Foundations research in information retrieval inspired by quantum theory

In the information age information is useless unless it can be found and used, search engines in our time thereby form a crucial component of research. For something so crucial, information retrieval (IR), the formal discipline investigating search, can be a confusing area of study. There is an underlying difficulty, with the very definition of information retrieval, and weaknesses in its operational method, which prevent it being called a 'science'. The work in this thesis aims to create a formal definition for search, scientific methods for evaluation and comparison of different search strategies, and methods for dealing with the uncertainty associated with user interactions; so that one has the necessary formal foundation to be able to perceive IR as "search science". The key problems restricting a science of search pertain to the ambiguity in the current way in which search scenarios and concepts are specified. This especially affects evaluation of search systems since according to the traditional retrieval approach, evaluations are not repeatable, and thus not collectively verifiable. This is mainly due to the dependence on the method of user studies currently dominating evaluation methodology. This evaluation problem is related to the problem of not being able to formally define the users in user studies. The problem of defining users relates in turn to one of the main retrieval-specific motivations of the thesis, which can be understood by noticing that uncertainties associated with the interpretation of user interactions are collectively inscribed in a relevance concept, the representation and use of which defines the overall character of a retrieval model. Current research is limited in its understanding of how to best model relevance, a key factor restricting extensive formalization of the IR discipline as a whole. Thus, the problems of defining search systems and search scenarios are the principle issues preventing formal comparisons of systems and scenarios, in turn limiting the strength of experimental evaluation. Alternative models of search are proposed that remove the need for ambiguous relevance concepts and instead by arguing for use of simulation as a normative evaluation strategy for retrieval, some new concepts are introduced that can be employed in judging effectiveness of search systems. Included are techniques for simulating search, techniques for formal user modelling and techniques for generating measures of effectiveness for search models. The problems of evaluation and of defining users are generalized by proposing that they are related to the need for an unified framework for defining arbitrary search concepts, search systems, user models, and evaluation strategies. It is argued that this framework depends on a re-interpretation of the concept of search accommodating the increasingly embedded and implicit nature of search on modern operating systems, internet and networks. The re-interpretation of the concept of search is approached by considering a generalization of the concept of ostensive retrieval producing definitions of search, information need, user and system that (formally) accommodates the perception of search as an abstract process that can be physical and/or computational. The feasibility of both the mathematical formalism and physical conceptualizations of quantum theory (QT) are investigated for the purpose of modelling the this abstract search process as a physical process. Techniques for representing a search process by the Hilbert space formalism in QT are presented from which techniques are proposed for generating measures for effectiveness that combine static information such as term weights, and dynamically changing information such as probabilities of relevance. These techniques are used for deducing methods for modelling information need change. In mapping the 'macro level search' process to 'micro level physics' some generalizations were made to the use and interpretation of basic QT concepts such the wave function description of state and reversible evolution of states corresponding to the first and second postulates of quantum theory respectively. Several ways of expressing relevance (and other retrieval concepts) within the derived framework are proposed arguing that the increase in modelling power by use of QT provides effective ways to characterize this complex concept. Mapping the mathematical formalism of search to that of quantum theory presented insightful perspectives about the nature of search. However, differences between the operational semantics of quantum theory and search restricted the usefulness of the mapping. In trying to resolve these semantic differences, a semi-formal framework was developed that is mid-way between a programmatic language, a state-based language resembling the way QT models states, and a process description language. By using this framework, this thesis attempts to intimately link the theory and practice of information retrieval and the evaluation of the retrieval process. The result is a novel, and useful way for formally discussing, modelling and evaluating search concepts, search systems and search processes

The study of probability model for compound similarity searching

Information Retrieval or IR system main task is to retrieve relevant documents according to the users query. One of IR most popular retrieval model is the Vector Space Model. This model assumes relevance based on similarity, which is defined as the distance between query and document in the concept space. All currently existing chemical compound database systems have adapt the vector space model to calculate the similarity of a database entry to a query compound. However, it assumes that fragments represented by the bits are independent of one another, which is not necessarily true. Hence, the possibility of applying another IR model is explored, which is the Probabilistic Model, for chemical compound searching. This model estimates the probabilities of a chemical structure to have the same bioactivity as a target compound. It is envisioned that by ranking chemical structures in decreasing order of their probability of relevance to the query structure, the effectiveness of a molecular similarity searching system can be increased. Both fragment dependencies and independencies assumption are taken into consideration in achieving improvement towards compound similarity searching system. After conducting a series of simulated similarity searching, it is concluded that PM approaches really did perform better than the existing similarity searching. It gave better result in all evaluation criteria to confirm this statement. In terms of which probability model performs better, the BD model shown improvement over the BIR model

Non-Compositional Term Dependence for Information Retrieval

Modelling term dependence in IR aims to identify co-occurring terms that are too heavily dependent on each other to be treated as a bag of words, and to adapt the indexing and ranking accordingly. Dependent terms are predominantly identified using lexical frequency statistics, assuming that (a) if terms co-occur often enough in some corpus, they are semantically dependent; (b) the more often they co-occur, the more semantically dependent they are. This assumption is not always correct: the frequency of co-occurring terms can be separate from the strength of their semantic dependence. E.g. "red tape" might be overall less frequent than "tape measure" in some corpus, but this does not mean that "red"+"tape" are less dependent than "tape"+"measure". This is especially the case for non-compositional phrases, i.e. phrases whose meaning cannot be composed from the individual meanings of their terms (such as the phrase "red tape" meaning bureaucracy). Motivated by this lack of distinction between the frequency and strength of term dependence in IR, we present a principled approach for handling term dependence in queries, using both lexical frequency and semantic evidence. We focus on non-compositional phrases, extending a recent unsupervised model for their detection [21] to IR. Our approach, integrated into ranking using Markov Random Fields [31], yields effectiveness gains over competitive TREC baselines, showing that there is still room for improvement in the very well-studied area of term dependence in IR

Efficient & Effective Selective Query Rewriting with Efficiency Predictions

To enhance effectiveness, a user's query can be rewritten internally by the search engine in many ways, for example by applying proximity, or by expanding the query with related terms. However, approaches that benefit effectiveness often have a negative impact on efficiency, which has impacts upon the user satisfaction, if the query is excessively slow. In this paper, we propose a novel framework for using the predicted execution time of various query rewritings to select between alternatives on a per-query basis, in a manner that ensures both effectiveness and efficiency. In particular, we propose the prediction of the execution time of ephemeral (e.g., proximity) posting lists generated from uni-gram inverted index posting lists, which are used in establishing the permissible query rewriting alternatives that may execute in the allowed time. Experiments examining both the effectiveness and efficiency of the proposed approach demonstrate that a 49% decrease in mean response time (and 62% decrease in 95th-percentile response time) can be attained without significantly hindering the effectiveness of the search engine

A survey on the use of relevance feedback for information access systems

Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems

Using the quantum probability ranking principle to rank interdependent documents

A known limitation of the Probability Ranking Principle (PRP) is that it does not cater for dependence between documents. Recently, the Quantum Probability Ranking Principle (QPRP) has been proposed, which implicitly captures dependencies between documents through “quantum interference”. This paper explores whether this new ranking principle leads to improved performance for subtopic retrieval, where novelty and diversity is required. In a thorough empirical investigation, models based on the PRP, as well as other recently proposed ranking strategies for subtopic retrieval (i.e. Maximal Marginal Relevance (MMR) and Portfolio Theory(PT)), are compared against the QPRP. On the given task, it is shown that the QPRP outperforms these other ranking strategies. And unlike MMR and PT, one of the main advantages of the QPRP is that no parameter estimation/tuning is required; making the QPRP both simple and effective. This research demonstrates that the application of quantum theory to problems within information retrieval can lead to significant improvements

The Most Influential Paper Gerard Salton Never Wrote

Gerard Salton is often credited with developing the vector space model (VSM) for information retrieval (IR). Citations to Salton give the impression that the VSM must have been articulated as an IR model sometime between 1970 and 1975. However, the VSM as it is understood today evolved over a longer time period than is usually acknowledged, and an articulation of the model and its assumptions did not appear in print until several years after those assumptions had been criticized and alternative models proposed. An often cited overview paper titled ???A Vector Space Model for Information Retrieval??? (alleged to have been published in 1975) does not exist, and citations to it represent a confusion of two 1975 articles, neither of which were overviews of the VSM as a model of information retrieval. Until the late 1970s, Salton did not present vector spaces as models of IR generally but rather as models of specifi c computations. Citations to the phantom paper refl ect an apparently widely held misconception that the operational features and explanatory devices now associated with the VSM must have been introduced at the same time it was fi rst proposed as an IR model.published or submitted for publicatio