Towards intelligent web services: the web service modeling ontology (WSMO)

The Semantic Web and the Semantic Web Services build a natural application area for Intelligent Agents, namely querying and reasoning about structured knowledge and semantic descriptions of services and their interfaces on the Web. This paper provides an overview of the Web Service Modeling Ontology, a conceptual framework for the semantical description of Web services

A Framework for Design and Composition of Semantic Web Services

Semantic Web Services (SWS) are Web Services (WS) whose description is semantically enhanced with markup languages (e.g., OWL-S). This semantic description will enable external agents and programs to discover, compose and invoke SWSs. However, as a previous step to the specification of SWSs in a language, it must be designed at a conceptual level to guarantee its correctness and avoid inconsistencies among its internal components. In this paper, we present a framework for design and (semi) automatic composition of SWSs at a language-independent and knowledge level. This framework is based on a stack of ontologies that (1) describe the different parts of a SWS; and (2) contain a set of axioms that are really design rules to be verified by the ontology instances. Based on these ontologies, design and composition of SWSs can be viewed as the correct instantiation of the ontologies themselves. Once these instances have been created they will be exported to SWS languages such as OWL-S

Ontology of core data mining entities

In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines themost essential datamining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend

WSMO-lite annotations for web services

and other research output

An Introduction to Mechanized Reasoning

Mechanized reasoning uses computers to verify proofs and to help discover new theorems. Computer scientists have applied mechanized reasoning to economic problems but -- to date -- this work has not yet been properly presented in economics journals. We introduce mechanized reasoning to economists in three ways. First, we introduce mechanized reasoning in general, describing both the techniques and their successful applications. Second, we explain how mechanized reasoning has been applied to economic problems, concentrating on the two domains that have attracted the most attention: social choice theory and auction theory. Finally, we present a detailed example of mechanized reasoning in practice by means of a proof of Vickrey's familiar theorem on second-price auctions