8,795 research outputs found
Business Process Retrieval Based on Behavioral Semantics
This paper develops a framework for retrieving business processes considering search requirements based on behavioral semantics properties; it presents a framework called "BeMantics" for retrieving business processes based on structural, linguistics, and behavioral semantics properties. The relevance of the framework is evaluated retrieving business processes from a repository, and collecting a set of relevant business processes manually issued by human judges. The "BeMantics" framework scored high precision values (0.717) but low recall values (0.558), which implies that even when the framework avoided false negatives, it prone to false positives. The highest pre- cision value was scored in the linguistic criterion showing that using semantic inference in the tasks comparison allowed to reduce around 23.6 % the number of false positives. Using semantic inference to compare tasks of business processes can improve the precision; but if the ontologies are from narrow and specific domains, they limit the semantic expressiveness obtained with ontologies from more general domains. Regarding the perform- ance, it can be improved by using a filter phase which indexes business processes taking into account behavioral semantics propertie
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Geospatial data integration with Semantic Web services: the eMerges approach
Geographic space still lacks the semantics allowing a unified view of spatial data. Indeed, as a unique but all encompassing domain, it presents specificities that geospatial applications are still unable to handle. Moreover, to be useful, new spatial applications need to match human cognitive abilities of spatial representation and reasoning. In this context, eMerges, an approach to geospatial data integration based on Semantic Web Services (SWS), allows the unified representation and manipulation of heterogeneous spatial data sources. eMerges provides this integration by mediating legacy spatial data sources to high-level spatial ontologies through SWS and by presenting for each object context dependent affordances. This generic approach is applied here in the context of an emergency management use case developed in collaboration with emergency planners of public agencies
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Technical Issues in the Development of Knowledge-Based Services for the Semantic Web
The Semantic Web aims to extend the current Web with formal semantics in order to improve how users experience the Web, by ameliorating current activities and supporting the automation of some others. So far, current Semantic Web prototypes mostly aim at collecting and exposing information. Still, a semantic layer can support applying Knowledge-Based Systems techniques to the development of brand-new fully-fledged Knowledge-Based Services for the Web. In this paper, we present the technical issues that have to be faced in the development of such a kind of application by presenting the Online Design of Events Application: a Semantic Web-based design support system that assists event organisers in the process of preparing events such as workshops and conferences, by effectively reasoning over an inter-organisational process across the Web
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FABilT – finding answers in a billion triples
This submission presents the application of two coupled systems to the Billion Triples Challenge. The first system (Watson) provides the infrastructure which allows the second one (PowerAqua) to pose natural language queries to the billion triple datasets. Watson is a gateway to the Semantic Web: it crawls and indexes semantic data online to provide a variety of access mechanisms for human users and applications.We show here how we indexed most of the datasets provided for the challenge, thus obtaining an infrastructure (comprising web services, API, web interface, etc.) which supports the exploration of these datasets and makes them available to any Watson-based application. PowerAqua is an open domain question answering system which allows users to pose natural language queries to large scale collections of heterogeneous semantic data. In this paper, we discuss the issues we faced in configuring
PowerAqua and Watson for the challenge and report on our results. The system composed of Watson and PowerAqua, and applied to the Billion Triples Challenge, is called FABilT
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