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This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO’s precision at 95% – as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO’s data.

We extend the description logic EL ++ with reflexive roles and range restrictions, and show that subsumption remains tractable if a certain syntactic restriction is adopted. We also show that subsumption becomes PSpace-hard (resp. undecidable) if this restriction is weakened (resp. dropped). Additionally, we prove that tractability is lost when symmetric roles are added: in this case, subsumption becomes ExpTime-hard.

Abstract. Logic programming (LP) is often seen as a way to overcome several shortcomings of the Web Ontology Language (OWL), such as the inability to model integrity constraints or perform closed-world querying. However, the open-world semantics of OWL seems to be fundamentally incompatible with the closed-world semantics of LP. This has sparked a heated debate in the Semantic Web community, resulting in proposals for alternative ontology languages based entirely on logic programming. To help resolving this debate, we investigate the practical use cases which seem to be addressed by logic programming. In fact, many of these requirements have already been addressed outside the Semantic Web. By drawing inspiration from these existing formalisms, we present a novel logic of hybrid MKNF knowledge bases, which seamlessly integrates OWL with LP. We are thus capable of addressing the identified use cases without a radical change in the architecture of the Semantic Web. 1

Since achieving W3C recommendation status in 2004, the Web Ontology Language (OWL) has been successfully applied to many problems in computer science. Practical experience with OWL has been quite positive in general; however, it has also revealed room for improvement in several areas. We systematically analyze the identified shortcomings of OWL, such as expressivity issues, problems with its syntaxes, and deficiencies in the definition of OWL species. Furthermore, we present an overview of OWL 2—an extension to and revision of OWL that is currently being developed within the W3C OWL Working Group. Many aspects of OWL have been thoroughly reengineered in OWL 2, thus producing a robust platform for future development of the language.

Expressive Description Logics (DLs) have been advocated as formalisms for modeling the domain of interest in various application areas. An important requirement is the ability to answer complex queries beyond instance retrieval, taking into account constraints expressed in a knowledge base. We consider this task for positive existential path queries (which generalize conjunctive queries and unions thereof), whose atoms are regular expressions over the roles (and concepts) of a knowledge base in the expressive DL ALCQIbreg. Using techniques based on two-way tree-automata, we first provide an elegant characterization of TBox and ABox reasoning, which gives us also a tight EXPTIME bound. We then prove decidability (more precisely, a 2EXPTIME upper bound) of query answering, thus significantly pushing the decidability frontier, both with respect to the query language and the considered DL. We also show that query answering is EXP-SPACE-hard already in rather restricted settings.

Although description logic systems can adequately be used for representing and reasoning about incomplete information (e.g., for John we know he is French or Italian), in practical applications it can be assumed that (only) for some tasks the expressivity of description logics really comes into play whereas for building complete applications, it is often necessary to effectively solve instance retrieval problems with respect to largely deterministic knowledge. In this paper we present and analyze the main results we have found about how to contribute to this kind of scalability problem. We assume familiarity with description logics in general and tableau provers in particular.

Fuzzy Description Logics are a family of logics which allow the representation of (and the reasoning within) structured knowledge affected by uncertainty and vagueness. They were born to overcome the limitations of classical Description Logics when dealing with such kind of knowledge, but they bring out some new challenges, requiring an appropriate fuzzy language to be agreed and needing practical and highly optimized implementations of the reasoning algorithms. In the current paper we face these problems by presenting a reasoning preserving procedure to obtain a crisp representation for a fuzzy extension of SHOIN, which makes possible to reuse a crisp representation language as well as currently available reasoners, which have demonstrated a very good performance in practice. As an additional contribution, we define the syntax and semantics of a novel fuzzy version of the nominal construct and allow to reason within fuzzy general concept inclusions.

Ontologies play a key role in the Semantic Web [4], data modeling, and information integration [16]. Recent trends in ontological reasoning have shifted from decidability issues to tractability ones, as e.g. reflected by the work on the DL-Lite family of tractable description logics (DLs) [11, 19]. An important result of these works is that the main

Abstract. We argue for the usefulness of abductive reasoning in the context of ontologies. We discuss several applicaton scenarios in which various forms of abduction would be useful, introduce corresponding abductive reasoning tasks, give examples, and begin to develop the formal apparatus needed to employ abductive inference in expressive description logics. 1