Naveen Srinivasan

Bio: Naveen Srinivasan is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Web service & OWL-S. The author has an hindex of 13, co-authored 15 publications receiving 3236 citations. Previous affiliations of Naveen Srinivasan include Artificial Intelligence Center & NTT DoCoMo.

Bringing semantics to web services: the OWL-S approach

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 cross-fertilization 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.

Bringing Semantics to Web Services with OWL-S

Abstract: 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 of unfamiliar Web Services and then employ those services to satisfy user goals. OWL-S ("OWL for Services") is a set of notations for expressing such specifications, based on the Semantic Web ontology language OWL. It consists of three interrelated parts: a profile ontology, used to describe what the service does; a process ontology and corresponding presentation syntax, used to describe how the service is used; and a grounding ontology, used to describe how to interact with the service. OWL-S can be used to automate a variety of service-related activities involving service discovery, interoperation, and composition. A large body of research on OWL-S has led to the creation of many open-source tools for developing, reasoning about, and dynamically utilizing Web Services.

Adding OWL-S to UDDI, implementation and throughput

Abstract: The increasing availability of web services demands for a discovery mechanism to find services that satisfy our requirement. UDDI provides a web wide registry of web services, but its lack of an explicit capability representation and its syntax based search provided produces results that are coarse in nature. We propose to base the discovery mechanism on OWL-S. OWL-S allows us to semantically describe web services in terms of capabilities offered and to perform logic inference to match the capabilities requested with the capabilities offered. We propose OWL-S/UDDI matchmaker that combines the better of two technologies. We also implemented and analyzed its performance.

An efficient algorithm for OWL-S based semantic search in UDDI

Abstract: The increasing availability of web services demands for a discovery mechanism to find services that satisfy our requirement. UDDI provides a web wide registry of web services, but its lack of an explicit capability representation and its syntax based search provided produces results that are coarse in nature. We propose to base the discovery mechanism on OWL-S. OWL-S allows us to semantically describe web services in terms of capabilities offered and to perform logic inference to match the capabilities requested with the capabilities offered. We propose OWL-S/UDDI matchmaker that combines the better of two technologies. We also implemented and analyzed its performance.

Web Service Modeling Ontology

Abstract: The potential to achieve dynamic, scalable and cost-effective marketplaces and eCommerce solutions has driven recent research efforts towards so-called Semantic Web Services that are enriching Web services with machine-processable semantics. To this end, the Web Service Modeling Ontology (WSMO) provides the conceptual underpinning and a formal language for semantically describing all relevant aspects of Web services in order to facilitate the automatization of discovering, combining and invoking electronic services over the Web. In this paper we describe the overall structure of WSMO by its four main elements: ontologies, which provide the terminology used by other WSMO elements, Web services, which provide access to services that, in turn, provide some value in some domain, goals that represent user desires, and mediators, which deal with interoperability problems between different WSMO elements. Along with introducing the main elements of WSMO, we provide a logical language for defining formal statements in WSMO together with some motivating examples from practical use cases which shall demonstrate the benefits of Semantic Web Services.

Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems

Abstract: When I started out as a newly hatched PhD student, back in the day, one of the first articles I read and understood (or at least thought that I understood) was Ray Reiter’s classic article on default logic (Reiter, 1980).This was some years after the famous ‘non-monotonic logic’ issue of Artificial Intelligence in which that article appeared, but default logic was still one of the leading approaches, a tribute to the simplicity and power of the theory. As a result of reading the article, I became fascinated by both default logic and, more generally, non-monotonic logics. However, despite my fascination, these approaches never seemed terribly useful for the kinds of problem that I was supposed to be studying—problems like those in medical decision making—and so I eventually lost interest. In fact non-monotonic logics seemed to me, and to many people at the time I think, not to be terribly useful for anything. They were interesting, and clearly relevant to the long-term goals of Artificial Intelligence as a discipline, but not of any immediate practical importance. This verdict, delivered at the end of the 1980s, continued, I think, to be true for the next few years while researchers working in non-monotonic logics studied problems that to outsiders seemed to be ever more obscure. However, by the end of the 1990s, it was becoming clear, even to folk as short-sighted as I, that non-monotonic logics were getting to the point at which they could be used to solve practical problems. Knowledge in action shows quite how far these techniques have come. The reason that non-monotonic logics were invented was, of course, in order to use logic to reason about the world. Our knowledge of the world is typically incomplete, and so, in order to reason about it, one has to make assumptions about things one does not know. This, in turn, requires mechanisms for both making assumptions and then retracting them if and when they turn out not to be true. Non-monotonic logics are intended to handle this kind of assumption making and retracting, providing a mechanism that has the clean semantics of logic, but which has a non-monotonic set of conclusions. Much of the early work on non-monotonic logics was concerned with theoretical reasoning, that is reasoning about the beliefs of an agent—what the agent believes to be true. Theoretical reasoning is the domain of all those famous examples like ‘Typically birds fly. Tweety is a bird, so does Tweety fly?’, and the fact that so much of non-monotonic reasoning seemed to focus on theoretical reasoning was why I lost interest in it. I became much more concerned with practical reasoning—that is reasoning about what an agent should do—and non-monotonic reasoning seemed to me to have nothing interesting to say about practical reasoning. Of course I was wrong. When one tries to formulate any kind of description of the world as the basis for planning, one immediately runs into applications of non-monotonic logics, for example in keeping track of the state of a changing world. It is this use of non-monotonic logic that is at the heart of Knowledge in action. Building on the McCarthy’s situation calculus, Knowledge in action constructs a theory of action that encompasses a very large part of what an agent requires to reason about the world. As Reiter says in the final chapter,

Bringing semantics to web services: the OWL-S approach

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 cross-fertilization 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.

A framework and ontology for dynamic Web services selection

Abstract: Current Web services standards lack the means for expressing a service's nonfunctional attributes - namely, its quality of service. QoS can be objective (encompassing reliability, availability, and request-to-response time) or subjective (focusing on user experience). QoS attributes are key to dynamically selecting the services that best meet user needs. This article addresses dynamic service selection via an agent framework coupled with a QoS ontology. With this approach, participants can collaborate to determine each other's service quality and trustworthiness.

Web Service Semantics - WSDL-S

Abstract: The current WSDL standard operates at the syntactic level and lacks the semantic expressivity needed to represent the requirements and capabilities of Web Services. Semantics can improve software reuse and discovery, significantly facilitate composition of Web services and enable integrating legacy applications as part of business process integration. The Web Service Semantics technical note defines a mechanism to associate semantic annotations with Web services that are described using Web Service Description Language (WSDL). It is conceptually based on, but a significant refinement in details of, the original WSDL-S proposal [WSDL-S] from the LSDIS laboratory at the University of Georgia. In this proposal, we assume that formal semantic models relevant to the services already exist. In our approach, these models are maintained outside of WSDL documents and are referenced from the WSDL document via WSDL extensibility elements. The type of semantic information that would be useful in describing a Web Service encompass the concepts defined by the semantic Web community in OWL-S [OWL-S] and other efforts [METEOR-S, WSMO]. The semantic information specified in this document includes definitions of the precondition, input, output and effects of Web service operations. This approach offers multiple advantages over OWL-S. First, users can describe, in an upwardly compatible way, both the semantics and operation level details in WSDLa language that the developer community is familiar with. Secondly, by externalizing the semantic domain models, we take an agnostic approach to ontology representation languages. This allows Web service developers to annotate their Web services with their choice of ontology language (such as UML or OWL) unlike in OWL-S. This is significant because the ability to reuse existing domain models expressed in modeling languages like UML can greatly alleviate the need to separately model semantics. Finally, it is relatively easy to update the existing tooling around WSDL specification to accommodate our incremental approach. Status This is a technical note provided for discussion purposes and to elicit feedback on approaches to adding semantics to Web services descriptions. Table of