DISSERTATION ABSTRACTIntelligent Techniques for Effective Information Retrieval(A Conceptual Graph Based Approach)
Tanveer J Siddiqui
J.K. Institute of Applied Physics & Technology
Department of Electronics & Communication
University of Allahabad
Allahabadtjs@jkinstitute.org

Abstract

With the explosive growth of information, it is becoming increasingly difficult to retrieve the relevant
documents with statistical means only. This begets new challenges to IR community and motivates
researchers to look for intelligent Information Retrieval (IR) systems that search and/or filter
information automatically based on some higher level of understanding are required. This higher level ofunderstanding can only be achieved through processing of text based on semantics, which is not possible
by considering a document as a “bag of words”. We make a humble effort in this direction by
investigating techniques that attempt to utilize semantics to improve effectiveness in IR. The hypothesisis that with an improved representation of documents and by incorporating limited semantic knowledge,
it is possible to improve the effectiveness of an IR system.
We propose the use of Conceptual Graph (CG) formalism for representing text. The level of semantic
details to be captured within conceptual graphs and the type of relations used to relate concepts dependon the nature of the application and the domain in which it is to be used. The objective of any IR system
is to classify documents as relevant and non-relevant with respect to a standing query. A detailed andaccurate semantic analysis is not required for this task. This fact distinguishes IR from other natural
language processing related tasks such as machine translation, question answering, text summarization,etc. Further, the amount of text with which an IR system has to deal with is so huge that it becomes
impractical to carry out such analysis. So we take a middle ground by investigating techniques that makeuse of limited semantic knowledge that can be easily exacted from the text in the CG representation. The
techniques investigated in this thesis make use of CG-based model in conjunction with the statistical
vector space model. The two models thus complement each other allowing us to take the benefits of thelong and established research efforts in the statistical models and versatility of semantic models.
The statistical model used in the experimental work is the most widely followed vector space model. We
investigate performance of this model with different combinations of term weighting schemes for
documents and queries, yielding seven different retrieval models, on the CACM-3204 collection. The
best performing case, we achieved was with ‘augmented normalized tem frequency’ and ‘raw term
frequency with length normalization’ components for document and query terms respectively, whereinverse document frequency component is incorporated in both. The eleven-point average precision
score (11-AvgP) score for the base model was 0.2961 (averaged over 64 queries).
To get CG-based representation of a document, we build conceptual graph for each sentence in thedocument. A small subset of basic relations has been used for constructing conceptual graphs. Theserelations have been extracted based on syntactic patterns. A manually constructed partial concept typehierarchy has been used. This restricts us from performing large-scale experimentation automatically. AsACM SIGIR ForumVol. 40 No. 2 December 2006an alternative to this, we maintain a set of terms replaceable for each other and use some simple
heuristics to capture structural variations. Queries undergo more detailed processing as compared todocuments. The conceptual graphs obtained thus are stored in the form of triplets. The CG
representation used is easily scalable and lends itself for fine-tuning easily according to the needs ofmany different NLP applications.
This thesis argues that conceptual graphs can be used as a precision tool. A hybrid information retrieval
model is proposed to back up this argument. This model is essentially a two-stage retrieval model thatfirst uses vector space model to quickly downsize the document collection and then uses CG-basedmodel to do the final ranking. A manually constructed partial concept type hierarchy has been used. The
experimental investigations have been made on CACM-3204, a collection called CGDOC, which is asmall document collection designed by us and on the top ten results retrieved from LYCOS searchengine. A significant improvement over the baseline performance has been observed for a subset ofCACM queries.
Next, a query expansion technique based on relevance feedback has been presented. This techniqueattempts to reduce the ‘query drift’ through the use of conceptual graph based representation of
document in the expansion process. Two relevance feedback strategies have been proposed andevaluated. Both the strategies resulted in a significant improvement over the baseline performance for asubset of CACM queries.
The motivation for the next technique evaluated by us lies in our belief that relation matching can beused to improve retrieval effectiveness. The roots of this belief lie in the fact that the semantics of terms
can only be understood in the context they are being used. Relation matching provides a means tocapture this context. To investigate the effectiveness of our technique we propose a retrieval model that
integrates relation and keyword matching. There can be no better way to capture the relationships thanthe use of conceptual graphs and hence the CG based model, with certain enhancements andmodification, remains an integral part of this work. A set of replaceable terms instead of type hierarchyhas been used. Some simple heuristics along with the transitivity of relations have been used to capture
structural variations. For identifying relational similarity between the query and the document, wepropose new CG similarity measures. These measures have been used in the retrieval process along withthe usual term similarity measures. The results obtained have shown a significant improvement (7.37%)
in retrieval performance.
The results of this thesis demonstrate that by improving document/query representation and byincorporating more information from within the document and the query into the retrieval process, theeffectiveness of the information retrieval is enhanced. The major findings can be summarizes as:••••••

The use of conceptual graphs as a knowledge representation formalism offers many advantagesin IR. The general model, presented in this thesis, for creating conceptual graph from naturallanguage text can be easily extended to capture more semantics if domain specific information isavailable.
Two stage retrieval models can be quite effective in improving retrieval performance.
Relation matching can be integrated successfully with existing statistical retrieval model toimprove retrieval effectiveness.
New CG similarity measures proposed in the thesis offer flexibility in defining textual units thatmake them useful for many different applications.
Simple heuristics can be used to capture structural variations.
Queries, instead of documents can be targeted for semantic interpretation.

ACM SIGIR Forum

Vol. 40 No. 2 December 2006