1,721,224 research outputs found
IntroductionSemantic Web Information Management
No full textIn the most recent years, the Semantic Web has become a most promising research field, which tries to automate and support sharing and reuse of the data and metadata representing the growing amount of digital information available to our society. The underlying idea of having a description of the data on the Web, in such a way that it can be employed by machines for automation, integration and reuse across various applications, has been exploited in several research fields. However, the gigantic amount of such useful information makes more and more difficult its efficient management, undermining the possibility to transform it into useful knowledge
Model Checking Multiagent Systems (Extend Abstract)
No full text) Massimo Benerecetti 1 , Fausto Giunchiglia 1;2 , Luciano Serafini 2 1 DISA - University of Trento, Via Inama 13, 38050 Trento, Italy 2 IRST - Istituto Trentino di Cultura, 38050 Povo, Trento, Italy [email protected] ffausto,[email protected] Abstract. Model checking is a very successful technique which has been applied in the design and verification of finite state concurrent reactive processes. In this paper we show how this technique can be lifted to be applicable to multiagent systems. Our approach allows us to reuse the technology and tools developed in model checking, to design and verify multiagent systems in a modular and incremental way, and also to have a very efficient model checking algorithm. 1 Introduction Model checking is a very successful automatic technique which has been devised for the design and verification of finite state reactive systems, e.g., sequential circuit designs, communication protocols, and safety critical control systems (see, e.g., [2])..
Modeling and Using Context - 4th International and Interdisciplinary Conference CONTEXT 2003
No full textThe Internet of Musical Things Ontology
Full text linkThe Internet of Musical Things (IoMusT) is an emerging research area consisting of the extension of the Internet of Things paradigm to the music domain. Interoperability represents a central issue within this domain, where heterogeneous objects dedicated to the production and/or reception of musical content (Musical Things) are envisioned to communicate between each other. This paper proposes an ontology for the representation of the knowledge related to IoMusT ecosystems to facilitate interoperability between Musical Things. There was no previous comprehensive data model for the IoMusT domain, however the new ontology relates to existing ontologies, including the SOSA Ontology for the representation of sensors and actuators and the Music Ontology focusing on the production and consumption of music. This paper documents the design of the ontology and its evaluation with respect to specific requirements gathered from an extensive literature review, which was based on scenarios involving IoMusT stakeholders, such as performers and audience members. The IoMusT Ontology can be accessed at: https://w3id.org/iomust#
Semantic matching with S-Match
Full text linkWe view matching as an operation that takes two graph-like structures (e.g., lightweight ontologies) and produces an alignment between the nodes of these graphs that correspond semantically to each other. Semantic matching is based on two ideas: (i) we discover an alignment by computing semantic relations (e.g., equivalence, more general); (ii) we determine semantic relations by analyzing the meaning (concepts, not labels) which is codied in the entities and the structures of ontologies. In this chapter we first overview the state of the art in the ontology matching eld. Then, we present basic and optimized algorithms for semantic matching as well as their implementation within the S-Match system. Finally, we evaluate S-Match against state of the art systems, thereby justifying empirically the strength of the approach. To appear in Roberto De Virgilio, Fausto Giunchiglia, and Letizia Tanca (eds.), "Semantic Web Information Management: a model based perspective", Springer, 2009. - http://www.springerlink.co
Classifications
No full textAbstract. There is a huge amount of information scattered on the World Wide Web. As the information flow occurs at a high speed in the WWW, there is a need to organize it in the right manner so that a user can access it very easily. Previously the organization of information was generally done manually, by matching the document contents to some pre-defined categories. There are two approaches for this text-based categorization: manual and automatic. In the manual approach, a human expert performs the classification task, and in the second case supervised classifiers are used to automatically classify resources. In a supervised classification, manual interaction is required to create some training data before the automatic classification task takes place. In our new approach, we intend to propose automatic classification of documents through semantic keywords and building the formulas generation by these keywords. Thus we can reduce this human participation by combining the knowledge of a given classification and the knowledge extracted from the data. The main focus of this PhD thesis, supervised by Prof. Fausto Giunchiglia, is the automatic classification of documents into user-generated classifications. The key benefits foreseen from this automatic document classification is not only related to search engines, but also to many other fields like, document organization, text filtering, semantic index managing
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