Search (4 results, page 1 of 1)

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Date

19. 7.2006 19:52:26

Footnote

Rez. in: JASIST 57(2006) no.8, S.1132-1133 (H. Che): "The World Wide Web has been the main source of an important shift in the way people communicate with each other, get information, and conduct business. However, most of the current Web content is only suitable for human consumption. The main obstacle to providing better quality of service is that the meaning of Web content is not machine-accessible. The "Semantic Web" is envisioned by Tim Berners-Lee as a logical extension to the current Web that enables explicit representations of term meaning. It aims to bring the Web to its full potential via the exploration of these machine-processable metadata. To fulfill this, it pros ides some meta languages like RDF, OWL, DAML+OIL, and SHOE for expressing knowledge that has clear, unambiguous meanings. The first steps in searing the Semantic Web into the current Web are successfully underway. In the forthcoming years, these efforts still remain highly focused in the research and development community. In the next phase, the Semantic Web will respond more intelligently to user queries. The first chapter gets started with an excellent introduction to the Semantic Web vision. At first, today's Web is introduced, and problems with some current applications like search engines are also covered. Subsequently, knowledge management. business-to-consumer electronic commerce, business-to-business electronic commerce, and personal agents are used as examples to show the potential requirements for the Semantic Web. Next comes the brief description of the underpinning technologies, including metadata, ontology, logic, and agent. The differences between the Semantic Web and Artificial Intelligence are also discussed in a later subsection. In section 1.4, the famous "laser-cake" diagram is given to show a layered view of the Semantic Web. From chapter 2, the book starts addressing some of the most important technologies for constructing the Semantic Web. In chapter 2, the authors discuss XML and its related technologies such as namespaces, XPath, and XSLT. XML is a simple, very flexible text format which is often used for the exchange of a wide variety of data on the Web and elsewhere. The W3C has defined various languages on top of XML, such as RDF. Although this chapter is very well planned and written, many details are not included because of the extensiveness of the XML technologies. Many other books on XML provide more comprehensive coverage.
The next chapter introduces resource description framework (RDF) and RDF schema (RDFS). Unlike XML, RDF provides a foundation for expressing the semantics of dada: it is a standard dada model for machine-processable semantics. Resource description framework schema offers a number of modeling primitives for organizing RDF vocabularies in typed hierarchies. In addition to RDF and RDFS, a query language for RDF, i.e. RQL. is introduced. This chapter and the next chapter are two of the most important chapters in the book. Chapter 4 presents another language called Web Ontology Language (OWL). Because RDFS is quite primitive as a modeling language for the Web, more powerful languages are needed. A richer language. DAML+OIL, is thus proposed as a joint endeavor of the United States and Europe. OWL takes DAML+OIL as the starting point, and aims to be the standardized and broadly accepted ontology language. At the beginning of the chapter, the nontrivial relation with RDF/RDFS is discussed. Then the authors describe the various language elements of OWL in some detail. Moreover, Appendix A contains an abstract OWL syntax. which compresses OWL and makes OWL much easier to read. Chapter 5 covers both monotonic and nonmonotonic rules. Whereas the previous chapter's mainly concentrate on specializations of knowledge representation, this chapter depicts the foundation of knowledge representation and inference. Two examples are also givwn to explain monotonic and non-monotonic rules, respectively. "To get the most out of the chapter. readers had better gain a thorough understanding of predicate logic first. Chapter 6 presents several realistic application scenarios to which the Semantic Web technology can be applied. including horizontal information products at Elsevier, data integration at Audi, skill finding at Swiss Life, a think tank portal at EnerSearch, e-learning. Web services, multimedia collection indexing, online procurement, raid device interoperability. These case studies give us some real feelings about the Semantic Web.

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Abstract

The relationship between a person with a question and a source of information is complex. Indexing and abstracting often fail because too much emphasis is put on the mechanics of description, and too little has been given as to what ought to be represented. Research literature suggests that inappropriate representation results in failed searches a significant number of times, perhaps even in a majority of cases. "Doing Things with Information" seeks to rectify this unfortunate situation by emphasizing methods of modeling and constructing appropriate representations of such questions and documents. Students in programs of information studies will find focal points for discussion about system design and refinement of existing systems. Librarians, scholars, and those who work within large document collections, whether paper or electronic, will find insights into the strengths and weaknesses of the access systems they use.

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Date

26. 5.1996 11:11:10

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Footnote

Rez. in: JASIST 58(2007) no.6, S.910-911 (J.L. Vicedo u. J. Gomez): "The Text REtrieval Conference (TREC) is a yearly workshop hosted by the U.S. government's National Institute of Standards and Technology (NIST) that fosters and supports research in information retrieval as well as speeding the transfer of technology between research labs and industry. Since 1992, TREC has provided the infrastructure necessary for large-scale evaluations of different text retrieval methodologies. TREC impact has been very important and its success has been mainly supported by its continuous adaptation to the emerging information retrieval needs. Not in vain, TREC has built evaluation benchmarks for more than 20 different retrieval problems such as Web retrieval, speech retrieval, or question-answering. The large and intense trajectory of annual TREC conferences has resulted in an immense bulk of documents reflecting the different eval uation and research efforts developed. This situation makes it difficult sometimes to observe clearly how research in information retrieval (IR) has evolved over the course of TREC. TREC: Experiment and Evaluation in Information Retrieval succeeds in organizing and condensing all this research into a manageable volume that describes TREC history and summarizes the main lessons learned. The book is organized into three parts. The first part is devoted to the description of TREC's origin and history, the test collections, and the evaluation methodology developed. The second part describes a selection of the major evaluation exercises (tracks), and the third part contains contributions from research groups that had a large and remarkable participation in TREC. Finally, Karen Spark Jones, one of the main promoters of research in IR, closes the book with an epilogue that analyzes the impact of TREC on this research field.