Abstract. Service interface description languages such as WSDL, and related standards, are evolving rapidly to provide a foundation for interoperation between Web services. At the same time, Semantic Web service technologies, such as the Ontology Web Language for Services (OWL-S), are developing the means by which services can be given richer semantic specifications. Richer semantics can enable fuller, more flexible automation of service provision and use, and support the construction of more powerful tools and methodologies. Both sets of technologies can benefit from complementary uses and crossfertilization of ideas. This paper shows how to use OWL-S in conjunction with Web service standards, and explains and illustrates the value added by the semantics expressed in OWL-S.

Current industry standards for describing Web Services focus on ensuring interoperability across diverse platforms, but do not provide a good foundation for automating the use of Web Services. Representational techniques being developed for the Semantic Web can be used to augment these standards. The resulting Web Service specifications enable the development of software programs that can interpret descriptions World Wide

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Abstract. In Linked Data, the use of owl:sameAs is ubiquitous in interlinking data-sets. There is however, ongoing discussion about its use, and potential misuse, particularly with regards to interactions with inference. In fact, owl:sameAs can be viewed as encoding only one point on a scale of similarity, one that is often too strong for many of its current uses. We describe how referentially opaque contexts that do not allow inference exist, and then outline some varieties of referentially-opaque alternatives to owl:sameAs. Finally, we report on an empirical experiment over randomly selected owl:sameAs statements from the Web of data. This theoretical apparatus and experiment shed light upon how owl:sameAs is being used (and misused) on the Web of data.

As adaptive agents become more complex and take increasing autonomy in their user's lives, it becomes more important for users to trust and understand these agents. Little work has been done, however, to study what factors influence the level of trust users are willing to place in these agents. Without trust in the actions and results produced by these agents, their use and adoption as trusted assistants and partners will be severely limited. We present the results of a study among test users of CALO, one such complex adaptive agent system, to investigate themes surrounding trust and understandability. We identify and discuss eight major themes that significantly impact user trust in complex systems. We further provide guidelines for the design of trustable adaptive agents. Based on our analysis of these results, we conclude that the availability of explanation capabilities in these agents can address the majority of trust concerns identified by users.

DAML+OIL's goal is to support the transformation of the Web from being a forum for information presentation to a resource for interoperability, understanding, and reasoning.

Abstractclassic is a data model that encourages the description o f o b j e cts not only in terms of their relations to other known objects, but in terms of a level of intensional structurea sw ell. The classic language of structured descriptions permits i partial descriptions of individuals, under an`open world' assumption, ii answers to queries either as extensional lists of valueso ra sd e s criptions that necessarily hold of all possible answers, and iiia n easily extensible s c hema, which can be accessed uniformly with the data. One of the strengths of the approach is that the same language plays multiple roles in the processes of de ning and populating the DB, as well as querying and answering.classic for which w e h a ve a prototype main-memory implementation can actively discover new information about objects from several sources: it can recognize new classes under which an object falls based on a description of the object, it can propagate some deductive consequences of DB updates, it has simple procedural recognizers, and it supports a limited form of forward-chaining rules to derive new conclusions about known objects.The kind of language of descriptions and queries presented here provides a new arena for the search for languages that are more expressive than conventional DBMS languages, but for which query processing is still tractable. This spaceo f languages di ers from the subsets of predicate calculus hitherto exploredb y deductive databases. MotivationA database is normally used to maintain a model of some aspect of reality. T raditional data models, such as the relational one,h a vea c hieved great e ciency in data storage and retrieval by restricting m o d e ling power; in particular, the database is assumed to be a complete and accurate model of the world, where all the individual objects are restricted With Department of Computer Science, Rutgers University, New Brunswick, NJ 08903. 1 to be primitivev alues liken umbers and strings, and all their inter-relationships are known and expressly stated. While undeniably of extensive v alue, this makes traditional data models unsuitable for a number of situations, for example, when complex objects are the naturalw ay of describing the domain; when information about the domaini sincomplete or becomesa vailable incrementally; when the database should be taking a more active role in deducing relationships rather than being just a passive repository of data. These situations include those in which new artifacts are being designed e.g., CAD CAM, con guration, or an understanding of some existing situation is being builtu po ver time e.g., diagnostic situations.The eldo flogic or deductive databases 14 has emerged as one response to some of these weaknesses: incomplete information can be expressed naturally in logical languages using disjunction and existential quanti ers, and the database can infer new relationships through deductive rules. The chief drawback of this approach is computationalintractability: a generalv ersion of this problem is equivale...

Abstractclassic is a data model that encourages the description o f o b j e cts not only in terms of their relations to other known objects, but in terms of a level of intensional structurea sw ell. The classic language of structured descriptions permits i partial descriptions of individuals, under an`open world' assumption, ii answers to queries either as extensional lists of valueso ra sd e s criptions that necessarily hold of all possible answers, and iiia n easily extensible s c hema, which can be accessed uniformly with the data. One of the strengths of the approach is that the same language plays multiple roles in the processes of de ning and populating the DB, as well as querying and answering.classic for which w e h a ve a prototype main-memory implementation can actively discover new information about objects from several sources: it can recognize new classes under which an object falls based on a description of the object, it can propagate some deductive consequences of DB updates, it has simple procedural recognizers, and it supports a limited form of forward-chaining rules to derive new conclusions about known objects.The kind of language of descriptions and queries presented here provides a new arena for the search for languages that are more expressive than conventional DBMS languages, but for which query processing is still tractable. This spaceo f languages di ers from the subsets of predicate calculus hitherto exploredb y deductive databases. MotivationA database is normally used to maintain a model of some aspect of reality. T raditional data models, such as the relational one,h a vea c hieved great e ciency in data storage and retrieval by restricting m o d e ling power; in particular, the database is assumed to be a complete and accurate model of the world, where all the individual objects are restricted With Department of Computer Science, Rutgers University, New Brunswick, NJ 08903. 1 to be primitivev alues liken umbers and strings, and all their inter-relationships are known and expressly stated. While undeniably of extensive v alue, this makes traditional data models unsuitable for a number of situations, for example, when complex objects are the naturalw ay of describing the domain; when information about the domaini sincomplete or becomesa vailable incrementally; when the database should be taking a more active role in deducing relationships rather than being just a passive repository of data. These situations include those in which new artifacts are being designed e.g., CAD CAM, con guration, or an understanding of some existing situation is being builtu po ver time e.g., diagnostic situations.The eldo flogic or deductive databases 14 has emerged as one response to some of these weaknesses: incomplete information can be expressed naturally in logical languages using disjunction and existential quanti ers, and the database can infer new relationships through deductive rules. The chief drawback of this approach is computationalintractability: a generalv ersion of this problem is equivale...