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.

EP-SPARQL: a unified language for event processing and stream reasoning

Abstract: Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams.To bridge the gap, we propose Event Processing SPARQL (EP-SPARQL) as a new language for complex events and Stream Reasoning. We provide syntax and formal semantics of the language and devise an effective execution model for the proposed formalism. The execution model is grounded on logic programming, and features effective event processing and inferencing capabilities over temporal and static knowledge. We provide an open-source prototype implementation and present a set of tests to show the usefulness and effectiveness of our approach.

Ontology languages for the Semantic Web

Abstract: Ontologies have proven to be an essential element in many applications. They are used in agent systems, knowledge management systems, and e-commerce platforms. They can also generate natural language, integrate intelligent information, provide semantic-based access to the Internet, and extract information from texts in addition to being used in many other applications to explicitly declare the knowledge embedded in them. However, not only are ontologies useful for applications in which knowledge plays a key role, but they can also trigger a major change in current Web contents. This change is leading to the third generation of the Web-known as the Semantic Web-which has been defined as the conceptual structuring of the Web in an explicit machine-readable way. New ontology-based applications and knowledge architectures are developing for this new Web. A common claim for all of these approaches is the need for languages to represent the semantic information that this Web requires-solving heterogeneous data exchange in this heterogeneous environment. Our goal is to help developers find the most suitable language for their representation needs.

How to Publish Linked Data on the Web - Proposal for a Half-day Tutorial at ISWC2008

Abstract: The Web is increasingly understood as a global information space consisting not just of linked documents, but also of Linked Data. The Linked Data principles provide a basis for realizing this Web of Data, or Semantic Web. Since early 2007 numerous data sets have been published on the Web according to these principles, in domains as broad as music, books, geographical information, films, people, events, reviews and photos. In combination these data sets consist of over 2 billion RDF triples, interlinked by more than 3 million triples that cross data sets. As this Web of Linked Data continues to grow, and an increasing number of applications are developed that exploit these data sets, there is a growing need for data publishers, researchers, developers and Web practitioners to understand Linked Data principles and practice. Run by some of the leading members of the Linked Data community, this tutorial will address those needs, and provide participants with a solid foundation from which to begin publishing Linked Data on the Web, as well as to implement applications that consume Linked Data from the Web.

Automatic fuzzy ontology generation for semantic Web

Abstract: Ontology is an effective conceptualism commonly used for the semantic Web. Fuzzy logic can be incorporated to ontology to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined concept hierarchy. However, to construct a concept hierarchy for a certain domain can be a difficult and tedious task. To tackle this problem, this paper proposes the FOGA (fuzzy ontology generation framework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: fuzzy formal concept analysis, concept hierarchy generation, and fuzzy ontology generation. We also discuss approximating reasoning for incremental enrichment of the ontology with new upcoming data. Finally, a fuzzy-based technique for integrating other attributes of database to the ontology is proposed