgraph structures with each ECU entity as a vertex. The numbers in the nodes
indicate the number of search entities that exist in each lower-level concept.

###  4.4 Determination of the Expanded Common Upper-level Entities and the
Number of Expansion Steps

Step 4 consists in performing a graph analysis among CU entities to determine
the starting points for subordinate expansion, which are ECU entities. The
search range from each ECU entity (i.e. the number of steps from an ECU entity
to a search entity) is then calculated. The search range of an ECU entity
should not exceed neighbouring CU entities. To ensure the validity of this
search rule, as a pre-processing step, common paths are removed from the
upper-level concept graph, and the graph structure is then reconstructed. As
shown in Figure 5C, the upper-level concept graph is partitioned into subgraph
structures where each ECU entity is a vertex. Other CU entities that are not
analytically relevant become isolated nodes.

Here, ECU entities are defined as CU entities having two or more search
entities in a lower-level concept of the partitioned complete structure. Then,
the subordinate search range from each ECU entity is defined as the number of
expansion steps (NES) for each ECU entity. The NES values are calculated by
obtaining the shortest number of steps between the ECU entity and each search
entity in the subgraph and defining the maximum value among those steps. Given
an ECU entity (X), a set of shortest distances ($L_{x}$) from entity X to the
N search entities in a subgraph are defined as
$L_{x}={L_{1},L_{2},L_{3},...L_{n}}$. The NES value for the expansion steps
from entity X ($NES_{x}$; i.e. the maximum value among $L_{x}$) is expressed
as follows:

$NES_{x}=max(L_{x})$

By determining the NES values, the corresponding search range of subsets from
each ECU entity (Figure 5C) is specified in the LOD.

###  4.5 Searching for Lower-Level Concepts

In step 5, the obtained NES is used to retrieve lower-level concepts from the
ECU entities and obtain a subset of concepts in the target domain from the
LOD. Concepts are retrieved from the class hierarchy by string-matching-based
searching in the RDF file or SPARQL queries. Figure 6 shows example property
paths used to search for lower-level concepts. In one-step retrieval, the
subClassOf (wdt:P279) or instanceOf (wdt:P31) property is used (Figure 6A). In
two- or three-step retrieval, the subClassOf (wdt:P279) property is used, and
the subClassOf or instanceOf property is then used at the end of the search
path (Figures 6B and 6C). This technique produces a subset of the target class
hierarchy of concepts from the LOD. Empirically, text searching is preferable
for processing RDF files because network communication via a SPARQL search may
not be complete because it is too time consuming or because of communication
failures if the search scale is large.

Figure 6: Typical patterns of property paths for the retrieval of lower-level
concepts. Models for (A) one-step retrieval, (B) two-step retrieval and (C)
three-step retrieval.

###  4.6 Evaluation of the Collected Vocabulary

Step 6 involves evaluating whether the dataset extracted from the LOD is a
valid candidate as a domain ontology. First, a dictionary index vocabulary
must be prepared for the target domain. Correct answers are generated by
judging whether these index terms are present in the LOD. By matching the
obtained candidate vocabulary with the correct answers, the percentage of
correct answers that can be extracted from the candidate vocabulary (i.e. the
recall rate) and the precision rate can be calculated.

To eliminate irrelevant entities, we examined the number of search entities in
each subtree of ECU entities. Subtrees that did not contain any search
entities were excluded as a post-processing step (Figure 7). For NES $=$ 1,
all entities were kept (Figure 7A). For NES $=$ 2, only the subtrees that
contains the search entity were retained (Figure 7B). We also considered a
case of mixed NES values (e.g. NES = 1 & 2; Figure 7C). For NES $\geq$ 3,
subtrees were defined as subconcepts from two steps above the search entity in
addition to the path to the ECU entity (Figure 7DE). Then, the obtained
candidate vocabulary was matched with the correct answers.

Figure 7: Typical patterns of conceptual trimmed steps. Cases with (A) NES =
1, (B) NES = 2, (C) NES = 1 and 2, (D) NES = 3, and (E) NES $\geq$ 4 were
represented. The number in each node shows the number of steps from each
search entity. Each arrow represents a relation in the class hierarchy. The
trimmed region in each graph is highlighted.

##  5 Results

###  5.1 Case Study Using Wikidata with Polymer Materials as the Target Domain

We used PoLyInfo to collect lexical information related to polymer materials
such as polymer names, physical properties and polymerisation methods. We
manually obtained a vocabulary of 510 related terms (in Japanese) from the
description text in PoLyInfo [Figure 8(1)]. Entity linking between the domain
vocabulary and Wikidata entities yielded 199 search entities [Figure 8(2)].
Figure 9 shows the label names and QIDs of representative search entities,
which were classified into sub-categories related to polymer materials and
properties. Then, we obtained individual graphs of upper-level concepts from
each search entity by exploring connections in the upward direction. We
integrated the connections to obtain a graph consisting of 763 entities and
1292 triples, as shown in Figure 10A. Among all the entities in the integrated
graph, we calculated the path linkages between entities and identified 346 CU
entities [Figure 8(3)]. We then removed the common paths in the integrated
graph to obtain the superordinate subgraph for further analytical purposes
(Figure 10B), and we used the graph-analysis algorithm described in Section
3.4 to identify the ECU entities and calculate their NES values [Figure 8(4)].
This resulted in 172 ECU entities, for which Figure 11 shows examples of the
corresponding label names, QIDs, and NES values. Technical terms related to
polymers, chemical processes, and physical properties were found in ECU
entities with NES = 1–4. Abstract entities, such as information and
proposition were found for NES $\geq$ 5.

Figure 8: Application of the proposed method to polymer materials. Figure 9:
Representative search entities obtained from technical terms for polymer
materials and properties. Each item was denoted as [English term] / [Japanese
term] (QIDs with the URI prefix ‘wd:’). Figure 10: Network diagram of an
integrated superordinate graph. (A) The entire integrated superordinate graph.
(B) The superordinate subgraph without the common paths between CU entities.
Figure 11: Representative ECU entities and the number of expansion steps (NES)
obtained from the technical terms for polymer materials and properties. Each
item was denoted as [English term] / [Japanese term] (QID).

We then obtained lower-level concepts for each ECU entity corresponding to the
obtained NES [Figure 8(5)]. Among ECU entities, the largest proportion had NES
= 2 (55 entities), and the number of ECU entities gradually decreased as the
NES increased (Figure 12A). The highest number of concepts was obtained with
NES = 5, followed by those obtained with NES = 4 (Figure 12B). Among the
concepts obtained with NES = 4, about 1.14 million terms were concrete names
for chemical compounds and were lower-level concepts belonging to the
chemical-compound class (wd:Q11173). For subordinate concepts of abstract
entities—such as work (wd:Q386724, NES = 5), artificial physical object
(wd:Q8205328, NES = 7), creative work (wd:Q17537576, NES = 4), object
(wd:Q488383, NES = 5), and spatial entity (wd:Q58416391, NES = 5)—we obtained
a concept count of over 10 million. We calculated about 68.3 million unique
concepts in total, of which 97.7% had NES $\leq$ 5, as shown in Figure 12C.
These 68.3 million concepts were selected as candidates for the ontology of
polymer material sciences.

Figure 12: Relationship between the number of lower-level concepts and NES.
(A) Number of ECU entities at a given NES. (B) Number of concept candidates
without duplicates at a given NES. (C) Cumulative number of concept candidates
without duplicates with integration up to an arbitrary NES. Blue-colored bars
indicate results for all candidates, and orange-colored bars indicate results
for the candidates after the trimming. (B & C) The unit of the vertical axis
is 1 million.

###  5.2 Evaluation of Candidate Concepts for the Ontology

The candidate concepts for the ontology were assumed to include those
unrelated to the target domain. To evaluate candidate concepts for their
inclusion in the ontology, we tried to optimise the NES according to the
percentage of lexical occurrences in the polymer dictionary index [Figure
8(6)]. The evaluation method was based on the number of polymer-related terms
(i.e. correct answers) within each NES. The polymer-related vocabulary was
digitally generated by optical character recognition of Japanese terms in the
Polymer Dictionary (3rd edition, edited by the Society of Polymer Science,
Japan). In total, about 6700 terms were generated. Then, we referred to the
vocabulary extracted from Wikidata and found 2054 matching terms, which we
defined as the total number of correct answers or ground truth. We calculated
the proportion of correct answers (i.e. the recall rate) and the precision
rate for each concept using organised search results up to an arbitrary NES.
Table 1 summarises the number of ECU entities, number of extracted candidate
concepts and Japanese terms, the number of extracted correct answers, and the
recall and precision rates with integration up to an arbitrary NES. The
results showed that the recall rate increased with the number of candidate
concepts. Figure 13A plots the recall rate against the NES and shows that the
recall rate increased up to NES = 5 and then plateaued at greater NES. This
result is mostly consistent with the conjecture that lower-level concepts of
ECU entities with a high NES contain abstract and general terms rather than
technical terms. Conversely, the precision rate gradually decreased up to NES
= 5. Thus, the results suggest that the candidate concepts for the ontology
should contain concepts up to NES = 5.

Figure 13: Results for the recall rate (i.e. the proportion of correct answers
extracted as candidate concepts) and the precision rate.

###  5.3 Trimming Candidate Concepts for the Ontology Based on Search Entities

Actual LODs such as Wikidata are created manually, and they contain unexpected
class hierarchies of concepts that are not strongly related to the domain
knowledge. Thus, the class hierarchy of concepts needs to be pruned as a post-
processing step. Lower-level concepts are retrieved from each ECU entity and
are then narrowed down based on the appearance of search entities in subtrees,
as previously shown in Figure 7. The number of search entities used was the
same (Figure 12A). After pruning, unique number of candidate concepts was
reduced to about a quarter or less (Figure 12BC). An evaluation was then
performed using the trimmed vocabulary of candidates. Gathered up to NES = 7,
the calculated recall rate was 67.0%, and the precision rate was 0.177% (Table
2), indicating similar trends to the case of all candidates (Figure 13). The
number of extracted concepts after the trimming step was 16,063,797 entities.
These results suggest that pruning based on the search entities can increase
the fit to the domain.

##  6 Discussion

Domain-specific vocabulary collection in various fields is not available.
There is still no standardised methodology for building domain-specific
ontologies. In most cases, they are constructed manually based on expert
curation. In this study, we developed a methodology for constructing an
initial model of a domain ontology in an automatic and objective manner, where
a subset of concepts related to the target domain is extracted from LOD. In a
validation experiment, we used Wikidata to construct an initial model of
ontology for polymer material sciences that contained about 68 million
hierarchy concepts corresponding to about 1.2 million Japanese terms.

The proposed method obtains a large number of candidate concepts for the
domain ontology, so the concepts need to be pruned. We presented a trimming
method based on search entities and obtained a trimmed class hierarchy that
contained about 16 million concepts corresponding to about 0.78 million
Japanese terms, suggesting the creation of a more domain-specific ontology.
The trimming step can be improved by incorporating domain-specific named-
entity recognition and contextual analysis (Zhang and Ciravegna, 2011; Li et
al., 2020; Mysore et al., 2019). However, the number of detected ECU entities
would be increased with an increased number of input terms. Thus, the range of
expansion should be limited to obtain a candidate ontology of appropriate
size. In future work, we plan to survey the number of search entities and the
corresponding range of lower-level expansion to determine the appropriate
number of search entities. The appropriate number of search seeds needed to
construct a new domain is still unknown and is a subject for future research.
Because the proposed method focuses on the relationships of concepts in the
class hierarchy, a methodology for extracting conceptual definitions outside
the class hierarchy is also desirable.

In this study, we gathered technical terms by manual selection from the
PoLyInfo database, but another option is to extract vocabulary from technical
documents in the target domain (Ren, 2012). For the LOD mapping, semantics-
matching techniques (e.g. word-embeddings-based approaches) may be useful.
However, the number of candidate concepts obtained by the proposed method is
considered to be sufficient for an initial trial, even with the complete
lexical matching. On another note, it is important to know the limitations of
the current LOD through this proposed method. An attempt to find and add
concepts that do not exist in LODs such as Wikidata is also necessary in the
future.

To generalise the proposed method to ontology construction, it should be
validated using datasets such as the Interlinking Ontology for Biological
Concepts (Kushida et al., 2019), Chemical Entities of Biological Interest
(ChEBI) (Degtyarenko et al., 2007) and Medical Subject Headings (MeSH) RDF
(Bushman et al., 2015). In a preliminary study, we used ChEBI and MeSH RDF
datasets to extract a class hierarchy related to polymer materials. The
proposed method is applicable to these datasets, but it involves some
conversions of the RDF data. As an alternative approach, we found that it is
possible to use terms from a dictionary index as search seeds instead of
manually collecting the vocabulary. Thus, the proposed method has the
potential to be applied to more general datasets with class hierarchies
instead of being limited to Wikidata. To generalize the methodology further is
a future task requring validation using vocabularies from different domain
knowledge.

Research on polymer informatics has focused on using structure-domain
knowledge to reduce the cost and time of designing, generating, and
manufacturing new materials (Zhao and Qian, 2017; Zhao et al., 2018; Ashino,
2010; Huan et al., 2016; Cheung et al., 2008; Adams and Murray-Rust, 2008).
The developed domain ontology can be used to manage the large amounts of data
involved in materials design and to integrate data such as the material
composition, experimental process, and conditions.

##  7 Acknowledgments

This work was partially supported by the New Energy and Industrial Technology
Development Organization (NEDO). The English language was reviewed by Enago
(https://www.enago.jp/).

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