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Anupriya Ankolekar
Researcher at Hewlett-Packard
Publications - 57
Citations - 5393
Anupriya Ankolekar is an academic researcher from Hewlett-Packard. The author has contributed to research in topics: Semantic Web & Semantic Web Stack. The author has an hindex of 22, co-authored 56 publications receiving 5348 citations. Previous affiliations of Anupriya Ankolekar include Karlsruhe Institute of Technology & Carnegie Mellon University.
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Proceedings Article
DAML-S: semantic markup for web services
Anupriya Ankolekar,Mark Burstein,Jerry R. Hobbs,Ora Lassila,David Martin,Sheila A. McIlraith,Srini Narayanan,Massimo Paolucci,Terry R. Payne,Katia Sycara,Honglei Zeng +10 more
TL;DR: The overall structure of the ontology, the service profile for advertising services, and the process model for the detailed description of the operation of services are described, which compare DAML-S with several industry efforts to define standards for characterizing services on the Web.
Journal ArticleDOI
Automated discovery, interaction and composition of Semantic Web services
TL;DR: A vision for Semantic Web Services, which combine the growing Web services architecture and theSemantic Web, is introduced and DAML-S is proposed as a prototypical example of an ontology for describing SemanticWeb services.
Proceedings ArticleDOI
The two cultures: mashing up web 2.0 and the semantic web
TL;DR: This paper outlines a semantic weblogs scenario that illustrates the potential for combining Web 2.0 and Semantic Web technologies, while highlighting the unresolved issues that impede its realization.
Journal ArticleDOI
The two cultures: Mashing up Web 2.0 and the Semantic Web
TL;DR: This paper outlines a semantic weblogs scenario that illustrates the potential for combining Web 2.0 and Semantic Web technologies, while highlighting the unresolved issues that impede its realization.
Proceedings ArticleDOI
Preference-based selection of highly configurable web services
TL;DR: This paper presents an OWL ontology for the specification of configurable Web service offers and requests, and a flexible and extensible framework for optimal service selection that combines declarative logic-based matching rules with optimization methods, such as linear programming.