Abstract. Upper Ontologies are quickly becoming a key technology for integrating heterogeneous knowledge coming from different sources. In this technical report we analyse 7 Upper Ontologies, namely BFO, Cyc, DOLCE, GFO, PROTON, Sowa’s ontology, and SUMO, according to a set of standard software engineering criteria, and we synthesise our analysis in form of a comparative table. A summary of some existing comparisons drawn among subsets of the 7 Upper Ontologies that we deal with in this document, is also provided. 1

Abstract. This paper deals with a theoretical issue related to multi-agent system development and deployment, namely the need of a mechanism for aligning ontologies owned by agents, in order to allow them to communicate in a profitable way. Our approach exploits upper ontologies, i.e., ontologies which describe very general concepts that are the same across all domains, as a “lingua franca ” among agents. This approach may overcome some problems that arise in various real scenarios, such as the impossibility for (or the lack of will of) an agent to disclose its own entire ontology to another agent, despite the need to communicate with it. In this paper we propose a comparison of seven existing upper ontologies, and an algorithm for aligning any two (or more) ontologies by exploiting an upper ontology as a bridge. 1

This paper discusses aspects of context as applied to ontologies. In particular, we note some formalizations of context that have been applied to ontologies such as Menzel (1999) and Akman & Surov (1996, 1997), that have largely been framed in terms of theories such as Situation Theory (Barwise & Perry, 1983) which originated in natural language semantics. We also mention the notion of labeled deduction (Gabbay, 1996) and speculate on its prospective use in the contextualizing of ontologies. The latter can be viewed as a mechanism for annotating ontological assertions and proofs with contextual information about provenance, security, strength/confidence of assertion, and aspects of policy. Labeled deduction correlates one or more logics, with one logic addressing the primary assertion or

Abstract. Performing simple keyword-based search has long been the only way to access information. But for a truly comprehensive search on multimedia data, this approach is no longer su cient. Therefore semantic annotation is a key concern for an improvement of the relevance in image retrieval applications. In this paper 1 we propose a system architecture for an automatic large-scale medical image understanding which aims at a formal fusion of feature extraction techniques operating on the bit-level representation of images (and time series data) with formal background knowledge represented in ontologies. We put forward a hierarchical framework of ontologies to formulate a precise and at the same time generic representation of the existing high level knowledge in the medical domain. We present a system architecture which aims at an integration of high- and low-level features on various abstraction levels allowing cross-modal as well as cross-lingual retrieval through Content Based Image Retrieval (CBIR), query by keyword/text, and query by concept. Experiences from implementing a framework supporting these features are reported as well as our e orts to select and acquire relevant domain knowledge. 1

Abstract. Ontologies enable explicit expression of collective concepts and support Machine-to-Machine (M2M) interactions at the semantic level. Ontologies expressed in a standard language, such as the Web Ontology Language (OWL) and exposed on a network offer the potential for unprecedented interoperability solutions since they are semantically rich, computer interpretable and inherently extensible. In this paper, we describe how we applied ontologies in OWL for rapid enterprise integration of heterogeneous data sources. We found that once a robust foundational domain ontology is established, it is easy and quick to integrate new data sources and therefore rapidly provide new system capabilities.

This chapter introduces the general vision of the Social Semantic Desktop (SSD) and details it in the context of the NEPOMUK project. It outlines the typical SSD requirements and functionalities that were identified from real world scenarios. In addition, it provides the design of the standard SSD architecture together with the ontology pyramid developed to support it. Finally, the chapter gives an overview of some of the technical challenges that arise from the actual development process of the SSD.

de opiniones con ontologías

Abstract. A semantic wiki provides visualization of social media analysis applicable to military Information Operations and law enforcement counterterrorism efforts. Using inputs from disparate data sets, semantic software exports data to link analysis, geospatial displays, and temporal representation. Challenges encountered in software development include the balance between automated and human assisted entity extraction, interoperability with existing visualization systems and ontology management. 1

We present an approach for the semantic integration of geoscience data, and a system implementing this approach. Specifically, we demonstrate the use of data ontologies and application of markup languages for semantic integration of data and services. We introduce a domain level object ontology, called Earth and Planetary ONTology (EPONT) to explore, extract, and integrate information from heterogeneous geologic data sets. As proof of concept, we define the DIA engine, an extensible infrastructure for the Discovery, Integration, and Analysis of geoscience data, tools, and services. DIA provides a collaborative environment where scientists can share their resources (e.g., geochemical data, filtering services, etc.) by registering them through well-defined ontologies. We envision the DIA infrastructure to also use other classes of ontologies, namely process and service, for knowledge creation.