Matching Semantic Service Descriptions with Local Closed-World Reasoning

Abstract

Abstract. Semantic Web Services were developed with the goal of automating the integration of business processes on the Web. The main idea is to express the functionality of the services explicitly, using semantic annotations. Such annotations can, for example, be used for service discovery-the task of locating a service capable of fulfilling a business request. In this paper, we present a framework for annotating Web Services using description logics (DLs), a family of knowledge representation formalisms widely used in the Semantic Web. We show how to realise service discovery by matching semantic service descriptions, applying DL inferencing. Building on our previous work, we identify problems that occur in the matchmaking process due to the open-world assumption when handling incomplete service descriptions. We propose to use autoepistemic extensions to DLs (ADLs) to overcome these problems. ADLs allow for non-monotonic reasoning and for querying DL knowledge bases under local closed-world assumption. We investigate the use of epistemic operators of ADLs in service descriptions, and show how they affect DL inferences in the context of semantic matchmaking.

Dl-Link: A Conceptual Clustering Algorithm for Indexing Description Logics Knowledge Bases

“…An interesting further development of the presented method would be its evaluation by employing the Entailment of Concept Non-Disjointness match test [20] that, for the best of our knowledge has never been implemented and experimentally evaluated. Further improvements could be obtained along a number of directions: 1) merging more than two descriptions at each step during the construction of the DL-Tree to speed-up the clustering process; 2) building the DL-Tree using a top-down divisional clustering approach that ensures the disjointness of the nodes in the tree at the same level; 3) implementing an incremental clustering algorithm to cope with the availability of new resources; 4) assigning intensional cluster descriptions by exploiting supervised learning methods [18].…”

“…Several real ontologies, freely available on the Web, have been exploited for the evaluation of our method: (a) SWSD p , is a collection of DL-based service descriptions, referring to a knowledge base, generated by following the framework proposed in [20]; (b) WINE, an ontology q describing the wine and food domain; (c) UNIVERSITY, an ontology, built from the Lehigh University Benchmark r , describing the academic domain; (d) FINANCIAL, an ontology s that was employed as a testbed for Pellet, describing the financial domain; (e) NTN, an ontology t describing the domain of the Bible; (f) BIOPAX, an ontology u describing the biological data domain. Details on such ontologies are reported in Tab.…”

“…An alternative matching function has been proposed in [20], it is called Entailment k If the test Q C is adopted, after retrieving the resource descriptions satisfying Q, the instances of C that are really instances of Q have to be determined (see end of Sect. 5.1) since C is more general than Q.…”

Semantic Matchmaking of Web Resources with Local Closed-World Reasoning

“…In a way similar to [17,18], we show how we map the structured description of abstract resource classes to the DL modelling constructs introduced in Section 2 by an example within the eCommerce PC product catalogue scenario. Figure 1 shows a resource description in form of a DL concept expression, either issued by a provider who supplies a PC, or by a requester who demands a PC from an electronically available product catalogue.…”

“…While satisfiability of concept conjunction is a rather weak check, entailment of non-disjointness is quite strict in that it requires the modelers of resource descriptions to exclude any interpretation in which an intended resource is not captured, which is usually achieved by including additional restrictions into the resource description. It is an attempt to cope with some problems in matching due to the open-world semantics, which we discuss in the following, and for which we propose a better solution through support of local-closed world reasoning, based on earlier work in [18]. For this reason, we focus on satisfiability of concept conjunction and mean this inference when we speak of intersection matching in the sequel.…”