Semantic Web

About: Semantic Web is a research topic. Over the lifetime, 26987 publications have been published within this topic receiving 534275 citations. The topic is also known as: Sem Web & SemWeb.

Web search behavior of Internet experts and newbies

Abstract: Searching for relevant information on the World Wide Web is often a laborious and frustrating task for casual and experienced users. To help improve searching on the Web based on a better understanding of user characteristics, we investigate what types of knowledge are relevant for Web-based information seeking, and which knowledge structures and strategies are involved. Two experimental studies are presented, which address these questions from different angles and with different methodologies. In the first experiment, 12 established Internet experts are first interviewed about search strategies and then perform a series of realistic search tasks on the World Wide Web. From this study a model of information seeking on the World Wide Web is derived and then tested in a second study. In the second experiment two types of potentially relevant types of knowledge are compared directly. Effects of Web experience and domain-specific background knowledge are investigated with a series of search tasks in an economics-related domain (introduction of the Euro currency). We find differential and combined effects of both Web experience and domain knowledge: while successful search performance requires the combination of the two types of expertise, specific strategies directly related to Web experience or domain knowledge can be identified.

Foundations of Semantic Web Technologies

Abstract: With more substantial funding from research organizations and industry, numerous large-scale applications, and recently developed technologies, the Semantic Web is quickly emerging as a well-recognized and important area of computer science. While Semantic Web technologies are still rapidly evolving, Foundations of Semantic Web Technologies focuses on the established foundations in this area that have become relatively stable over time. It thoroughly covers basic introductions and intuitions, technical details, and formal foundations.The book concentrates on Semantic Web technologies standardized by the World Wide Web Consortium: RDF and SPARQL enable data exchange and querying, RDFS and OWL provide expressive ontology modeling, and RIF supports rule-based modeling. The text also describes methods for specifying, querying, and reasoning with ontological information. In addition, it explores topics that are clearly beyond foundations, such as tools, applications, and engineering aspects.Written by highly respected researchers with a deep understanding of the material, this text centers on the formal specifications of the subject and supplies many pointers that are useful for employing Semantic Web technologies in practice.Updates, errata, slides for teaching, and links to further resources are available at http://semantic-web-book.org/

Scalable semantic web data management using vertical partitioning

Abstract: Efficient management of RDF data is an important factor in realizing the Semantic Web vision. Performance and scalability issues are becoming increasingly pressing as Semantic Web technology is applied to real-world applications. In this paper, we examine the reasons why current data management solutions for RDF data scale poorly, and explore the fundamental scalability limitations of these approaches. We review the state of the art for improving performance for RDF databases and consider a recent suggestion, "property tables." We then discuss practically and empirically why this solution has undesirable features. As an improvement, we propose an alternative solution: vertically partitioning the RDF data. We compare the performance of vertical partitioning with prior art on queries generated by a Web-based RDF browser over a large-scale (more than 50 million triples) catalog of library data. Our results show that a vertical partitioned schema achieves similar performance to the property table technique while being much simpler to design. Further, if a column-oriented DBMS (a database architected specially for the vertically partitioned case) is used instead of a row-oriented DBMS, another order of magnitude performance improvement is observed, with query times dropping from minutes to several seconds.

Crawling the Hidden Web

Abstract: Current-day crawlers retrieve content only from the publicly indexable Web, i.e., the set of Web pages reachable purely by following hypertext links, ignoring search forms and pages that require authorization or prior registration. In particular, they ignore the tremendous amount of high quality content “hidden” behind search forms, in large searchable electronic databases. In this paper, we address the problem of designing a crawler capable of extracting content from this hidden Web. We introduce a generic operational model of a hidden Web crawler and describe how this model is realized in HiWE (Hidden Web Exposer), a prototype crawler built at Stanford. We introduce a new Layout-based Information Extraction Technique (LITE) and demonstrate its use in automatically extracting semantic information from search forms and response pages. We also present results from experiments conducted to test and validate our techniques.

OWL 2 Web Ontology Language Primer (Second Edition)

Abstract: The OWL 2 Web Ontology Language, informally OWL 2, is an ontology language for the Semantic Web with formally defined meaning. OWL 2 ontologies provide classes, properties, individuals, and data values and are stored as Semantic Web documents. OWL 2 ontologies can be used along with information written in RDF, and OWL 2 ontologies themselves are primarily exchanged as RDF documents. The OWL 2 Document Overview describes the overall state of OWL 2, and should be read before other OWL 2 documents.