In this paper we discuss the challenges of performing reasoning on large scale RDF datasets from the Web. Using ter-Horst's pD * fragment of OWL as a base, we compose a rule-based framework for application to web data: we argue our decisions using observations of undesirable examples taken directly from the Web. We further temper our OWL fragment through consideration of "authoritative sources" which counter-acts an observed behaviour which we term "ontology hijacking": new ontologies published on the Web re-defining the semantics of existing entities resident in other ontologies. We then present our system for performing rule-based forward-chaining reasoning which we call SAOR: Scalable Authoritative OWL Reasoner. Based upon observed characteristics of web data and reasoning in general, we design our system to scale: our system is based upon a separation of terminological data from assertional data and comprises of a lightweight in-memory index, on-disk sorts and file-scans. We evaluate our methods on a dataset in the order of a hundred million statements collected from real-world web sources and present scale-up experiments on a dataset in the order of a billion statements collected from the Web.

Abstract. The Semantic Web enables companies and organizations to gather huge amounts of valuable semantically annotated data concerning their subjects of interest. Nowadays, many applications attach metadata and semantic annotations taken from domain and application ontologies to the information they generate. From our point of view, the concepts in these ontologies could describe the facts, dimensions, categories and values implied in the analysis subjects of a data warehouse. In this paper we propose the Semantic Data Warehouse to be a repository of ontologies and semantically annotated data resources. We also propose an ontology-driven framework to design multidimensional analysis models for Semantic Data Warehouses. This framework provides means for building an integrated ontology, called the Multidimensional Integrated Ontology (MIO), including the classes, relationships and instances that represent interesting analysis dimensions and measures. The reasoning capabilities of a MIO can be used to check the properties required by current multidimensional databases (e.g., dimension orthogonality, category satisfiability, etc.). In this paper we also sketch how the instance data of a MIO can be translated into OLAP cubes for analysis purposes. Finally, some implementation issues of the overall framework are discussed. Keywords: Data warehouses, Semantic Web, Multi-ontology integration 1.

There are various techniques for specifying a module of an ontology that covers all knowledge about a given set of terms. These differ with respect to the size of the module, the complexity of its computation, and certain robustness properties. In this paper, we survey existing logic-based approaches, focus on syntactic approximations, and compare different kinds of modules with respect to their properties. This is intended to give guidelines on how to choose “the right kind of module”.

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DL, is being developed by a large consortium seeking to provide a crossdomain, shared framework for representing investigations in the biological and biomedical sciences. In this paper we report our experiences and describe our development process as it pertains to OWL, which includes a number of elements that might inform tool developers as well as suggest general development patterns. Finally, we review where improvements to OWL and OWL related tools might be beneficial. 1

Medical research and clinical workflows often involve collaboration between various institutions. Therefore, ontologies such as SNOMED CT 1 or the Foundational Model of Anatomy (FMA) 2 have gained acceptance as an important tool for a common standard of communication. Medical images are often stored in

Extracting a subset of a given ontology that captures all the ontology’s knowledge about a specified set of terms is a well-understood task. This task can be based, for instance, on locality-based modules. However, a single module does not allow us to understand neither topicality, connectedness, structure, or superfluous parts of an ontology, nor agreement between actual and intended modeling. The strong logical properties of locality-based modules suggest that the family of all such modules of an ontology can support comprehension of the ontology as a whole. However, extracting that family is not feasible, since the number of localitybased modules of an ontology can be exponential w.r.t. its size. In this paper we report on a new approach that enables us to efficiently extract a polynomial representation of the family of all locality-based modules of an ontology. We also describe the fundamental algorithm to pursue this task, and report on experiments carried out and results obtained. 1

and evaluate the progress of research projects funded by the Spanish Ministery of Science and Technology, JISBD has long since moved beyond its initial boundaries and crossed several oceans. Presently, the conference has become an important reference for younger researchers, as well as a forum which the more experienced do not wish to miss. In recent years, JISBD has broadened its radius, accepting papers also in English and Portuguese, in addition to Spanish. This change, not only brought more conference participants, but also significantly increased the number of submissions and, principally, the quality of the submissions accepted. The JISBD community is now self-sustained and continues to expand. The quality of work accepted is equivalent to that of other relevant international events. In recent years, it has been possible to edit a special volume of IEEE LA with extended versions of the best conference papers and this also is happening with the current edition. This special issue, together with the conference proceedings with ISBN, is a showcase of the quality of the work of JISBD. One of the highlights of this conference has been the excellence of its keynote speakers. Many of the most admired international researchers and professionals have already been invited to address

Data integration is complex often requiring much technical knowledge and expert understanding of the data and its meaning. In this paper we investigate the use of current semantic tools as an aid to data integration, and identify the need to modify these tools to meet the needs of spatial data. Illustrating the benefits of exposing the semantics of integration through creation of a demonstrator.

Effective communication in open environments relies on the ability of agents to reach a mutual understanding of the exchanged message by reconciling the vocabulary (ontology) used. Various approaches have considered how mutually acceptable mappings between corresponding concepts in the agents ’ own ontologies may be determined dynamically through argumentation-based negotiation (such as Meaning-based Argumentation). However, the complexity of this process is high, approaching