An Art of Review on Conceptual based Information Retrieval

2002

In the Web search, since users' queries usually consist of only a few words, it is hard to identify their information needs. To solve this problem, many approaches have been tried to expand initial queries and to reweight the terms in the expanded queries using users' relevance judgments. Although relevance feedback is most effective when relevance information about retrieved documents is provided by users, it is not a fully automatic method. Another solution is to use correlated terms for query expansion. The main problem with this approach is how to construct the term-term correlations that can be used effectively to improve retrieval performance. In this study, we try to construct query concepts that denote users' information needs from a document space, rather than to reformulate initial queries using the term correlations and/or users' relevance feedback. To make query concepts, we extract features from each document, and cluster the features into primitive concepts that are used to form query concepts. Experiments are performed on a TREC collection.

2003

As the number of available Web pages grows, users experience increasing difficulty finding documents relevant to their interests. One of the underlying reasons for this is that most search engines find matches based on keywords, regardless of their meanings. To provide the user with more useful information, we need a system that includes information about the conceptual frame of the queries as well as its keywords. This is the goal of KeyConcept, a search engine that retrieves documents based on a combination of keyword and conceptual matching. Documents are automatically classified to determine the concepts to which they belong. Query concepts are determined automatically from a small description of the query or explicitly entered by the user. This paper describes the system architecture, the training of the classifier, and the results of our experiments evaluating system performance. KeyConcept is shown to significantly improve search result precision through its use of conceptual retrieval.

Traditional information retrieval systems rely on keywords to index documents and queries. In such systems, documents are retrieved based on the number of shared keywords with the query. This lexical-focused retrieval leads to inaccurate and incomplete results when different keywords are used to describe the documents and queries. Semantic-focused retrieval approaches attempt to overcome this problem by relying on concepts rather than on keywords to indexing and retrieval. The goal is to retrieve documents that are semantically relevant to a given user query. This paper addresses this issue by proposing a solution at the indexing level. More precisely, we propose a novel approach for semantic indexing based on concepts identified from a linguistic resource. In particular, our approach relies on the joint use of WordNet and WordNetDomains lexical databases for concept identification. Furthermore, we propose a semantic-based concept weighting scheme that relies on a novel definition of concept centrality. The resulting system is evaluated on the TIME test collection. Experimental results show the effectiveness of our proposition over traditional IR approaches.

In this paper we present a novel approach, called Concept Search, which extends syntactic search, i.e., search based on the computation of string similarity between words, with semantic search, i.e., search based on the computation of semantic relations between concepts. The key idea of Concept Search is to operate on complex concepts and to maximally exploit the semantic information available, reducing to syntactic search only when necessary, i.e., when no semantic information is available. The experimental results show that Concept Search performs at least as well as syntactic search, improving the quality of results as a function of the amount of available semantics.

ACM Transactions on Information Systems, 2011

Information retrieval systems traditionally rely on textual keywords to index and retrieve documents. Keyword-based retrieval may return inaccurate and incomplete results when different keywords are used to describe the same concept in the documents and in the queries. Furthermore, the relationship between these related keywords may be semantic rather than syntactic, and capturing it thus requires access to comprehensive human world knowledge. Concept-based retrieval methods have attempted to tackle these difficulties by using manually built thesauri, by relying on term cooccurrence data, or by extracting latent word relationships and concepts from a corpus. In this article we introduce a new concept-based retrieval approach based on Explicit Semantic Analysis (ESA), a recently proposed method that augments keyword-based text representation with concept-based features, automatically extracted from massive human knowledge repositories such as Wikipedia. Our approach generates new tex...

Journal of Medical Systems, 2000

This is the third in the series of the articles on an application of the systems analytic approach to evaluation of information retrieval (IR). Previously terminological and evaluation problems associated with IR were identified, and it was proposed that the systems analytic approach can provide solutions to these problems. Here, after the general discussion of a systems approach, different attempts to introduce this approach into IR are presented and critiqued: modelling of IR, identifying boundaries of a system under evaluation, identifying variables under investigation; and creating evaluation frameworks. On the basis of the critique of this work, the authors present their proposal for a solution: first the IR components, their boundaries and relationships are identified, and then the two models of IR are introduced. One of the models addresses components of the IR process and relationships between them, the second one is the process model of IR.

Journal of Intelligent Information Systems, 1995

In this paper, we advance a technique to develop a user pro le for information retrieval through knowledge acquisition techniques. The pro le bridges the discrepancy between user-expressed keywords and system-recognizable index terms. The approach presented in this paper is based on the application of personal construct theory to determine a user's vocabulary and his/her view of di erent documents in a training set. The elicited knowledge is used to develop a model for each phrase/concept given by the user by employing machine learning techniques.

1999

Most engines used for searching information resources via the Internet employ the Boolean Retrieval Model. Two main drawbacks of this model are that users have difficulty to precisely formulate their concept (or, topic) of interest using Boolean logic and the resulting output is not ranked. We propose to address both these problems by employing a Concept-based Retrieval Model, where a concept is defined by a set of production rules and the rule-base is represented as a rule-base tree. Features of a prototype developed at USL, referred to as the Concept-Set Structuring System (CS 3 ), which includes a graphical interface for defining and refining rule-base trees and for converting them into equivalent sets of conjunctions, called Minimal Term Sets (MTSs), are described. By submitting MTSs generated for a concept to an existing search engine and by reordering the returned results according to the importance of MTSs they satisfy, the CS 3 prototype enhances the capabilities of the underlying search engine. Results that demonstrate the use of the prototype, coupled with DOE Information-Bridge, will be presented.

Information Sciences Letters, 2021

Nowadays, the traditional information retrieval (IR) is inadequate for the user who requires precise results. Hence, the importance of the semantic IR arises. It is very important to move from the level of ambiguous terms to that of well-specified concepts in the indexing phase to enrich the search process. To handle the problems of semantic ambiguity of indexed terms as well as the uncertainty and imprecision inherent in the information retrieval process, a semantic indexing approach was proposed for a better document representation. It is based on indexing the associated synsets with document terms that are identified by mapping on the WordNet ontology. These synsets are defined following a term disambiguation process (WSD). The key to the proposed system is a weighting model which calculates the importance of each index item considering many factors that improve the performance of the information retrieval system. Proposed conceptual weight is based on local and global integrality, degree of re-homogenization, and degree of specificity of the concept. A corrector parameter to reduce the impact of errors in WSD process is included. The experimental evaluation of the introduced semantic IR model shows very satisfactory results compared to well-cited benchmarks.

2006

This paper presents an information retrieval methodology which uses Formal Concept Analysis in conjunction with semantics to provide contextual answers to Web queries The conceptual context defined in this article can be global -i.e. stable-or instantaneous-i.e. bounded by the global context. Our methodology consists first in a pre-treatment providing the global conceptual context and then in an online contextual processing of users' requests, associated to an instantaneous context. Our information retrieval process is illustrated through experimentation results in the tourism domain. One interest of our approach is to perform a more relevant and refined information retrieval, closer to the users' expectation.