=Paper=
{{Paper
|id=Vol-294/paper-7
|storemode=property
|title=Ontology-based Semantic Relevance Measure
|pdfUrl=https://ceur-ws.org/Vol-294/paper07.pdf
|volume=Vol-294
|authors=Sang Keun Rhee,Jihye Lee and Myon-Woong Par
|dblpUrl=https://dblp.org/rec/conf/semweb/RheeLP07
}}
==Ontology-based Semantic Relevance Measure==
https://ceur-ws.org/Vol-294/paper07.pdf
Ontology-based Semantic Relevance Measure
Sang Keun Rhee, Jihye Lee, and Myon-Woong Park
Intelligence and Interaction Research Center,
Korea Institute of Science and Technology,
39-1 Hawolgok-dong, Seongbuk-gu, Seoul, 136-791, Korea
{greyrhee,bluesea,myon}@kist.re.kr
Abstract. Semantic relevance among information resources can play
an effective role in information retrieval, and there are several different
approaches to measure semantic similarities. However, they are usually
limited measuring similarities among structured concepts, and some also
considers the similarities between individual resources but they are lim-
ited to to textual documents. This paper aims to extend the similarity
to relevance so that even the relations between completely different re-
sources can also be considered, and explore the possibility of measuring
such semantic relevance not limited to a specific structure. The method
presented in this paper is a novel measure for semantic relevance among
resources that are organised within an ontological structure.
Key words: Semantic Relevance, Ontology, Information Search, Knowl-
edge Sharing.
1 Introduction
There have long been various researches to enhance information retrieval and
knowledge sharing, and ontology-based semantic technology opened a new pos-
sibility of improvement. Upon retrieving information, usually a list of results is
provided based on the given query. However, the search result is generally con-
centrating on the relevance between the query and each resource, and to find
other resources that are similar or relevant to a certain resource, users need to
go back to the previous results list or initiate another query. Since each resource
representing different knowledge is semantically connected to each other, mea-
suring and utilising the semantic relevance between those resources can provide
more flexible range of results which are not strictly limited to the given query.
Such measure can also be used to provide a guideline or recommendation on
what other resources are related and needed to be considered when the user is
examining a certain resource.
In this paper, we propose a methodology to measure semantic relevance be-
tween resources, based on ontological representation. First, generic aspects of
semantic similarity measure are briefly discussed with some existing method-
ologies. Then, an ontology-based methodology for measuring semantic relevance
between resources is proposed and described in detail, and it is concluded with
a discussion.
2 Sang Keun Rhee, Jihye Lee, and Myon-Woong Park
2 Generic Semantic Similarity Measure
Semantic similarity is the likeness of the semantic content among documents or
the meaning among a set of structured terms, and there are several methods
to discover the semantic similarities among concepts or resources. One of the
oldest but still effective approaches is text-based technique, including vector-
based model such as latent semantic analysis[1]. Also, there are several researches
using direct relations between objects, based on either tree structure[3] or more
generic and complex graph structure[2]. Their approach is typically (1) edge-
based[4] that considers the number of edges between nodes but does not rate
different characteristics for each edges, (2) node-based[4][3] that is more accurate
but needs more computation for the content of each concept, or (3) hybrid[5][6]
which merges both edge-based and node-based methods.
3 Semantic Relevance Measure
The method for semantic relevance measure proposed in this paper aims to cal-
culate the relevance between resources as numeric values based on the semantic
relations between concepts.
3.1 Ontological Representation
To measure semantic relevance between resources, all resources should be se-
mantically structured and represented. An ontology would be the ideal repre-
sentation for this purpose. Such ontology can be constructed in two different
ways, depending on the purpose of the system and the type of resources being
managed. Firstly, the ontology can be designed such that some classes contain
resources as their instances whereas other classes represent other semantic con-
cepts. For example, an ontology can have a class called Documents containing
all the resources as documents form, and another class called Person contain-
ing personal profiles which are not regarded as resources hence not the target
objects for searching but used as additional semantic information to produce,
for instance, personalised search results. Secondly, all instances in all classes can
represent resources. This is appropriate when everything described within the
ontology is regarded as a resource. In this case, for above example, personal
profiles are also regarded as resources hence they are also the target objects for
searching.
Once the fundamental structure of the ontology - the concepts, attributes
and properties - is defined, a set of rules should be defined to specify additional
semantic information. The rules can be defined in the usual way, but for our
methodology, some additional rules may be required to concentrate on represent-
ing the meaningful semantic relations between resources. As a simple example,
suppose we have an ontology with a class Resource containing all the resources
as its instances and another class Topic containing the topic hierarchy of topics
as its instances(Fig. 1). Here, one may define a rule between a resource R1 which
Ontology-based Semantic Relevance Measure 3
Fig. 1. A simple example ontology.
Fig. 2. An example rule.
is about Topic T1 and another resource R2 which is about Topic T2 , a subtopic
of T1 (Fig. 2). This example rule can be defined in F-Logic[7] as following:
R2 [isSubT opicResourceOf R1 ] ←
R1 : Resource ∧ R2 : Resource ∧ T1 : T opic ∧ T2 : T opic
∧ R1 [isAbout T1 ] ∧ R2 [isAbout T2 ] ∧ T1 [hasSubT opic T2 ]
The fundamental classes(concepts), properties(attributes and relations) and
rules should be defined by domain experts, and the resources(instances) can be
created by either a limited number of experts or the users themselves depending
on the purpose and functionalities of the system.
3.2 Semantic Distance between Concepts
Once the ontology is defined, the next step is assigning a numeric value for each
relation or rule, representing the semantic distance between concepts(classes).
The semantic distance is the opposite term of semantic relevance - in other words,
the higher the value is, the less relevant the given two concepts are. Depending
on the structure of the ontology, the resources can be stored in a single class,
multiple classes or all classes may contain resources. In any case, the ultimate
concern at this stage is defining the semantic distance among classes containing
4 Sang Keun Rhee, Jihye Lee, and Myon-Woong Park
resources. Distances between other class can also be defined, but it would only
be an intermediate step to assign the right value to rules.
Deciding the right value for each relation or rule can be tricky. The values can
instantiated manually by domain experts, and some historical evaluation meth-
ods can be applied to update those values. Also, there are several researches
on detecting the semantic similarity between concepts in both tree-based and
graph-based structure, some of which are briefly mentioned in Section 2. How-
ever, the detailed methods for defining semantic distance between concepts are
not the scope of this paper hence not listed here.
3.3 Semantic Relevance Measure between Resources
To infer the semantic relevance between resources(instances), we propose a graph-
based relevance measure to exploit various relations and rules between resources.
The final semantic relevance value can be determined via the following five steps:
Graph Creation The ontology is interpreted into a directed graph G = (V, E)
where:
– V is a set of nodes, representing all resources;
– E is a set of edges, representing relations and rules between resources.
Since additional rules as well as relations are interpreted as edges, it is possible
that multiple edges e1 (x, y), e2 (x, y), . . . , en (x, y) are created between two nodes
x ∈ V and y ∈ V . If there is an edge whose final node is its initial node, such
edge is ignored hence not included in the graph.
∀x ∈ V : e(x, x) ∈
/E (1)
For each edge ei (x, y) ∈ E where x, y ∈ V , a numeric value dei (x,y) is assigned
as its label to represent the distance from node x to node y. This value is derived
from the distance between concepts(Section 3.2). The relevance value between
two nodes is the inverse of the distance value.
1
relevance = (2)
distance
Merging Multiple Edges Once the graph is constructed, the next step is to
merge multiple edges into a single edge, where there are multiple edges with
same direction between two adjacent nodes. For two adjacent nodes x, y ∈ V ,
edges e1 (x, y), e2 (x, y), . . . , en (x, y) ∈ E from node x to node y, and the distance
value dei (x,y) for edge ei (x, y) where 1 ≤ i ≤ n, the semantic relevance value Rxy
is defined as follows:
Xn
1
Rxy = (3)
d
i=1 ei (x,y)
Ontology-based Semantic Relevance Measure 5
The distance value de0 (x,y) of the merged edge e0 (x, y) is:
1
de0 (x,y) = (4)
Rxy
By applying this edge merging algorithm above, a new graph with no multiple
edges with same direction between two adjacent nodes can be obtained. Fig. 3
shows an example illustration of merging multiple edges.
Fig. 3. An example illustration of merging multiple edges. The numbers in parentheses
represents the distance values.
Paths Finding For two non-adjacent nodes, there can be multiple paths. Here,
a path is a valid one if and only if no node is visited more than once(i.e. simple
path). A simple graph traversal algorithm can be applied to obtain the list of
paths for each pair of nodes.
Single Path Distance In a weighted graph, the weight of a path is the sum of
the weights of each visited edges, and this can be applied to calculate the distance
of a single path in our graph. Therefore, for a path P (a1 an ) which visits n nodes
a1 , a2 , . . . , an ∈ V , the distance of the path DP (a1 an ) can be defined as follows:
X
n−1
DP (a1 an ) = de0 (ak ak+1 ) (5)
k=1
However, it is found that it can produce undesirable results, and the desired
semantic distance value of a path should be greater than the simple sum of the
edge distances. More precisely, the value added should become greater every
time it passes another node. Therefore, the above equation is replaced by the
following one with a value k.
X
n−1
DP (a1 an ) = (k × de0 (ak ak+1 ) ) (6)
k=1
6 Sang Keun Rhee, Jihye Lee, and Myon-Woong Park
Merging Multiple Paths The last step is to merge multiple paths between
two nodes so that the final relation value can be obtained. This method is fun-
damentally the same as merging multiple edges.
For n paths P1 , P2 , . . . , Pn between two non-adjacent nodes x, y ∈ V , the
semantic relation value Rxy is as follows:
X
n
1
Rxy = (7)
i=1
DPk (xy)
4 Discussion
In this paper, we presented a novel measure of semantic relevance between re-
sources based on ontological representation. Unlike previous approaches, this
measures the semantic relevance between individual resources rather than con-
cepts, and it also utilises all the different meaningful relations and rules for its
calculation. It can be readily applied to complex ontologies where each resource
have multiple different properties and their values so that many services can
become possible or improved especially in information retrieval or recommender
systems. This approach can be particularly useful in engineering design area
where knowledge reuse and flexible knowledge searching is vital, but it can also
be applied in many other areas such as knowledge portal, e-learning, etc.
Acknowledgments. This work was supported by the Korea Institute of Sci-
ence and Technology(KIST) with the project IRS(Intelligent Responsive Space),
which is a part of TSI(Tangible Space Initiative) project.
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