Peter D. Bruza

Query expansion using term relationships in language models for information retrieval

Language Modeling (LM) has been successfully applied to Information Retrieval (IR). However, most of the existing LM approaches only rely on term occurrences in documents, queries and document collections. In traditional unigram based models, terms (or words) are usually considered to be independent. In some recent studies, dependence models have been proposed to incorporate term relationships into LM, so that links can be created between words in the same sentence, and term relationships (e.g. synonymy) can be used to expand the document model. In this study, we further extend this family of dependence models in the following two ways: (1) Term relationships are used to expand query model instead of document model, so that query expansion process can be naturally implemented; (2) We exploit more sophisticated inferential relationships extracted with Information Flow (IF). Information flow relationships are not simply pairwise term relationships as those used in previous studies, but are between a set of terms and another term. They allow for context-dependent query expansion. Our experiments conducted on TREC collections show that we can obtain large and significant improvements with our approach. This study shows that LM is an appropriate framework to implement effective query expansion.

Interactive Internet search: keyword, directory and query reformulation mechanisms compared

This article compares search effectiveness when using query-based Internet search (via the Google search engine), directory-based search (via Yahoo) and phrase-based query reformulation assisted search (via the Hyperindex browser) by means of a controlled, user-based experimental study. The focus was to evaluate aspects of the search process. Cognitive load was measured using a secondary digit-monitoring task to quantify the effort of the user in various search states; independent relevance judgements were employed to gauge the quality of the documents accessed during the search process. Time was monitored in various search states. Results indicated the directory-based search does not offer increased relevance over the query-based search (with or without query formulation assistance), and also takes longer. Query reformulation does significantly improve the relevance of the documents through which the user must trawl versus standard query-based internet search. However, the improvement in document relevance comes at the cost of increased search time and increased cognitive load.

Towards context sensitive information inference

Humans can make hasty, but generally robust judgements about what a text fragment is, or is not, about. Such judgements are termed information inference. This article furnishes an account of information inference from a psychologistic stance. By drawing on theories from nonclassical logic and applied cognition, an information inference mechanism is proposed that makes inferences via computations of information flow through an approximation of a conceptual space. Within a conceptual space information is represented geometrically. In this article, geometric representations of words are realized as vectors in a high dimensional semantic space, which is automatically constructed from a text corpus. Two approaches were presented for priming vector representations according to context. The first approach uses a concept combination heuristic to adjust the vector representation of a concept in the light of the representation of another concept. The second approach computes a prototypical concept on the basis of exemplar trace texts and moves it in the dimensional space according to the context. Information inference is evaluated by measuring the effectiveness of query models derived by information flow computations. Results show that information flow contributes significantly to query model effectiveness, particularly with respect to precision. Moreover, retrieval effectiveness compares favorably with two probabilistic query models, and another based on semantic association. More generally, this article can be seen as a contribution towards realizing operational systems that mimic text-based human reasoning.

Stratified hypermedia structures for information disclosure

In this paper we generalise the two-level approach to hypertext (hypermedia) systems into stratified hypermedia structures. First we describe the overall architecture of such systems, including the fundamentals of the user interface. Thereafter, its various components are discussed. Special emphasis is paid on how the underlying information model is layered. Two layers are featured: the hyperbase and the hyperindex. A characterisation calculus is presented for the characterisation of structured elements. This calculus forms the basis of a logic-based approach in connection with the associated information processor (Disclosure Machine). The logic-based approach is considered as the most general approach to the retrieval process. In addition, this calculus is useful for quality assurance in hypermedia applications. Attention is also paid to spatial coherence for relevance judgements.

Investigating aboutness axioms using information fields

This article proposes a framework, a so called information field, which allows information retrieval mechanisms to be compared inductively instead of experimentally. Such a comparison occurs as follows: Both retrieval mechanisms are first mapped to an associated information field. Within the field, the axioms that drive the retrieval process can be filtered out. Tn this way, the implicit assumptions governing an information retrieval mechanism can be brought to light. The retrieval mechanisms can then be compared according to which axioms they are governed by. Using this method it is shown that Boolean retrieval is more powerful than a strict form of coordinate retrieval. The salient point is not this result in itself, but how the result was achieved.

Web searching: a process-oriented experimental study of three interactive search paradigms

This article compares search effectiveness when using query-based Internet search (via the Google search engine), directory-based search (via Yahoo), and phrase-based query reformulation-assisted search (via the Hyperindex browser) by means of a controlled, user-based experimental study. The focus was to evaluate aspects of the search process. Cognitive load was measured using a secondary digit-monitoring task to quantify the effort of the user in various search states; independent relevance judgements were employed to gauge the quality of the documents accessed during the search process and time was monitored as a function of search state. Results indicated directory-based search does not offer increased relevance over the query-based search (with or without query formulation assistance), and also takes longer. Query reformulation does significantly improve the relevance of the documents through which the user must trawl, particularly when the formulation of query terms is more difficult. However, the improvement in document relevance comes at the cost of increased search time, although this difference is quite small when the search is self-terminated. In addition, the advantage of the query reformulation seems to occur as a consequence of providing more discriminating terms rather than by increasing the length of queries.

Introduction to the special issue on quantum cognition

The subject of this special issue is quantum models of cognition. At first sight it may seem bizarre, even ridiculous, to draw a connection between quantum mechanics, a highly successful theory usually understood as modeling sub-atomic phenomena, and cognitive science. However, a growing number of researchers are looking to quantum theory to circumvent stubborn problems within their own fields. This is also true within cognitive science and related areas, hence this special issue.

Literature-based Discovery

When Don Swanson hypothesized a connection between Raynauds phenomenon and dietary fish oil, the field of literature-based discovery (LBD) was born. During the subsequent two decades a steady stream of researchers have published articles about LBD and the field has made steady progress in laying foundations and creating an identity. LBD is an inherently multi-disciplinary enterprise where collaborations between the information and biomedical sciences are readily encountered. It is the hope and intention that this volume will plant a flag in the ground and inspire new researchers to the LBD challenge.

Aboutness from a commonsense perspective

Information retrieval (IR) is driven by a process that decides whether a document is about a query. Recent attempts spawned from a logic-based information retrieval theory have formalized properties characterizing “aboutness,” but no consensus has yet been reached. The proposed properties are largely determined by the underlying framework within which aboutness is defined. In addition, some properties are only sound within the context of a given IR model, but are not sound from the perspective of the user. For example, a common form of aboutness, namely overlapping aboutness, implies precision degrading properties such as compositional monotonicity. Therefore, the motivating question for this article is: independent of any given IR model, and examined within an information-based, abstract framework, what are commonsense properties of aboutness (and its dual, nonaboutness)? We propose a set of properties characterizing aboutness and nonaboutness from a commonsense perspective. Special attention is paid to the rules prescribing conservative behavior of aboutness with respect to information composition. The interaction between aboutness and nonaboutness is modeled via normative rules. The completeness, soundness, and consistency of the aboutness proof systems are analyzed and discussed. A case study based on monotonicity shows that many current IR systems are either monotonic or nonmonotonic. An interesting class of IR models, namely those that are conservatively monotonic, is identified.

What is information discovery about

The Internet has led to an increase in the quantity and diversity of information available for searching. Furthermore, users are bombarded by a constant barrage of electronic messages in the form of e-mail, faxes, etc. This has led to a plethora of search engines, “intelligent” agents, etc., that aim to help users in their quest for relevant information, or shield them against irrelevant information. All these systems aim to identify the potentially relevant information in among a large pool of available information. No unifying underlying theory for information discovery systems exists as yet. The aim of this article is to provide a logic-based framework for information discovery, and relate this to the traditional field of information retrieval. Furthermore, the often ignored user receives special emphasis. In information discovery, a good understanding of a users (sometimes hidden) needs and beliefs is essential. We will develop a logic-based approach to express the mechanics of information discovery, while the pragmatics are based on an analysis of the underlying informational semantics of information carriers and information needs of users.