In conventional information retrieval Boolean combinations of index terms are used to formulate the users'' information requests. While any document is in principle retrievable by a Boolean query, the amount of output obtainable by Boolean processing is difficult to control, and the retrieved items are not ranked in any presumed order of importance to the user population. In the vector processing model of retrieval, the retrieved items are easily ranked in decreasing order of the query-record similarity, but the queries themselves are unstructured and expressed as simple sets of weighted index terms. A new, extended Boolean information retrieval system is introduced which is intermediate between the Boolean system of query processing and the vector processing model. The query structure inherent in the Boolean system is preserved, while at the same time weighted terms may be incorporated into both queries and stored documents; the retrieved output can also be ranked in strict similarity order with the user queries. A conventional retrieval system can be modified to make use of the extended system. Laboratory tests indicate that the extended system produces better retrieval output than either the Boolean or the vector processing systems.

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Extended Boolean information retrieval

The objective of the paper is to amalgamate theories of text retrieval from various research traditions into a cognitive theory for information retrieval interaction. Set in a cognitive framework, the paper outlines the concept of polyrepresentation applied to both the user's cognitive space and the information space of IR systems. The concept seeks to represent the current user's information need, problem state, and domain work task or interest in a structure of causality. Further, it implies that we should apply different methods of representation and a variety of IR techniques of different cognitive and functional origin simultaneously to each semantic full‐text entity in the information space. The cognitive differences imply that by applying cognitive overlaps of information objects, originating from different interpretations of such objects through time and by type, the degree of uncertainty inherent in IR is decreased. Polyrepresentation and the use of cognitive overlaps are associated with, but not identical to, data fusion in IR. By explicitly incorporating all the cognitive structures participating in the interactive communication processes during IR, the cognitive theory provides a comprehensive view of these processes. It encompasses the ad hoc theories of text retrieval and IR techniques hitherto developed in mainstream retrieval research. It has elements in common with van Rijsbergen and Lalmas' logical uncertainty theory and may be regarded as compatible with that conception of IR. Epistemologically speaking, the theory views IR interaction as processes of cognition, potentially occurring in all the information processing components of IR, that may be applied, in particular, to the user in a situational context. The theory draws upon basic empirical results from information seeking investigations in the operational online environment, and from mainstream IR research on partial matching techniques and relevance feedback. By viewing users, source systems, intermediary mechanisms and information in a global context, the cognitive perspective attempts a comprehensive understanding of essential IR phenomena and concepts, such as the nature of information needs, cognitive inconsistency and retrieval overlaps, logical uncertainty, the concept of ‘document’, relevance measures and experimental settings. An inescapable consequence of this approach is to rely more on sociological and psychological investigative methods when evaluating systems and to view relevance in IR as situational, relative, partial, differentiated and non‐linear. The lack of consistency among authors, indexers, evaluators or users is of an identical cognitive nature. It is unavoidable, and indeed favourable to IR. In particular, for full‐text retrieval, alternative semantic entities, including Salton et al.'s ‘passage retrieval’, are proposed to replace the traditional document record as the basic retrieval entity. These empirically observed phenomena of inconsistency and of semantic entities and values associated with data interpretation support strongly a cognitive approach to IR and the logical use of polyrepresentation, cognitive overlaps, and both data fusion and data diffusion.

Cognitive perspectives of information retrieval interaction : elements of a cognitive IR theory

It hsa been known that different representations of a query ret rieve different sets of documents. Recent work suggests that significant improvements in retrieval performance can be achieved by combining multiple representations of an information need. However, little effort has been made to understand the reason why combining multiple sources of evidence improves retrieval effectiveness. In this paper we analyze why improvements can be achieved with evidence combination, and investigate how evidence should be combined. We describe a rationale for multiple evidence combination, and propose a combining method whose properties coincide wit h the rationale. We also investigate e the effect of using rank instead of similarity on retrieval effectiveness.

Analyses of multiple evidence combination

Network representations have been used in information retrieval since at least the early 1960’s. Networks have been used to support diverse retrieval functions, including browsing [38], document clustering [7], spreading activation search [4], support for multiple search strategies [11], and representation of user knowledge [27] or document content [40]. Recent work suggests that significant improvements in retrieval performance will require techniques that, in some sense “understand” the content of documents and queries [9, 43] and can be used to infer probable relationships between documents and queries. In this view, information retrieval is an inference or evidential reasoning process in which we estimate the probability that a user’s information need, expressed as one or more queries, is met given a document as “evidence.” Network representations show promise as mechanisms for inferring these kinds of relationships [4, 12].

Evaluation of an inference network-based retrieval model

Three different types of classifiers were investigatedin the context of a text categorization problem in the medical domain: the automatic assignment of ICD9 codes to dictated inpatient discharge summaries. K-nearest-neighbor, relevance feedback, and Bayesian independence classifiers were applied individually and in combination. A coknbination of different classifiers produced better results than any single type of classifier. For this specific medical categorization problem, new query formulation and weighting methods used in the k-nearest-neighbor classifier improved performance.

Combining classifiers in text categorization

Most previous investigations comparing the performance of different representations have used recall and precision as performance measures. However, there is evidence to show that these measures are insensitive to an important difference between representations. To explain, two representations may perform similarly on these measures, while retrieving very different sets of documents. Equivalence of representations should be decided on the basis of similarity in performance and similarity in the documents retrieved. This study compared the performance of four representations in the PsycAbs database. In addition, overlap between retrieved sets was also computed where overlap is the proportion of retrieved documents that are the same for pairs of document representations. Results indicate that for any two representations considered, performance values differed slightly while overlap scores were also low, thus supporting the evidence that recall and precision as performance measures mask differences between the sets of retrieved documents. Results are interpreted to propose an optimal ordering of the representations and to examine the contribution of each representation given this combination.

A Study of the Overlap Among Document Representations

On‐Line Information Retrieval Systems: An Introductory‐Manual to Principles and Practice. Bernard Houghton anD John Convey. London: Clive Bingley; 1977

Judith A. Tessier

Papers

A Study of the Overlap Among Document Representations

On‐Line Information Retrieval Systems: An Introductory‐Manual to Principles and Practice. Bernard Houghton anD John Convey. London: Clive Bingley; 1977