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Combining ontological profiles with context
in information retrieval
Date：2012.03.06
Reporter : Hong Ji Wei
Auther : Geir Solskinnsbakk and Jon Atle Gulla
出處: Data & Knowledge Engineering,vol 69, no 3, 2010, pp 251–260
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Outline
1
Introduction
2
Related Work
3
Ontological profiles
4
Construction of ontological profiles
35
Experiment
46
Context
37
Conclusions
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Introduction
An ontology is a formal conceptualization of a
domain,specifying the concepts of the domain
and the relations between them.
大體而言，本體論的兩大重點：
1.以已經定義的字彙名詞(vocabulary of terms)
來描述以存在的實體(entity)
2.以一定規格(specification)表示出這些實體間
的關係(relationship)與存在的意義(meaning)
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Early semantic search engines tried to use
ontology concepts and structures as controlled
search vocabularies,but this was unpractical
both functionally and usability perspective.
Ontological profile=Ontology and concept
characterizations.
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Related Work
Su describes in a method for ontology mapping,
based on an extension of the ontology.
Tomassen describes an ontology driven
information retrieval system based on extending
ontologies.
Sieg et al describe an approach for
representing user context for search.
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Ontological profiles
An ontological profile is an extension of a
domain ontology.
The ontology is extended with semantically
related terms that are added as vectors for
each of the concepts of the ontology.
The concept vector can be viewed as an
extended semantic characterization of the
concept, reflecting the semantics of the concept
in the document collection.
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therefore argue that the ontological profile may
be better suited:
(i) a specific document collection
(ii) the vocabulary in the documents
(iii)the use of the ontology concepts in the
documents.
EX:
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Construction of ontological profiles
Construction of the ontological profile is based
on three important aspects:
1. Statistics: Apply statistical techniques to the
document collection by counting the frequency of
the terms in the documents
2. Linguistics:the terms of the documents, namely
stemming, to collapse certain semantically similar
terms into a single form.
3. Proximity:Apply a proximity measure to the cooccurrence of concept names and terms
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Experiment
The process consists of two steps:
(i) query interpretation
1. Simple query interpretation
•
This is the most basic strategy for query interpretation
2. Best match query interpretation
•
•
In contrast to the simple query interpretation
During interpretation of the query we try to recognize
the connection between the query terms by mapping
the terms collectively to a single concept
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3. Cosine similarity query interpretation
• we are not able to directly recognize the relation from the
query terms.
• Try to recognize relations between the concepts that the
query terms map to via the ontological profile.
4. Ontology structure query interpretation
• The measure we use is based on the distance between
the concepts in terms of relations.
• by generating a graph representation of the ontology, in
which nodes represent concepts and edges represent
relations.(to find the distance for each pair of concepts)
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(ii)Query expansion
 The query expansion process reformulates the
original query by adding semantically related terms
to the query.
 The weight of the original query terms are boosted
to reflect their importance.
 The motivation behind adding these terms is to
remove some of the noise introduced by the
increased set of query terms.
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Experimental data and results
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Context
Context is the set of suitable environmental
states and settings concerning a user.
Attributes are context if they are non-essential
(for tasks).
We can thus conclude that only non-essential
information used to enhance the applications
computations can be considered context.
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1. Search context
 Definition. Search context is the context of a user
posting queries to a search application.
We can partition the systems knowledge about
the user into two separate parts:
 The first is of course the query
 The second part is additional knowledge which can
characterize the situation of the user(i.e., the
context.)
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2. Contextualized ontological search
Ontology Profiles



FQV   UQn   CQun (    1)
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Conclusions
The ontological profile describes each concept
as a vector of terms with weights describing the
strength of the relation between them.
We defined search context and discussed how
contextual search may be incorporated into our
semantic search approach
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