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Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

How to Do Things With Words

Statistics for Spatial Data.

Information retrieval is the science concerned with the efficient and effective storage of information for the later retrieval and use by interested parties. During the last forty years, a plethora of information retrieval models and their variations have emerged. Logic-based models were launched to provide a rich and uniform representation of information and its semantics with the aim to improve information retrieval effectiveness. This approach was first advanced in 1986 by Van Rijsbergen with the so-called logical uncertainty principle. Since, various logic-based models have been developed. This paper presents an introduction and a survey of the use of logic for information retrieval modeling.

The use of logic in information retrieval modelling

This paper addresses the notion of aboutness in information retrieval. First, an exposition is given on how aboutness relates to relevance—a fundamental notion in information retrieval. A short summary is given on how aboutness is defined in more prominent information retrieval models. A model-theoretic definition of aboutness is then analyzed in an abstract setting using so called information fields. These allows properties of aboutness to be expressed independent of any given information retrieval model. As a consequence, information retrieval models can be theoretically compared according to what aboutness postulates they support. The Boolean and Coordinate retrieval models are compared in this fashion. In addition to model-theoretic aboutness, preferential entailment and conditional probabilities are employed to define aboutness between primitive information carriers. The preferential entailment approach is based on a preference semantics derived from nonmonotonic logics. The nonmonotonic behaviour of aboutness under information composition is highlighted. Rules describing how aboutness may be preserved under composition are proposed. Finally, a term aboutness definition drawn from a network-based probabilistic framework is analyzed. Conclusions regarding the implied retrieval effectiveness are drawn.

A study of aboutness in information retrieval

It is well known that taking the Web user profiles into account can enhance the effectiveness of Web mining systems. However, due to the dynamic and complex nature of Web users, automatically acquiring worthwhile user profiles was found to be very challenging. Ontology-based user profile can possess more accurate user information. This research emphasizes on acquiring search intentions information. This paper presents a new approach of developing user profile for Web searching. The model considers the user's search intentions by the process of PTM (Pattern-Taxonomy Model). Initial experiments show that the user profile based on search intention is more useful than the generic PTM user profile. Developing user profile that contains user search intentions is essential for effective Web search and retrieval.

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Utilizing Search Intent in Topic Ontology-Based User Profile for Web Mining

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 user's (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.

What is information discovery about

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.

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Peter Bruza

Papers

The use of logic in information retrieval modelling

A study of aboutness in information retrieval

Utilizing Search Intent in Topic Ontology-Based User Profile for Web Mining

What is information discovery about

Aboutness from a commonsense perspective