{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:28:19Z","timestamp":1767637699671,"version":"3.48.0"},"reference-count":28,"publisher":"Maximum Academic Press","issue":"2","license":[{"start":{"date-parts":[[2013,2,18]],"date-time":"2013-02-18T00:00:00Z","timestamp":1361145600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The Knowledge Engineering Review"],"published-print":{"date-parts":[[2013,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This paper presents PORSCE II, an integrated system that performs automatic Semantic Web service composition exploiting artificial intelligence (AI) techniques, specifically planning. Essential steps in achieving Web service composition include the translation of the Web service composition problem into a solver-ready planning domain and problem, followed by the acquisition of solutions, and the translation of the solutions back to Web service terms. The solutions to the problem, that is, the descriptions of the desired composite service, are obtained by means of external domain-independent planning systems, they are visualized and finally evaluated. Throughout the entire process, the system exploits semantic information extracted from the semantic descriptions of the available Web services and the corresponding ontologies, in order to perform composition under semantic awareness and relaxation.<\/jats:p>","DOI":"10.1017\/s0269888912000392","type":"journal-article","created":{"date-parts":[[2013,2,19]],"date-time":"2013-02-19T00:54:14Z","timestamp":1361235254000},"page":"137-156","source":"Crossref","is-referenced-by-count":26,"title":["The PORSCE II framework: using AI planning for automated Semantic Web service composition"],"prefix":"10.48130","volume":"28","author":[{"given":"Ourania","family":"Hatzi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitris","family":"Vrakas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nick","family":"Bassiliades","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimosthenis","family":"Anagnostopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis","family":"Vlahavas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"27968","published-online":{"date-parts":[[2013,2,18]]},"reference":[{"key":"S0269888912000392_ref15","unstructured":"McDermott D. 2002. 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In PKDD '02: Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, Elomaa, T., Mannila, H. & Toivonen, H. (eds). London, UK, 348\u2013360. Springer-Verlag.","DOI":"10.1007\/3-540-45681-3_29"},{"key":"S0269888912000392_ref10","unstructured":"Hatzi O. , Vrakas D. , Bassiliades N. , Anagnostopoulos D. , Vlahavas I. 2007. VLEPPO: A visual language for problem representation. In PlanSIG 2007: The 26th workshop of the UK Planning and Scheduling Special Interest Group, Bartak, R. (ed.), 60\u201366."},{"key":"S0269888912000392_ref20","doi-asserted-by":"crossref","unstructured":"Paolucci M. , Ankolekar A. , Srinivasan N. , Sycara K. 2003. The DAML-S virtual machine. In The SemanticWeb\u2014ISWC 2003, Lecture Notes in Computer Science 2870, 290\u2013305. 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