Search (18 results, page 1 of 1)

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Abstract

We present a deductive data model for concept-based query expansion. It is based on three abstraction levels: the conceptual, linguistic and occurrence levels. Concepts and relationships among them are represented at the conceptual level. The expression level represents natural language expressions for concepts. Each expression has one or more matching models at the occurrence level. Each model specifies the matching of the expression in database indices built in varying ways. The data model supports a concept-based query expansion and formulation tool, the ExpansionTool, for environments providing heterogeneous IR systems. Expansion is controlled by adjustable matching reliability.

Source

Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR '96), Zürich, Switzerland, August 18-22, 1996. Eds.: H.P. Frei et al

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Abstract

Describes a deductive data model based on 3 abstraction levels for representing vocabularies for information retrieval: conceptual level; expression level; and occurrence level. The proposed data model can be used for the representation and navigation of indexing and retrieval thesauri and as a vocabulary source for concept based query expansion in heterogeneous retrieval environments

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Abstract

Reports an evaluation of 3 methods for the expansion of natural language queries in ranked output retrieval systems. The methods are based on term co-occurrence data, on Soundex codes, and on a string similarity measure. Searches for 110 queries in a data base of 26.280 titles and abstracts suggest that there is no significant difference in retrieval effectiveness between any of these methods and unexpanded searches

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Abstract

Modern fact databses contain abundant data classified through several classifications. Typically, users msut consult these classifications in separate manuals or files, thus making their effective use difficult. Contemporary database systems do little support deductive use of classifications. In this study we show how deductive data management techniques can be applied to the utilization of data value classifications. Computation of transitive class relationships is of primary importance here. We define a representation of classifications which supports transitive computation and present an operation-oriented deductive query language tailored for classification-based deductive information retrieval. The operations of this language are on the same abstraction level as relational algebra operations and can be integrated with these to form a powerful and flexible query language for deductive information retrieval. We define the integration of these operations and demonstrate the usefulness of the language in terms of several sample queries

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Abstract

The Okapi system has been used in a series of experiments on the TREC collections, investiganting probabilistic methods, relevance feedback, and query expansion, and interaction issues. Some new probabilistic models have been developed, resulting in simple weigthing functions that take account of document length and within document and within query term frequency. All have been shown to be beneficial when based on large quantities of relevance data as in the routing task. Interaction issues are much more difficult to evaluate in the TREC framework, and no benefits have yet been demonstrated from feedback based on small numbers of 'relevant' items identified by intermediary searchers

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Abstract

An automatic query expansion (AQE) facility in anonline catalogue was evaluated in an operational library setting. The OKAPI experimental system had other features including: ranked output 'best match' keyword searching, automatic stemming, spelling normalisation and cross referencing as well as relevance feedback. A combination of transaction log analysis, search replays, questionnaires and interviews was used for data collection. Findings show that contrary to previous results, AQE was beneficial in a substantial number of searches. Use intentions, the effectiveness of the 'best match' search and user interaction were identified as the main factors affecting the take-up of the query expansion facility

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Abstract

To provide advice for online searching about term selection and query expansion, an intermediary expert system should indicate a terminological knowledge structure. Terminological attributes could provide the foundation of a knowledge base, and knowledge acquisition could rely on knowledge base techniques coupled with statistical techniques. The strategies of expert searchers would provide 1 source of knowledge. The knowledge structure would include 3 constructs for each term: frequency data, a hedge, and a position in a classification scheme. Switching vocabularies could provide a meta-scheme and facilitate the interoperability of databases in similar subjects. To develop such knowledge structure, research should focus on terminological attributes, word and phrase disambiguation, automated text processing, and the role of thesauri and classification schemes in indexing and retrieval. It should develop techniques that combine knowledge base and statistical methods and that consider user preferences

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Abstract

Shows how probabilistic information retrieval based on document components may be implemented as a feedforward (feedbackward) artificial neural network. The network supports adaptation of connection weights as well as the growing of new edges between queries and terms based on user relevance feedback data for training, and it reflects query modification and expansion in information retrieval. A learning rule is applied that can also be viewed as supporting sequential learning using a harmonic sequence learning rate. Experimental results with 4 standard small collections and a large Wall Street Journal collection show that small query expansion levels of about 30 terms can achieve most of the gains at the low-recall high-precision region, while larger expansion levels continue to provide gains at the high-recall low-precision region of a precision recall curve

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Abstract

Describes the shortcomings of search engines for the WWW comparing their current capabilities to those of the first generation CD-ROM products. Some allow phrase searching and most are improving their Boolean searching. Few allow truncation, wild cards or nested logic. They are stateless, losing previous search criteria. Unlike the indexing and classification systems for today's CD-ROMs, those for Web pages are random, unstructured and of variable quality. Considers that at best Web search engines can only offer free text searching. Discusses whether automatic data classification systems such as Infoseek Ultra can overcome the haphazard nature of the Web with neural network technology, and whether Boolean search techniques may be redundant when replaced by technology such as the Euroferret search engine. However, artificial intelligence is rarely successful on huge, varied databases. Relevance ranking and automatic query expansion still use the same simple inverted indexes. Most Web search engines do nothing more than word counting. Further complications arise with foreign languages

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Abstract

Document retrieval is a highly interactive process dealing with large amounts of information. Visual representations can provide both a means for managing the complexity of large information structures and an interface style well suited to interactive manipulation. The system we have designed utilizes visually displayed graphic structures and a direct manipulation interface style to supply an integrated environment for retrieval. A common visually displayed network structure is used for query, document content, and term relations. A query can be modified through direct manipulation of its visual form by incorporating terms from any other information structure the system displays. An associative thesaurus of terms and an inter-document network provide information about a document collection that can complement other retrieval aids. Visualization of these large data structures makes use of fisheye views and overview diagrams to help overcome some of the inherent difficulties of orientation and navigation in large information structures.

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Abstract

Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text (Landauer and Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words and sets of words to each other. The adequacy of LSA's reflection of human knowledge has been established in a variety of ways. For example, its scores overlap those of humans on standard vocabulary and subject matter tests; it mimics human word sorting and category judgments; it simulates word-word and passage-word lexical priming data; and as reported in 3 following articles in this issue, it accurately estimates passage coherence, learnability of passages by individual students, and the quality and quantity of knowledge contained in an essay.

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Abstract

The second half of a 'before and after' study to evaluate the impact of an online catalogue on subject searching behaviour is reported. A holistic approach is adopted encompassing both catalogue use and browsing at the shelves for catalogue users and non-users. Verbal and non-verbal data were elicited from searchers using a combined methodology including talk-aloud technique, observation and a screen logging facility. An extensive qualitative analysis was carried out correlating expressed topics, search formulation strategies and documents retrieved at the shelves. The online catalogue environment does not appear to have increased the extent of subject searching nor the use of the bibliographic tool. The manual PRECIS index supported a contextual approach for broad and more interactive search formulations whereas the OPAC encouraged a matching approach and narrow formulations with fewer but user generated formulations. The success rate of the online catalogue was slightly better than that of the manual tools but fewer items were retrieved at the shelves. Non-users of the bibliographic tools seemed to be just as successful. To improve retrieval effectiveness it is suggested that online catalogues should cater for both matching and contextual approaches to searching. Recent research indicates that a more interactive process could be promoted by providing query expansion through a combination of searching aids for matching, for search formulation assistance and for structured contextual retrieval

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Date

30. 3.2001 13:35:22

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Source

Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon

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Date

1. 8.1996 22:08:06

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Source

Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society: July 22 - 25, 1995, University of Pittsburgh / ed. by Johanna D. Moore and Jill Fain Lehmann

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Abstract

Context plays an important role in a number of domains where reasoning intervenes as in understanding, interpretation, diagnosis, etc. The reason is that reasoning activities heavily rely on a background (or experience) that is generally not made explicit and that gives a contextual dimension to knowledge. On the Web in December 1996, AItaVista gave more than 710000 pages containing the word context, when concept gives only 639000 references. A clear definition of this word stays to be found. There are several formal definitions of this concept (references are given in Brézillon, 1996): a set of preferences and/or beliefs, an infinite and only partially known collection of assumptions, a list of attributes, the product of an interpretation, possible worlds, assumptions under which a statement is true or false. One faces the same situation at the programming level: a collection of context schemas; a path in information retrieval; slots in object-oriented languages; a special, buffer-like data structure; a window on the screen, buttons which are functional customisable and shareable; an interpreter which controls the system's activity; the characteristics of the situation and the goals of the knowledge use; or entities (things or events) related in a certain way that permits to listen what is said and what is not said. Context is often assimilated at a set of restrictions (e.g., preconditions) that limit access to parts of the applications. The first works considering context explicitly are in Natural Language. Researchers in this domain focus on the linguistic context, sometimes associated with other types of contexts as: semantic context, cognitive context, physical and perceptual context, and social context (Bunt, 1997).

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Date

22. 2.1996 13:14:10