http://hdl.handle.net/123456789/463 2026-04-03T20:56:14Z http://hdl.handle.net/123456789/7102 A Semantic Web Primer A Semantic Web Primer Second Edition Antoniou Grigoris; Harmelen Frank Van; Antoniou Grigoris; Harmelen Frank Van The development of the Semantic Web, with machine-readable content, has the potential to revolutionize the World Wide Web and its uses. A Semantic Web Primer provides an introduction and guide to this still emerging field, describing its key ideas, languages, and technologies. Suitable for use as a textbook or for self-study by professionals, it concentrates on undergraduate-level fundamental concepts and techniques that will enable readers to proceed with building applications on their own and includes exercises, project descriptions, and annotated references to relevant online materials. A Semantic Web Primer provides a systematic treatment of the different languages (XML, RDF, OWL, and rules) and technologies (explicit metadata, ontologies, and logic and inference) that are central to Semantic Web development as well as such crucial related topics as ontology engineering and application scenarios. This substantially revised and updated second edition reflects recent developments in the field, covering new application areas and tools. The new material includes a discussion of such topics as SPARQL as the RDF query language; OWL DLP and its interesting practical and theoretical properties; the SWRL language (in the chapter on rules); OWLS (on which the discussion of Web services is now based). The new final chapter considers the state of the art of the field today, captures ongoing discussions, and outlines the most challenging issues facing the Semantic Web in the future. Supplementary materials, including slides, online versions of many of the code fragments in the book, and links to further reading, can be found at 2008-01-01T00:00:00Z http://hdl.handle.net/123456789/7101 Student modeling from conventional test data: A Bayesian approach without priors Vanlehn Kurt; Niu Zhendong; Siler Stephanie; Gertner Abigail Although conventional tests are often used for determining a student's overall competence, they are seldom used for determining a fine-grained model. However, this problem does arise occasionally, such as when a conventional test is used to initialize the student model of an ITS. Existing psychometric techniques for solving this problem are intractable. Straightforward Bayesian techniques are also inapplicable because they depend too strongly on the priors, which are often not available. Our solution is to base the assessment on the difference between the prior and posterior probabilities. If the test data raise the posterior probability of mastery of a piece of knowledge even slightly above its prior probability, then that is interpreted as evidence that the student has mastered that piece of knowledge. Evaluation of this technique with artificial students indicates that it can deliver highly accurate assessments. 1998-01-01T00:00:00Z http://hdl.handle.net/123456789/7090 Complete Answer Aggregates for Tree- Like Databases: A Novel Approach to Combine Querying and Navigation Meuss Holger; Schulz Klaus U The use of markup languages like SGML, HTML, or XML for encoding the structure of documents or linguistic data has lead to many databases where entries are adequately described as trees. In this context querying formalisms are interesting that offer the possibility to refer both to textual content and logical structure. We consider models where the structure specified in a query is not only used as a filter, but also for selecting and presenting different parts of the data. If answers are formalized as mappings from query nodes to the database, a simple enumeration of all mappings in the answer set will often suffer from the effect that many answers have common subparts. From a theoretical point of view this may lead to an exponential time complexity of the computation and presentation of all answers. Concentrating on the language of so-called tree queries—a variant and extension of Kilpeläi-nen's Tree Matching formalism—we introduce the notion of a " complete answer aggregate " for a given query. This new data structure offers a compact view of the set of all answers and supports active exploration of the answer space. Since complete answer aggregates use a powerful structure-sharing mechanism their maximal size is of order Od h q where d and q respectively denote the size of the database and the query, and h is the maximal depth of a path of the database. An algorithm is given that computes a complete answer aggregate for a given tree query in time Od logd h q. For the sublanguage of so-called rigid tree queries, as well as for so-called " nonrecursive " databases, an improved bound of Od logd q is obtained. The algorithm is based on a specific index structure that supports practical efficiency. http://hdl.handle.net/123456789/7023 Do Clarity Scores for Queries Correlate with User Performance? Turpin A; Hersh W Recently the concept of a clarity score was introduced in order to measure the ambiguity of a query in relation to the collection in which the query issuer is seeking information [Cronen-Townsend et al. Proc. ACM SIGIR2002, Tampere Finland, August 2002]. If the query is expressed in the " same language " as the whole collection then it has a low clarity score, otherwise it has a high score, where the similarity is the relative entropy of the query and collection models. Cronen-Townsend et al. show that clarity scores correlate directly with average precision , hence a query with a high clarity score is likely to produce relevant documents high in a list of resulting documents. Other authors, however, have shown that high precision does not necessarily correlate with increased user performance. In this paper we examine the correlation between user performance and clarity score. Using log files from user experiments conducted within the framework of the TREC Interactive Track, we measure the clarity score of all user queries, and their actual performance on the searching task. Our results show that there is no correlation between the clarity of a query and user performance. The results also demonstrate that users were able to slightly improve their queries, so that subsequent queries had slightly higher clarity scores than initial queries, but this was not dependent on the quality of the system they used, nor the user's searching experience. 2004-01-01T00:00:00Z