Thesaurus-Based Automatic Indexing

In this chapter, we present a thesaurus application in the field of text mining and more specifically automatic indexing on the set of descriptors defined by a thesaurus. We begin by presenting various definitions and a mathematical thesaurus model, and also describe various examples of real world thesauri which are used in official institutions. We then explore the problem of thesaurus-based automatic indexing by describing its difficulties and distinguishing features and reviewing previous work in this area. Finally, we propose various lines of future research.

Text Mining in Bioinformatics

The biology literatures have been increased in an exponential growth in recent year. The researchers need an effective tool to help them find out the needed information in the databases. Text mining is a powerful tool to solve this problem. In this chapter, we talked about the features of text mining and bioinformatics, text mining applications, research methods in bioinformatics and problems and future path.

A Survey of Selected Software Technologies for Text Mining

This chapter presents background on text mining, and comparisons and summaries of seven selected software for text mining. The text mining software selected for discussion and comparison in this chapter are: Compare Suite by AKS-Labs, SAS Text Miner, Megaputer Text Analyst, Visual Text by Text Analysis International, Inc. (TextAI), Magaputer PolyAnalyst, WordStat by Provalis Research, and SPSS Clementine. This chapter not only discusses unique features of these text mining software packages but also compares the features offered by each in the following key steps in analyzing unstructured qualitative data: data preparation, data analysis, and result reporting. A brief discussion of Web mining and its software are also presented, as well as conclusions and future trends.

Web Service Architectures for Text Mining

Text mining technology can be used to assist in finding relevant or novel information in large volumes of unstructured data, such as that which is increasingly available in the electronic scientific literature. However, publishers are not text mining specialists, nor typically are the end-user scientists who consume their products. This situation suggests a Web services based solution, where text mining specialists process the literature obtained from publishers and make their results available to remote consumers (research scientists). In this chapter we discuss the integration of Web services and text mining within the domain of scientific publishing and explore the strengths and weaknesses of three generic architectural designs for delivering text mining Web services. We argue for the superiority of one of these and demonstrate its viability by reference to an application designed to provide access to the results of text mining over the PubMed database of scientific abstracts.

Application of Text Mining Methodologies to Health Insurance Schedules

This chapter describes the application of a number of text mining techniques to discover patterns in the health insurance schedule with an aim to uncover any inconsistency or ambiguity in the schedule. In particular, we will apply first a simple “bag of words” technique to study the text data, and to evaluate the hypothesis: Is there any inconsistency in the text description of the medical procedures used? It is found that the hypothesis is not valid, and hence the investigation is continued on how best to cluster the text. This work would have significance to health insurers to assist them to differentiate descriptions of the medical procedures. Secondly, it would also assist the health insurer to describe medical procedures in an unambiguous manner.

Web Mining System for Mobile-Phone Marketing

With the ever-increasing and ever-changing flow of information available on the Web, information analysis has never been more important. Web text mining, which includes text categorization, text clustering, association analysis and prediction of trends, can assist us in discovering useful information in an effective and efficient manner. In this chapter, we have proposed a Web mining system that incorporates both online efficiency and off-line effectiveness to provide the “right” information based on users’ preferences. A Bi-Objective Fuzzy c-Means algorithm and information retrieval technique, for text categorization, clustering and integration, was employed for analysis. The proposed system is illustrated via a case involving the Web site marketing of mobile phones. A variety of Web sites exist on the Internet and a common type involves the trading of goods. In this type of Web site, the question to ask is: If we want to establish a Web site that provides information about products, how can we respond quickly and accurately to queries? This is equivalent to asking: How can we design a flexible search engine according to users’ preferences? In this study, we have applied data mining techniques to cope with such problems, by proposing, as an example, a Web site providing information on mobile phones in Taiwan. In order to efficiently provide useful information, two tasks were considered during the Web design phase. One related to off-line analysis: this was done by first carrying out a survey of frequent Web users, students between 15 and 40 years of age, regarding their preferences, so that Web customers’ behavior could be characterized. Then the survey data, as well as the products offered, were classified into different demand and preference groups. The other task was related to online query: this was done through the application of an information retrieval technique that responded to users’ queries. Based on the ideas above the remainder of the chapter is organized as follows: first, we present a literature review, introduce some concepts and review existing methods relevant to our study, then, the proposed Web mining system is presented, a case study of a mobile-phone marketing Web site is illustrated and finally, a summary and conclusions are offered.

Literature Review in Computational Linguistics Issues in the Developing Field of Consumer Informatics

With the increased use of Internet, a large number of consumers first consult on line resources for their healthcare decisions. The problem of the existing information structure primarily lies in the fact that the vocabulary used in consumer queries is intrinsically different from the vocabulary represented in medical literature. Consequently, the medical information retrieval often provides poor search results. Since consumers make medical decisions based on the search results, building an effective information retrieval system becomes an essential issue. By reviewing the foundational concepts and application components of medical information retrieval, this paper will contribute to a body of research that seeks appropriate answers to a question like “How can we design a medical information retrieval system that can satisfy consumer’s information needs?”

Collaborative Filtering Based Recommendation Systems

This chapter introduces Collaborative filtering-based recommendation systems, which has become an integral part of E-commerce applications, as can be observed in sites like Amazon.com. It will present several techniques that are reported in the literature to make useful recommendations, and study their limitations. The chapter also lists the issues that are currently open and the future directions that may be explored to address those issues. Furthermore, the authors hope that understanding of these limitations and issues will help build recommendation systems that are of high accuracy and have few false positive errors (which are products that are recommended, though the user does not like them).

An HL7-Aware Decision Support System for E-Health

In this chapter we present an information system conceived for supporting managers of Public Health Care Agencies to decide the new health care services to propose. Our system is HL7-aware; in fact, it uses the HL7 (Health Level Seven) standard (Health Level Seven [HL7], 2007) to effectively handle the interoperability among different Public Health Care Agencies. HL7 provides several functionalities for the exchange, the management and the integration of data concerning both patients and health care services. Our system appears particularly suited for supporting a rigorous and scientific decision making activity, taking a large variety of factors and a great amount of heterogeneous information into account.

Current Issues and Future Analysis in Text Mining for Information Security Applications

Text mining is an instrumental technology that today’s organizations can employ to extract information and further evolve and create valuable knowledge for more effective knowledge management. It is also an important tool in the arena of information systems security (ISS). While a plethora of text mining research has been conducted in search of revamped technological developments, relatively limited attention has been paid to the applicable insights of text mining in ISS. In this chapter, we address a variety of technological applications of text mining in security issues. The techniques are categorized according to the types of knowledge to be discovered and the text formats to be analyzed. Privacy issues of text mining as well as future trends are also discussed.