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[cor1]Corresponding author

# WikiLink: an encyclopedia-based semantic network for design innovation

Haoyu Zuo<EMAIL_ADDRESS>Qianzhi Jing<EMAIL_ADDRESS>Tianqi Song
<EMAIL_ADDRESS>Huiting Liu<EMAIL_ADDRESS>Lingyun Sun
<EMAIL_ADDRESS>Peter Childs<EMAIL_ADDRESS>Liuqing Chen
<EMAIL_ADDRESS>Department of Computer Science and Technology, Zhejiang
University,Hangzhou,310030,China  Dyson School of Design Engineering, Imperial
College London,Exhibition Rd, South Kensington,SW7 2AZ,United Kingdom

###### Abstract

Data-driven design and innovation is a process to reuse and provide valuable
and useful information. However, existing semantic networks for design
innovation is built on data source restricted to technological and scientific
information. Besides, existing studies build the edges of a semantic network
only on either statistical or semantic relationships, which is less likely to
make full use of the benefits from both types of relationships and discover
implicit knowledge for design innovation. Therefore, we constructed WikiLink,
a semantic network based on Wikipedia. Combined weight which fuses both the
statistic and semantic weights between concepts is introduced in WikiLink, and
four algorithms are developed for inspiring new ideas. Evaluation experiments
are undertaken and results show that the network is characterised by high
coverage of terms, relationships and disciplines, which proves the network’s
effectiveness and usefulness. Then a demonstration and case study results
indicate that WikiLink can serve as an idea generation tool for innovation in
conceptual design. The source code of WikiLink and the backend data are
provided open-source for more users to explore and build on.

###### keywords:

Design innovation Concept generation Data-driven design Knowledge discovery
Semantic network

##  1 Introduction

Design is a ubiquitous process that occurs throughout a variety of fields.
Conceptual design is the early stage of design where an initial idea is
formulated (Childs, 2013). The progression of conceptual design development
requires a designer to fully utilize their innovation capability and existing
knowledge. In other words, the creative attributes of conceptual design depend
highly on a designer’s ability to master, apply, and utilize human-centred,
scientific and technological knowledge according to the design problem to
provoke design innovation. Researchers have utilized a large amount of imagery
data or textual data available on the internet to provide design intuition for
novel ideas. This imposes a heavy challenge (Hao et al., 2014) for designers
on how to effectively discover and acquire pertinent knowledge and information
to promote design innovation.

With the advent of big data, semantic networks can represent associations well
between ontology-based knowledge, making it easier and more intuitive to
discover implicit knowledge for design innovation. The highly diverse nature
of design suggests that design innovation can benefit from a multiplicity of
distinct data. However, existing semantic networks for design innovation are
built on data sources restricted to technological and scientific knowledge.
Besides, existing studies build the edges of a semantic network only on either
statistical or semantic relationships, which is less likely to make full use
of the benefits from both types of relationships and discover implicit
knowledge for design innovation.

To address the challenges highlighted, this study proposed an encyclopedia
based network called WikiLink for design innovation. The source code of
WikiLink is published on https://github.com/zju-d3/WikiLink. The main
contributions of this paper can be summarized as follows:

  1. [(1)]

  2. 1.

A semantic network for design innovation is constructed. Wikipedia is applied
as the data source for the semantic network, which contains information from a
wide range of fields and expands the data to a new boundary.

  3. 2.

A combined weight is introduced for the relationship in the semantic network.
The combined weight mixes the statistical relationship and semantic
relationship which better captures the implicit connection between concepts
for design innovation. Four algorithms are further developed for design which
enables the retrieval with different levels and manners.

  4. 3.

The constructed semantic network for design innovation is further developed as
a tool. An evaluation and demonstration for the tool are conducted
subsequently, the results show that WikiLink can effectively provide design
stimuli for idea generation.

The paper is organised as follows: section 2 describes the state of knowledge
and background for the research, and section 3 introduces the process of
constructing WikiLink. Section 4 presents the experimentation including the
results on coverage of concepts, coverage of relationships, coverage of
disciplines and term to term relationships. Section 5 demonstrates the use of
four functions in WikiLink and presents a design case with WikiLink. Finally,
section 6 concludes with limitations and suggestions for further research
directions.

##  2 Related Work

###  2.1 Design innovation and idea generation

Design can be regarded as the process of conceiving, developing and realising
products, artefacts, processes, systems, services, platforms and experiences
with the aim of fulfilling identified or perceived needs or desires typically
working within defined or negotiated constraints (Childs, 2013). Design
innovation is the progress of creating innovative design, which needs the
designer to fully utilize their ability to generate a design idea. Normally,
the whole design innovation process can benefit from considering as many ideas
as possible (Liu et al., 2003). Ideas, especially creative ideas, are an
essential part of the design innovation process (Han et al., 2018a, b).

Much research has endeavored to propose novel approaches for idea generation.
The diverse idea generation techniques include brainstorming (Osborn, 1953),
brainwriting (Geschka, 1983), checklists (Ivanov and Cyr, 2014), and synectics
(VanGundy, 1988). Recently, data-driven approaches have attracted researchers’
attention. In the process of design innovation, data-driven approaches attempt
to uncover useful design knowledge from huge, unstructured, heterogeneous, and
highly contextualized data resources (Shi et al., 2017; Cheong et al., 2017;
Luo et al., 2021). Researchers emphasize the importance of generating creative
ideas in design the innovation process from big data (Howard et al., 2008;
Kwon et al., 2018a) and further indicate that creative ideas can originate
from diverse existing knowledge and defined associations.

###  2.2 Semantic network

A semantic network is a graph with nodes representing concepts or individual
objects and edges representing relationships or associations among concepts
(Sowa, 1987). The use of a semantic network can help integrate and migrate
valuable, unstructured data into systematic robust knowledge for design
innovation (Gorti et al., 1998; Rezgui et al., 2011; Georgiev and Georgiev,
2018).

When design work is completed, a great number of data and information are
usually accumulated and reported afterwards (Ackoff, 1989), in the format of
proceedings, literature, patents or public reports. These pieces of recorded
information are expected to be transformed into design knowledge, which is
expected to be reused for unhappened design tasks, to speed up more design
work. When considering knowledge reuse, common knowledge sources generally
include research papers, patent documents, encyclopedias.

Academic papers and patents are original research outcome or totally new
inventions, which contain rich scientific and technological knowledge. Several
attempts (Munoz and Tucker, 2016; Fu et al., 2013; He et al., 2019a; McCaffrey
and Spector, 2018; Shi et al., 2017; Sarica et al., 2020) have been made to
apply the academic paper and patents to a design innovation task. However, one
of the major limitations is that patents and scientific literature are
restricted to only technological and scientific knowledge (Shibata et al.,
2008; Furukawa et al., 2015; Li et al., 2019; Ernst, 2003), while the nature
of design tasks is of high diversity and complexity, with broad coverage of
disciplines. To address the issue, an encyclopedia can be applied for design
innovation since the most notable advantage of an encyclopedia is that it
contains information from a wide range of fields and can expand the design
knowledge coverage to a wider boundary compared with paper and patents (Kwon
et al., 2018b).

###  2.3 Semantic network for design innovation

Over the past decade, several general semantic networks have been developed
such as the lexical database WordNet (Fellbaum, 2010) and ConceptNet (Speer et
al., 2017). These general semantic networks were first developed for
artificial intelligence tasks such as machine translation, natural language
understanding (Sowa, 1987). These lexical semantic networks are utilized
increasingly in the engineering design domain. They are often employed as the
backend knowledge to computational tools for design idea generation and
analysis (Han et al., 2018a, 2020; Bae et al., 2020; Georgiev and Georgiev,
2018). However, these lexical databases built on common-sense knowledge are
not specifically aimed at use in design innovation.

Thus, there is an impetus for developing a design innovation-focused semantic
network to meet the growing demands for engineering knowledge discovery,
technology information retrieval, engineering design aids and innovation
management. An innovation-focused semantic network normally builds nodes
retrieved from a reliable data source and establishes the association based on
statistical or semantic relationship. The statistical relationship that
represents the value on associations are assigned with a statistical
calculation. For example, Shi et al. (2017) created a large semantic network
with statistical relationships in the engineering and design domain. Its
statistical relationships are built on the co-occurrence between each pair of
words in nearly one million engineering papers and one thousand design posts.
He et al. (2019b) created a semantic network with a core-periphery structure
according to the word clouds embedding co-occurrences information. In this
way, the semantic network built the edges on a statistical level and could
support engineering and technology innovation from a statistical perspective.

The semantic relationships are the associations that there exist between the
meanings of words and are applied in many design activities, such as analogy
and metaphor methods (Johnson, 1992; Goel, 1997). As for semantic network for
design innovation, Sarica et al. (2019b) built a large-scale comprehensive
semantic network of technology-related data for engineering knowledge
discovery (TechNet). The semantic relationships between words are established
by using natural language processing techniques to derive the vector of such
terms. Kim and Kim (2012) suggest a cause-and-effect relationship to build a
cause-and-effect function network to support technology innovation. With
semantic relationships, the network could support data integration, knowledge
discovery and in-depth analysis from a semantic perspective (Sarica and Luo,
2021; Sarica et al., 2019a, 2021).

This study plan to build a large encyclopedia based semantic network with
statistical-semantic fused relationships. Inspired by the use of statistical
relationship in a semantic network and semantic relationships in the design
engineering domain, we aim to build a semantic network that combines the
benefits of both the statistical relationship and the semantic relationship to
better capture the implicit connection of cross-domain concepts to better
stimulate design innovation.

##  3 Construction of WikiLink

In this section, we constructed WikiLink, a semantic network based on
Wikipedia data. The Wikipedia items are regarded as the nodes, the interlinks
between the items on the same page are regarded as the directly connected
relationship (edges) between nodes. The edges in the network are assigned with
a fused weight consisting of two types of weight, and four algorithms are
proposed to retrieve relevant knowledge concepts and relationships for design
innovation.

###  3.1 Data source

While patents and scientific literature focus on technological and scientific
knowledge, an encyclopedia is an integrated source of general knowledge and
specific knowledge, with broad coverage of disciplines. Wikipedia, as an
online encyclopedia, is unrestricted by the weight and volume, and has the
potential to be truly comprehensive in knowledge. Wikipedia is written and
maintained by a community of volunteers and offers copies of available content
to anyone to download. WikiLink processes on English Wikipedia pages before
3rd January 2021, comprised 6,408,679 articles. For each Wikipedia article,
WikiLink extracts the titles, main text, ”see also” and categories for further
analysis. Figure 1 is an example page of a Wikipedia article, containing a
title, main text, ”see also” and categories. It should be noted that articles
with a colon in the title are excluded. These articles with a colon account
for 10% of total articles, which are Wikipedia’s administrative pages and are
not relevant as the core source of design information.

###  3.2 Extraction process

Wikipedia covers 13 main categories to group pages on similar subjects, with
each main category having up to 6 layers of subcategories. The deeper the
subcategory is, the more specific Wikipedia’s title will be. The articles are
firstly filtered based on the indicated categories on their article pages to
avoid too specific articles: only the articles within 3-layer subcategories
are kept. The network is constructed based on these selected articles’ title,
main text, and ”see also”.

There are two parts in a semantic network: the nodes and relationships
between. The nodes are from three sections in each Wikipedia article: the
title, the hyperlinks in the main text, and the hyperlinks in the ”see also”
section. These hyperlinks in the main text are chosen as nodes since they are
verified concepts in Wikipedia and indicate explicit associations between
concepts as they occur with other concepts in the same articles.

The relationships are assumed to be established between two concepts if they
co-occur in the same article. Two different criteria are applied for the raw
weight accumulation of each relationship: since there is a large number of
concepts in the main text, if two concepts co-occur in the main text, the
weight is assigned a lower value to avoid dominant concepts; the concept in
”see also” are intrinsically strong associations but with less amount compared
with the concepts in the main text which is assigned a higher weight. The
choice of different weight assignment is determined based on experimental
results: if two concepts co-occur in the main text, the weight will be added
with one; if two concepts occur in the ”see also”, the weight will be added
with nine. The raw weight is accumulated and stored for later filtering. In
this way, the nodes appearing in one article will be interlinked. Taking the
content in Figure 1 as an example, the nodes are “fastText”, “word embeddings”
“Facebook” “unsupervised learning”, “supervised learning”, “Word2vec”,
“Glove”, “Neural Network”, “Natural Language Processing”. The relationships
are established between each pair of nodes because they co-occur in the same
article. In this way, a network can be constructed by processing all articles
in Wikipedia’s database.

Figure 1: An example page in Wikipedia

###  3.3 Construction of edge weights

After the extraction process, an initial network with nodes and edges can be
constructed. In the semantic network, explicit knowledge associations are
direct edges linking pairs of nodes, and implicit knowledge associations are
paths consisting of multiple edges, which means an implicit knowledge
association is essentially a concatenation of a series of interconnected
explicit knowledge associations (Shi et al., 2017). To evaluate the
correlation degree of implicit knowledge associations, the weight of explicit
knowledge associations should be quantified.

####  3.3.1 Semantic cosine similarity weight

In the construction process, the explicit associations are built based on the
interlinked concepts within pages and the corresponding raw weights are
statistically calculated. These statistical relationships construct the basic
edges in a semantic network from a statistical perspective, which provides the
foundation for WikiLink and statistical intuition for information retrieval.
While in design activities, the semantic relationship also contributes much to
design innovation such as analogy and metaphor methods (Hey et al., 2008;
Linsey et al., 2012) from a semantic perspective. Inspired by the implication
of semantic relationship in design innovation activities, the statistical
association between two concepts can be combined and balanced with the
semantic similarity for boosting design innovation. The semantic similarity
can be obtained by transforming all words to vectors and calculating the
semantic cosine similarity between these vectorized concepts. The conventional
word embedding methods like Word2Vec train a unique word embedding for every
individual word. However, Wikipedia contains a large number of terms, with
some of them even being new terms out of vocabulary. FastText (Bojanowski et
al., 2017; Joulin et al., 2016) can solve this issue by treating each word as
the aggregation of its subwords. The vector for a word is simply taken to be
the sum of all vectors of its component char-ngrams. In this way, fastText can
obtain vectors even for out-of-vocabulary (OOV) words, or the new terms in
Wikipedia, by summing up vectors for its component char-ngrams, provided that
at least one of the char-ngrams was present in the training data. When all
concepts have been represented as word vectors, all edges connecting two nodes
are assigned with a value by calculating the semantic cosine similarity
between these vectors.

####  3.3.2 Global normalization and local normalization

In many design models, the design innovation process usually involves two
important phases: divergence and convergence. For example, there are rounds of
divergent and convergent phases in the ”double diamond” design process model
(The Design Council, 2017). Divergence is a phase that encourages exploring
different solutions as much as possible while convergence follows a particular
set of logical steps to arrive at one solution which in some cases is a
”correct” solution. Inspired by the principles of divergence and convergence,
the retrieval behaviors can be facilitated in two distinct ways: a ”general”
and ”specific” ways. ”General” means the nodes are common and basic concepts
with a relatively general meaning, which tends to lead divergent thinking in a
design innovation process. While ”specific” means the nodes are detailed and
domain-specific concepts, which has higher potential to guide convergent
thinking. The ”general” and ”specific” retrieval are realized by normalizing
the raw weight with a globalization method as shown in equation (1) and a
localization method as shown in equation (2):

$w_{ij}^{g}=\left(w_{ij}-w_{min}\right)/\left(w_{max}-w_{min}\right)$ (1)

$w_{ij}^{l}=w_{ij}/S_{i}$ (2)

where wmax and wmin are the maximum and minimum value of the raw weight in the
whole network. wij is the raw weight between the node i and node j, Si is the
sum value of all raw weights of edges around node i.

The global normalization performs feature scaling normalization from a global
perspective, in which wijg expresses the significance of the strength compared
to the whole network. Global normalization tends to retrieve more ”general”
concepts(Shi et al., 2017). The local normalization performs feature scaling
normalization from a local perspective, in which wiji expresses the relative
importance of the strength compared to its own adjacent value. Local
normalization tends to extract more domain-specific concepts.

####  3.3.3 Geometric mean and harmonic mean

Since an implicit knowledge association is essentially a concatenation of a
series of explicit associations, the accumulation of the strength of contained
explicit associations (edges) can potentially indicate the correlation degree
of the implicit association (path). Therefore, in order to reflect the overall
strength of all the explicit associations in an arbitrary implicit
association, the retrieval behaviors can be facilitated in two distinct ways:
one type of retrieval, referred to as ”basic”, is a short implicit association
across fewer edges focusing on relevant concepts which tend to be in the same
domain while another type, referred to as ”professional”, is a long implicit
association with more edges across multiple distant domains. Therefore, the
geometric mean (GM) and the harmonic mean (HM) are applied on the normalized
weights for different design innovation behaviors.

The geometric mean(GM) and harmonic mean(HM) are given in equation (3) and (4)
respectively:

$\textrm{GM:}w_{(k_{1}-k_{2}-\cdots-
k_{n+1})}=\sqrt[n]{\prod_{k=1}^{n}w_{k,k+1}}$ (3)

$\textrm{HM:}w_{(k_{1}-k_{2}-\cdots-
k_{n+1})}=\frac{n}{\sum_{k=1}^{n}\frac{1}{w_{k,k+1}}}$ (4)

where the w(k1-k2-…-kn+1) is the overall weight of the path, wk,k+1 is each
weight along the path.

###  3.4 Four algorithms for design innovation

The primary use of the design semantic network is to retrieve relevant
knowledge concepts and relationships for design innovation. In addition to
retrieving around a single concept, retrieving the implicit associations
between two distant knowledge concepts is also introduced. Four algorithms are
developed by applying the normalization and mean methods to the proposed
retrieval approach. The four algorithms, which are ”Explore-General”,
”Explore-Specific”, ”Search Path-Basic” and ”Search Path-Professional” are
applied as four functions in WikiLink.

Figure 2: Four functions in the panel of WikiLink

The ”Explore” algorithm is used to explore and retrieve around a single
knowledge concept. The retrieved results can be classified as either ”general”
or ”specific”. The ”Explore” function panel in WikiLink is shown in Figure 2.
Specifically, since it is preferred to retrieve both ”general” and ”specific”
knowledge concepts related to a query, we apply two different normalization
algorithms with distinct retrieval behaviours in this ”Explore” function. One
is global normalization to retrieve ”general” concepts for divergence, and the
another is local normalization to retrieve ”specific” concepts for
convergence. The overall weight is calculated on a combination of the
statistical weight and the semantic weight. The algorithm for ”Explore-
General”, and ”Explore-Specific” are given in equations (5) and (6)
respectively:

$w_{explore}^{general}=0.3\times(1-w_{semantic})+0.7\times w^{g}$ (5)

$\begin{split}w_{explore}^{specific}=0.2\times(1-w_{semantic})+0.8\times
w^{l}\end{split}$ (6)

where the wsemantic is the semantic cosine similarity weight, wg is the
statistical weight after global normalization and wl is the statistical weight
after local normalization.The weight in the algorithm are determined based on
experimental results.

The ”Explore” algorithms are further combined with the single source
Dijkstra’s shortest path algorithm, which starts from the source query to
retrieve all reachable nodes in order from the shortest distance. In addition,
a ”Minimum Step” functionality is provided on the ”Explore” panel, where
knowledge associations with edges less than the number of the defined minimum
step are filtering out for paths with fewer steps. Therefore, the knowledge
associations are retrieved and ranked under the combined weight with the
minimum step.

The ”Search Path” algorithm is used to find implicit associations as paths are
given two knowledge concepts. The retrieval result can be classified as either
”basic” or ”professional”, where ”basic” means the path is short and nodes are
general concepts while ”professional” means the paths are long and nodes are
domain-specific concepts. The ”Search Path” function panel in WikiLink is
shown on the right side of Figure 2. Specifically, besides two different
normalization algorithms, the geometric mean(GM) is further applied to
retrieve short implicit associations across fewer edges focusing on relevant
knowledge while harmonic mean(HM) is applied to retrieve long implicit
associations with more edges across multiple domains.

The algorithm of ”Search Path-Basic” and ”Search Path-Professional” are given
in equations (7)(8) and (9)(10) respectively:

$w_{(k_{1}-k_{2}-\cdots-
k_{n+1})}^{basic}=\sqrt[n]{\prod_{k=1}^{n}w_{k,k+1}^{basic}}$ (7)

$\begin{split}w_{k,k+1}^{basic}=0.3\times(1-w_{semantic})+0.7\times
w^{g}\end{split}$ (8)

$w_{(k_{1}-k_{2}-\cdots-
k_{n+1})}^{professional}=\frac{n}{\sum_{k=1}^{n}\frac{1}{w_{k,k+1}^{professional}}}$
(9)

$\begin{split}w_{k,k+1}^{professional}=0.2\times(1-w_{semantic})+0.8\times
w^{l}\end{split}$ (10)

where the wsemantic is the semantic cosine similarity weight, wg is the
statistical weight after global normalization and wl is the statistical weight
after local normalization.

##  4 Evaluation

In this section, we conduct four studies on WikiLink to demonstrate its
effectiveness and usefulness. Some other semantic networks, which are publicly
accepted or aiming for design innovation, are selected as benchmarks during
the comparison, including B-link, WordNet and ConceptNet. The evaluation is
conducted from four perspectives, i.e., coverage of concepts, coverage of
relationships, coverage of disciplines, term-to-term evaluation and
effectiveness of combined relationships to provide an overview of the
strengths and weaknesses of WikiLink.

###  4.1 Coverage of golden concepts

In order to demonstrate the feasibility of WikiLink, golden concepts, which
are composed of words and terms, are defined as the benchmark to evaluate
WikiLink’s term coverage. The golden concepts are collected manually within an
online source Encyclopedia Britannica through several steps. Firstly featured
concepts are obtained from its website. There are several categories of topics
available concerning different domains, including culture, science, and
technology. By gathering these classified words and terms, it is ensured that
the collected data contains interdisciplinary knowledge. The original data is
refined afterward by removing uncommon expressions and standardizing their
formats. The aim of this step is to assure the precision and impartiality of
the following evaluations. Eventually, we obtain a list of 468 words and
terms, covering knowledge in 8 domains, and part of the concepts are shown in
Table 1.

Table 1: The overview of golden concepts Categories |  Related concepts
---|---
Animal |  bird, chordate, coral, insect, sea otter, …
Art |  acting, ballade, chinese literature, emmy award, film, …
Event |  american civil war, bronze age, cold war, french revolution, hurricane katrina, …
Place |  africa, anatolia, berlin, cape town, indonesia, …
Plant |  carnivorous plant, venus flytrap, …
Science |  atmosphere, brain, carbohydrate, chemistry, disease, ….
Sports |  athletics, boxing, gymnastics, rugby, …
Technology |  airplane, bicycle, industry, radar, smartphone, supercomputer, …
Topic |  accident, architecture, buddhism, cbs corporation, democracy, …
|

With these golden concepts, we then evaluate how many concepts are contained
in WikiLink. The retrieval rate $C_{R}$, as shown in equation (11), is applied
as the metric of concept retrieval:

$C_{R}=\frac{n_{C}}{N_{C}}$ (11)

where $n_{C}$ means how many concepts are contained in the network, while
$N_{C}$ represents the number of golden concepts, which is 468 is this case.

WordNet and ConceptNet are used as two benchmarks for evaluation. It is
observed that WordNet only contains 209 concepts, resulting in a low $C_{R}$
rate of 0.449. The specific $C_{R}$ values of different categories are shown
separately in Table 2, from which we notice that WikiLink gives the highest
retrieval rate, indicating that our network has a wider coverage of concepts
compared with the other tools considered.

Table 2: Retrieving results of golden concepts Categories |  WordNet |  ConceptNet |  WikiLink
---|---|---|---
Total Rate CR |  0.449 |  0.810 |  0.938
art |  0.386 |  0.818 |  0.841
animal |  1.000 |  1.000 |  1.000
event |  0.037 |  0.630 |  0.963
place |  0.602 |  1.000 |  1.000
plant |  0.333 |  1.000 |  1.000
science |  0.631 |  0.954 |  0.954
sports |  0.652 |  0.957 |  0.913
technology |  0.636 |  0.818 |  0.909
topic |  0.287 |  0.638 |  0.920
|  |  |

To be specific, our approach involves more concepts in most categories and
achieves the highest retrieval rate. In comparison, WordNet shows overall
weaknesses, due to its inadequacy in processing two-word terms. ConceptNet has
decent performance in the fields of art, science, sports, and technology, but
it lacks strengths in certain categories such as topics and events.

This result can be explained by the limitation of ConceptNet’s construction
properties. Even though the data source of ConceptNet includes two-word terms,
such as stained glass, chemical element, and mental disorder, these terms are
mostly composed of one adjective and one noun. Except for names of countries
and regions, seldom are two-noun terms involved in ConceptNet. Based on our
observation, plenty of concepts in those two categories, i.e., topics and
events, are composed of more than one noun, e.g., teacher education, Paris
agreement, and pacific crest trail, which are exactly situations that
ConceptNet lacks solution to. This explains ConceptNet’s low $C_{R}$ rate for
those two categories. In contrast, our approach can deal with various kinds of
terms, which explains its overall high coverage. This high coverage of
concepts can support design innovation with a large concept space.

###  4.2 Coverage of golden relationships

A list of golden relationships is selected from the data source as the
evaluation benchmark to quantitatively evaluate the performance of
relationship coverage. Similar to the construction process of WikiLink, we
extracted concept relationships from Encyclopedia Britannica’s spotlight
articles. Only those which are composed of golden concepts are retained. We
randomly picked 1000 concept pairs from the retained ones and defined as
golden relationships.

Denoting golden relationships as set $H$, we compare the performance of
WikiLink with other tools in terms of the coverage of golden relationships. In
this process, we retrieve all relationships between golden concepts from each
tool, and denote these retrieved relationships as set $V$. The evaluation
metric is defined as follows:

$\displaystyle\begin{aligned} R=\frac{\lvert V\cap H\rvert}{\lvert
H\rvert}\end{aligned}$ (12)

where $R$ indicates the retrieving rate of relationships. WordNet and
ConceptNet are chosen as benchmarks, and the results are shown in the Table 3.

Table 3: Evaluation results of golden relationships Categories |  Count |  R
---|---|---
WordNet |  15 |  0.015
ConceptNet |  170 |  0.170
WikiLink |  721 |  0.721
|  |

Specifically, 15 relationships are retrieved from WordNet, which belong to
golden relationships, leading to a significantly low $R$ value of only 0.015.
This retrieving rate can be explained by WordNet’s data structure. To our
knowledge, WordNet only retrieves specific relationships, including
”synonyms”, ”sister terms”, ”hypernyms”, and ”hyponyms”, between two concepts,
which leads to its huge deficiency in context association and results in a low
retrieving rate.

The web API of ConceptNet is used to retrieve concepts and relationships. It
turns out that there are 170 relationships which are found in the golden
relationships, resulting in a $R$ value of 0.170. The retrieving rate can be
understood from two perspectives. ConceptNet’s network contains more concepts
than WordNet, which can be observed from its $C_{R}$ value. In addition, it
provides richer explanations for ”relationships”. In other words, as well as
”synonyms” and ”hypernyms”, ConceptNet is also able to retrieve ”related
terms” and ”terms with this context” for an arbitrary single concept. These
two reasons both contribute to its retrieving rate.

In the end, 721 relationships can be retrieved from the golden relationships
within WikiLink. This can be explained by its largest number of concepts, and
the relationships in our approach are defined differently, i.e., they are
established between concepts that are shown on the same pages. To summarize,
WikiLink achieves a retrieving rate of 0.721 and shows the best performance.
This high retrieving rate of relationships builds enough associations which
can potentially contribute to design innovation.

###  4.3 Coverage of categories

To prove that WikiLink covers a wide range of categories, we categorize and
count all the nodes in WikiLink according to Wikipedia’s category rules.
Wikipedia defines 13 main categories: cultural, geography, health, history,
human, mathematics, natural, people, philosophy, religion, society,
technology, and reference. By traversing all the items’ categories in
WikiLink, the distribution of the 13 categories is presented in Figure 3.

Figure 3: The distribution of concepts in WikiLink

It can be seen from the graph that WikiLink’s data have a wide distribution
among 13 main categories, and the count of a particular main category can
reach up to 100,000 level and even higher. Especially, the natural, people and
reference categories have the largest counts, which are 1,241,491, 1,161,583
and 1,222,966 respectively. Rather than focusing only on technological and
scientific knowledge, WikiLink is a more generic semantic network, with
knowledge from a wide coverage of disciplines, which can be used in daily
design innovation activities to obtain inspiration. Specifically, the data
source of B-link mainly comes from scientific papers, which leads to the
uneven distribution of each discipline, while WikiLink has a wide range of
information in different fields and disciplines. Compared with TechNet, the
result of WikiLink shows higher diversity as the distribution of TechNet is
highly correlated with the distribution of patents, which may affect the
inspiration of the design because of the coverage limitation, even though it
contains a large number of domains within technology fields.

###  4.4 Term to term evaluation

To evaluate whether the computed edge weights are consistent with human
judgement, thirty term pairs (three groups and each ten as a group)
representing various degrees of relevance were prepared by language experts
and ten students were employed to rate the relevance of each pair. The
students scored semantic relevance and statistical relevance on a five-scale
from one (not related) to five (highly related), and the average of scores is
computed for each pair. The semantic relevance and statistical relevance are
then combined as the weight in ”Explore-General” algorithm. In this
evaluation, only the ”Explore-General” edge weights in the four algorithms is
evaluated since the weight calculation in the four algorithms is all similar.

With the evaluation results, Cronbach’s alpha is used to measure the inter-
rater reliability which is 0.78 as an acceptable result. Spearman’s rank
correlation coefficient is then used to assess the relationship between
computed edge weights and human judgments. Table 4 shows the result of the
Spearman rank correlation coefficients between the pairwise association values
of the same term pairs.

Table 4: Term to term evaluation results Group Number | Spearman Correlation
---|---
1 | 0.69
2 | 0.89
3 | 0.64
|

The hypothesis of Spearman correlation coefficient is then tested to determine
whether the results are statistically significant. By checking the table of
critical values, the three groups’ Spearman’s rho are all greater than the
critical value 0.57 (one tail,$\alpha$=0.05), so the null hypothesis is
rejected. This proves that there is a strong correlation between the computed
edge weights and human judgments , upheld by a significance level of 95%.

###  4.5 Effectiveness of combined relationships

As introduced in section 3, the statistical relationships between two concepts
are established if they co-occur in the same article. Constructing the basic
connection from a statistical perspective only could potentially lead to a
phenomenon that the retrieval is dominated by some highly common concepts.
These dominating common concepts decrease the retrieval probability of other
useful concepts for design innovation. However, using semantic relationships
only as the weight of edges is beneficial for design but might require in a
longer association for implicit knowledge discovery. The semantic
relationships are thus incorporated to balance the statistical relationship.
To demonstrate the effectiveness of the proposed weight fusion, three types of
retrieval results based on different relationships (networks with combined
relationships, with statistical relationships, and with semantic
relationships) are compared. The concept “health” is chosen for the “Explore”
function and the concept pair “health & 3d printing” is chosen for the “Search
Path” function.

Table 5: The high-correlated knowledge associations between ”health” and ”3d printing” with three different relationships | Combined relationship | Statistical relationship | Semantic relationship
---|---|---|---
Basic |  health $\rightarrow$ economics $\rightarrow$ Massachusetts Institute of Technology $\rightarrow$ 3D printing |  health $\rightarrow$ education $\rightarrow$ United States $\rightarrow$ The New York Times $\rightarrow$ artificial intelligence $\rightarrow$ 3D printing |  health $\rightarrow$ health care $\rightarrow$ palliative care $\rightarrow$ intensive care unit $\rightarrow$ 3D printing
Professional |  health $\rightarrow$ construction $\rightarrow$ ladder $\rightarrow$ 3D printing |  health $\rightarrow$ physical fitness $\rightarrow$ physical strength $\rightarrow$ eccentric contraction $\rightarrow$ weight plate $\rightarrow$ knurling $\rightarrow$ deep drawing $\rightarrow$ hydroforming $\rightarrow$ direct metal laser sintering $\rightarrow$ rapid prototyping $\rightarrow$ 3D printing |  health $\rightarrow$ health care $\rightarrow$ palliative care $\rightarrow$ intensive care unit $\rightarrow$ 3D printing
|  |  |
| Combined relationship | Statistical relationship | Semantic relationship
---|---|---|---
General |  |  |
Specific |  |  |
Figure 4: Retrieval results for ”health” with three different relationships Table 6: The average node degree of retrieval results for ”health” with two different relationship Category | ”Explore-General” | ”Explore-Specific”
---|---|---
Statistical relationship | 536 | 63
Combined relationship | 308 | 32
|  |
Table 7: The average node degree of knowledge associations between ”health” and ”3d printing” with two different relationship Category | ”Search Path-Basic” | ”Search Path-Professional”
---|---|---
Statistical relationship | 565 | 139
Combined relationship | 473 | 131
|  |

Figure 4 and Table 5 are the results of “Explore” and “Search Path”
respectively. It can be seen that the results of “Explore” and “Search Path”
with statistical relationship have more concepts which contain common and
general meaning but are irrelevant with “health” semantically, e.g., “United
States” and “United Kingdom” which are dominant nodes in this case.
Conversely, the results of the two functions with semantic relationships
contain more relevant concepts but only show the semantic relevance to
“health” (e.g., “environmental health” and “health care”). The combined
relationship makes a balance between the statistical relationship and semantic
relationship so that it produces a relatively positive result. The node degree
of a concept means the sum of weights of all edges incident to that node. The
average node degree of concepts are calculated in combined relationships and
statistical relationships to demonstrate whether the very common results are
balanced quantitatively. Table 6 and Table 7 shows that, in four functions,
the average node degrees of concepts with combined relationship are all
observably lower than that of concepts with statistical relationship, which
imply that the semantic relationship balances the statistical relationship to
retrieve valuable information. Both the quantitative and qualitative results
indicate that the combined relationship is efficient to reduce the influence
of dominant concepts with high node degree in retrieval results thus could
facilitate design innovation activities.

##  5 Demonstration

In this section, we showcase four functions in WikiLink for information
retrieval and design innovation. Qualitative analysis of the results is
performed to demonstrate the features of each function. In addition, the
performance of WikiLink is compared with four state-of-the-art tools, and the
corresponding results are also analyzed qualitatively.

| ”3d printing” | ”fused deposition modeling”
---|---|---
General |  |
Specific |  |
Figure 5: Retrieval results for ”3d printing” and ”fused deposition modeling”
in ”Explore-General” and ”Explore-Specific” mode

###  5.1 The ”Explore-General” and ”Explore-Specific” mode

To fairly compare the performance of ”Explore-General” and ”Explore-Specific”
modes, two terms in the field of engineering design are chosen: ”3d printing”
and ”fused deposition modeling”. 3D printing is a multi-faceted technology and
has been employed across a broad range of applications (Berman, 2012), and is
a widely used term with general meanings. Fused deposition modeling (FDM) is a
3D printing method that heats a continuous thermoplastic filament and extrudes
it for layer-by-layer deposition (Hamzah et al., 2018), which is also a widely
used term with specific meanings. These two terms are inputted and explored in
”general” and ”specific” modes, respectively. Figure 5 shows the top 10
relevant terms in each retrieval. By comparing the ”general” results (the
first row) with the ”specific” results (the second row), it can be seen that
the terms in ”general” results are more common and comprehensible, such as
computer-aided design, and artificial intelligence, while the terms in
”specific” results, such as stl (file format) and polyetherimide, are normally
very specific concepts in particular domains. Furthermore, as the figure
shows, FDM’s specific result is centered scattering. It implies that primary
terms in a particular domain are discrete and irrelevant to each other.

Table 8: The high-correlated two types of knowledge associations

|  Brain & Computer |  Avocado & Chair
---|---|---
3*Basic |  brain $\rightarrow$ artificial intelligence $\rightarrow$ computer |  avocado $\rightarrow$ fruit $\rightarrow$ furniture $\rightarrow$ chair
|  brain $\rightarrow$ biology $\rightarrow$ computer |  avocado $\rightarrow$ walnut $\rightarrow$ furniture $\rightarrow$ chair
5*Professional |  brain $\rightarrow$ neuroscience $\rightarrow$ psychology $\rightarrow$ science $\rightarrow$ technology $\rightarrow$ computer |  avocado $\rightarrow$ guacamole $\rightarrow$ burrito $\rightarrow$ xylitol $\rightarrow$ product call $\rightarrow$ ikea $\rightarrow$ rocking chair $\rightarrow$ chair
|  brain $\rightarrow$ neuroscience $\rightarrow$ psychology $\rightarrow$ science $\rightarrow$ technology $\rightarrow$ internet $\rightarrow$ computer |  avocado $\rightarrow$ guacamole $\rightarrow$ taco $\rightarrow$ hockey puck $\rightarrow$ potato chips $\rightarrow$ ladder $\rightarrow$ rocking chair $\rightarrow$ chair
|  |

###  5.2 The ”Search Path-Basic” and ”Search Path-Professional” mode

The ”Search Path” function allows users to explore the implicit associations
between two items even from different domains. It also has two modes that can
return two types of associations. In order to test the above two modes, we
used two pairs of terms, ”brain” and ”computer”, which are weakly related, and
”avocado” and ”chair”, which are seemingly unrelated. Table 5.1 shows the
retrieved highest-correlated ”basic” and ”professional” knowledge associations
of the two pairs. Obviously, the ”basic” paths are shorter and the
”professional” paths are longer. Besides, most of the nodes in ”basic” paths
are concepts with general meanings between the two domains, such as artificial
intelligence, fruit and furniture, while the ”professional” path is longer and
the nodes are almost scientific terms or specific objects such as
”neuroscience”, ”xylitol” and ”guacamole”. Some explicit associations are
discovered in the results. For example, brain science drives the advance of
computer science, especially artificial intelligence, which appears in the
path ”brain → artificial intelligence → computer”. In addition, more implicit
associations are connected by some surprising concepts like ”fruit”,
”furniture” and ”rocking chair”, which may remind the idea of fruit-shaped
furniture such as an avocado-shaped rocking chair. It is found that, in some
cases, purely statistical weights between edges result in a longer and more
surprising path which may inspire more innovative ideas in design activities.

|  |
---|---|---
(a) The ”Explore Specific” results for ”metaverse” |  (b) The ”Search Path-Professional” results between ”metaverse” and ”shopping” |  (c) The ”Search Path-Professional” results between ”metaverse” and ”meeting”
Figure 6: Comparisons of the results from ”Explore” and ”Search path”

###  5.3 The ”Explore” and ”Search Path” function

The above shows that the ”Explore” function aims to discover the knowledge
associations around a single term, while the ”Search Path” function aims to
search for the associations between two terms. To clarify the difference
between them, a hot concept in engineering design, ”metaverse”, was explored
along with two weakly related terms separately: ”shopping” and ”meeting”.
Retrieval experiments were conducted in ”Explore-Specific” and ”Search Path-
Professional” mode respectively. As shown in Figure 6, the retrieval results
of ”metaverse” cover a wide range of fields including ”virtual world”,
”simulated reality”, ”cyberspace” and related games including ”Second Life”
and ”Active Worlds”. These wide results can lead to comprehensive knowledge
discovery and an open imagination about the target term. On the other hand,
the paths between ”metaverse” and a selected concept focus on bridging the
fields that connect them, which leads to combinational ideas. For instance,
the nodes linking ”metaverse” and ”shopping” are related to ”virtual economy”,
and the nodes linked ”metaverse” and ”meeting” are related to virtual society.

Table 9: The top 10 related terms to ”neural network” and ”trypsin” in WikiLink and the four benchmark tools | Neural Network | Trypsin
---|---|---
WikiLink (general) | deep learning, google, c++, linux, cross-platform, javascript, open-source software, operating system, perl | amino acid, pancreas, enzyme, transcription(genetics), translation(genetics), base pair(genetics), life, active site, translation(biology), stroke
WikiLink (specific) | classification rule, deep learning, cognitive model, stockfish(chess), machine learning, black box, Hebbian learning, list of memory biases, deepmind, artificial neuron | phenylisothiocyanate, myotoxin, triosephosphateisomerase, zymogen, tandem mass spectrometry, peptide mass fingerprinting, ligase, dihydrofolate reductase, pepsin, papain
B-link (general) | genetic algorithm, optimization, fuzzy logic, classification, pattern recognition, artificial neural network, multi-objective optimization, simulated annealing, simulation, response surface methodology | chymotrypsin, protease, pepsin, purification, thrombin, digestion, characterization, expression, synthesis, crystal structure
B-link (specific) | backpropagation, genetic algorithm, fuzzy logic, self-organizing map, multilayer perceptron, backpropagation algorithm, neuro-fuzzy, pattern recognition, neuro-fuzzy system, artificial intelligence | chymotrypsin, enzyme thermostability, modified enzyme, pepsin, protease-activated receptor-2, protease-activated receptor, digestive protease, pyloric caecum, carboxypeptidase a, viscera
ConceptNet | neural net; autoencoder, backpropagation, catastrophic interference, computational intelligence, condela, convolutional neural network, dropout, hidden layer | antitrypsin, antitryptic, apronitin, chymotrypsin, endopeptidase, enterokinase, meromyosin, mesotrypsin, ovoinhibitor, ovomucin
WordNet | neural net, computer architecture, network of neurons, network of nuclei | enzyme, pancreas, protein, polypeptide units
TechNet | artificial neural network, machine learning, training data, pattern recognition, hidden layer, layer node, upper hidden layer, neuron, residual activation, automobile overspeed, vehicular safety sensor, time many | Proteolytic enzyme, pepsin trypsin, subtilisin family, bromelain ficin, proteolytic, no amidolytic, enzymatic, amidolytic, protease, trypsin thrombin plasmin, dynorphin targeting moiety, irtx
|  |

###  5.4 Comparison with benchmark tools

We undertook a retrieval comparison between WikiLink and the other four
benchmark tools. The target terms are ”neural network” in computer science and
”trypsin” in medical physiology. The aim of this experiment is to test whether
our network can return a broad range of related terms which are able to
stimulate innovation in the design process efficiently. Since the number and
presentation of retrieval results vary from tool to tool, we have selected the
top 10 related terms for each tool to present in Table 9. Especially, the
results of WikiLink and B-link (Shi et al., 2017) were obtained through their
”Explore” function, the result of ConceptNet (Speer et al., 2017) was obtained
from its ”Related terms” category, the result of WordNet (Fellbaum, 2010) was
obtained from its ”Synset” and ”Example sentence” functions. According to
Table 9, the terms retrieved by WikiLink in the ”general” and ”specific” modes
both prove the effectiveness of the ”Explore” function. For example, the
retrieval results of ”neural network” in the ”specific” mode are all domain-
specific terms related to the components (e.g., ”artificial neuron”),
functions (e.g., ”cognitive model”) and applications (e.g., ”deep learning”)
of ”neural network”. Since the ”Explore” function of WikiLink is divided into
the ”Explore-General” and ”Explore-Specific” modes, its results, containing
common terms (from the ”Explore-General” mode) and technical terms (from the
”Explore-Specific” mode), cover a comprehensive range. In contrast,
ConceptNet, WordNet and TechNet simply have only one retrieval mode, which
leads to their retrieved results invariably focus on some technical terms in a
specific range. Even though B-Link retrieves in the two modes as WikiLink, its
results are also limited by the data source which are engineering academic
papers and design websites. It can be seen that the retrieved terms of B-Link
tend to contain specific meanings. Instead, WikiLink applies Wikipedia as the
data source for its semantic network, which covers information from a wide
range of domains. The comparison suggests that WikiLink is more capable of
retrieving terms in various domains, which is essential for knowledge
discovery in the knowledge-intensive design innovation process.

###  5.5 A design case

A designer is recruited to conduct a design case and demonstrate the process
of applying WikiLink for design innovation. Generally, the designer would be
initially given a design question with a ”Basic word”, and then required to
apply the ”Explore” function and ”Search path” function in WikiLink to freely
explore the related concepts around the ”Basic word” which could potentially
inspire the designer. By applying the ”Explore function”, the designer could
discover the knowledge concepts ”C1”, ”C2”, ”C3” around the ”Basic word” as
denoted in Figure 7. While the ”Search path” function provides the paths, e.g
PathC1C2 between two terms ”C1” and ”C2” for combinational creativity (Han et
al., 2019). This process can be iteratively applied to discover knowledge
associations and paths such as ”C3” and PathC1C3. The related concepts
obtained from WikiLink are then used to form design inspiration links such as
”Basic_word-C1, PathC1C3, PathC2C3” and some of them are eventually chosen for
the design output of specific design ideas.

Figure 7: The flow of concepts exploration in WikiLink

A real design case is conducted to illustrate how to facilitate design
innovation with WikiLink. Since ”hair dryer” is a well-known product of which
the homogenization in the market is serious and its innovation has encountered
a bottleneck, the designer is required to generate ideas and provide
innovative designs for hair dryer. The concept ”hair dryer” is chosen as the
design query (also known as the basic word) in WikiLink in this case. The
designer then started with the ”Explore” function by freely choosing several
different step lengths and switching between general and specific mode for
divergent and convergent thinking. Some screenshot examples are shown in
Figure 8. It is noted that the designer is not restricted to using ”hair
dryer” as the query only. After the initial exploration in WikiLink, the
designer obtained some interesting and inspiring concepts, such as
”Entertainment weekly”, ”Vacuum cleaner”, ”Comb”,”Hair iron”,” Hair gel”,”
Hair roller”,” Hot comb”,” Horn” and ”Pyramid”. The next step is to apply the
”Search Path” function by freely querying the paths between two concepts of
the designer’s interests. Some retrieval results are shown in Table 10.

|  |
---|---|---
(a) ”Explore-General” results with one step for ”hair dryer” |  (b) ”Explore-General” results with two steps for ”hair dryer” |  (c) ”Explore-Specific” results with two step for ”hair dryer”
Figure 8: The examples of concepts retrieved by ”Explore” Table 10: The paths between the inspiring concepts and ”hair dryer” retrieved by ”Search Path” Query concepts |  Mode |  Retrieval results
---|---|---
hair dryer & entertainment weekly |  Basic |  hair dryer $\rightarrow$ vacuum cleaner $\rightarrow$ automobile $\rightarrow$ united states $\rightarrow$ entertainment weekly
hair dryer & entertainment weekly |  Professional |  hair dryer $\rightarrow$ hair iron $\rightarrow$ natural hair movement $\rightarrow$ afro $\rightarrow$ tie-dye $\rightarrow$ zardozi $\rightarrow$ choli $\rightarrow$ crop top $\rightarrow$ the face (magazine) $\rightarrow$ arena (magazine) $\rightarrow$ loaded (magazine) $\rightarrow$ fhm’s 100 sexiest women (uk) $\rightarrow$ fhm $\rightarrow$ maxim (magazine) $\rightarrow$ people (magazine) $\rightarrow$ entertainment weekly
hair dryer & tie-dye |  Basic |  hair dryer $\rightarrow$ vacuum cleaner $\rightarrow$ automobile $\rightarrow$ textile $\rightarrow$ tie-dye
|  |

Afterwards, the designer continued to explore knowledge concepts for design
innovation stimuli by iteratively using the ”Explore” and ”Search path”
functions. The ”Explore” function helps discover the knowledge associations
around a single term, while the ”Search path” function can potentially look
for the associations between two terms. The designer recorded all the
interesting and inspiring concepts and formed the ”design inspiration links”,
as shown in Fig 9, where the base of the link is ”Hair Dryer”, and rest of the
concepts were from WikiLink obtained by using ”Explore” and ”Search path”
functions. The above process was repeated to produce at least one design
inspiration link and until the designer thought it is enough to formulate
design ideas. Eventually, with the ideas originating from the concepts in the
inspiration link, the designer produced the final complete design scheme and
drew corresponding design sketches.

Figure 9: The example of “design inspiration link”

In particular, we use Figure 10 and 11 are the designs produced with the
inspiration links ”Hair dryer” – ”Comb” – ”Hairstyle” - ”Tie-dye” – ”Zardozi”.

Figure 10: The sketch of hair dryer inspired by ”Tie-dye” and ”Zardozi” Figure
11: The sketch of hair dryer inspired by ”Comb”

In particular, two ideas were generated during the designer’s manipulation
with WikiLink. The first design, as shown in Figure 10, is an appearance
design inspired by ”Tie-dye” and ”Zardozi”. The existing hair dryers in the
market is mostly in pure color with a smooth or frosted plastic shell. ”Tie-
dye”, the characteristic of the Bai nationality, has special patterns which
are uneven in-depth and rich in layers, and overcomes the rigidity of pure
color. ”Zardozi”, a traditional Chinese craft, has a delicate touch feeling
compared with plastic material. Thus ”Tie-dye” and ”Zardozi” inspire the
designer to integrate traditional Chinese cultural elements into the design of
hair dryer to increase cultural connotation. The second design (Figure 11) is
functional and inspired by ”Hairstyle” and ”Comb” in the design inspiration
link. The idea is to design the replaceable hair dryer nozzle with the
features of ”Comb” so that users can comb their hair conveniently while drying
the hair without searching it in a hurry.

##  6 Conclusion

In this research, firstly, a semantic network for design innovation is
constructed. Wikipedia is applied as the data source for the semantic network.
During the construction, the Wikipedia items are regarded as the nodes, the
interlinks between the items on the same page are regarded as the directly
connected relationship (edges) between nodes. The evaluation result indicate
that the network contains information from a wide range of fields and expands
the data to a new boundary. Secondly, instead of simply one type of weight, a
combined weight is introduced for the relationship in the semantic network.
The combined weight fuses the statistical relationship and semantic
relationship which better captures the implicit connection between concepts
for design innovation. Four algorithms are further developed to retrieve
relevant knowledge concepts and relationships with different levels and
manners. Thirdly, the constructed semantic network for design innovation is
further developed as a tool, called WikiLink. An evaluation and demonstration
for WikiLink are conducted subsequently. Compared with other benchmarks, with
the fusion of semantic meaning weight and statistical weight, WikiLink can
well balance the breadth and depth in exploring knowledge for design
innovation. A design case is conducted to demonstrate the process of how
WikiLink can facilitate idea generation. The results indicate that WikiLink
can serve as a design ideation tool for design innovation.

The study leaves some space for future research though it provides a
functional panel for practical use. The weight strength fusing two types of
weight is one of the main contributions in this research, but it only shows
the numerical value and lacks of explicit semantic meaning describing the
relationship between two concepts. Thus a semantic description is expected to
be added to the edges in WikiLink and provide richer information for design
innovation. Besides, the network visualization of WikiLink is currently
designed on a two-dimensional scale, which might cause information explosion
when the retrieved network keeps growing. A three-dimensional scale network
along with other information visualization techniques could be a solution and
provide a more dynamic way for users to explore information and obtain
inspiration more effectively.

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