Journal on Data Semantics XII

Ontology Mapping is a mandatory requirement for enabling semantic interoperability among different agents and services relying on different ontologies. This aspect becomes more critical in Peer-to-Peer (P2P) networks for several reasons: (i) the number of different ontologies can dramatically increase; (ii) mappings among peer ontologies have to be discovered on the fly and only on the parts of ontologies “contextual” to a specific interaction in which peers are involved; (iii) complex mapping strategies (e.g., structural mapping based on graph matching) cannot be exploited since peers are not aware of one another’s ontologies. In order to address these issues, we developed a new ontology mapping algorithm called Semantic Coordinator (SECCO). SECCO is composed by three individual matchers: syntactic, lexical and contextual. The syntactic matcher, in order to discover mappings, exploits different kinds of linguistic information (e.g., comments, labels) encoded in ontology entities. The lexical matcher enables discovering mappings in a semantic way since it “interprets” the semantic meaning of concepts to be compared. The contextual matcher relies on a “how it fits” strategy, inspired by the contextual theory of meaning, and by taking into account the contexts in which the concepts to be compared are used refines similarity values. We show through experimental results that SECCO fulfills two important requirements: fastness and accuracy (i.e., quality of mappings). SECCO, differently from other semantic P2P applications (e.g., Piazza, GridVine) that assume the preexistence of mappings for achieving semantic interoperability, focuses on the problem of finding mappings. Therefore, if coupled with a P2P platform, it paves the way towards a comprehensive semantic P2P solution for content sharing and retrieval, semantic query answering and query routing. We report on the advantages of integrating SECCO in the K-link+ system.

In this paper, we investigate the problem of query rewriting using views in a hybrid language allowing nominals (i.e., individual names) to occur in intentional descriptions. Of particular interest, restricted form of nominals where individual names refer to simple values enable the specification of value constraints, i.e, sets of allowed values for attributes. Such constraints are very useful in practice enabling, for example, fine-grained description of queries and views in integration systems and thus can be exploited to reduce the query processing cost. We use description logics to formalize the problem of query rewriting using views in presence of value constraints and show that the technique of query rewriting can be used to process queries under the certain answer semantics. We propose a sound and complete query rewriting Bucket-like algorithm. Data mining techniques have been used to favor scalability w.r.t. the number of views. Experiments on synthetic datasets have been conducted.

The reference reconciliation problem consists in deciding whether different identifiers refer to the same data, i.e. correspond to the same real world entity. In this article we present a reference reconciliation approach which combines a logical method for reference reconciliation called L2R and a numerical one called N2R. This approach exploits the schema and data semantics, which is translated into a set of Horn FOL rules of reconciliation. These rules are used in L2R to infer exact decisions both of reconciliation and non-reconciliation. In the second method N2R, the semantics of the schema is translated in an informed similarity measure which is used by a numerical computation of the similarity of reference pairs. This similarity measure is expressed in a non linear equation system, which is solved by using an iterative method. The experiments of the methods made on two different domains, show good results for both recall and precision. They can be used separately or in combination. We have shown that their combination allows to improve runtime performance.

We present a novel approach to probabilistic description logic programs for the Semantic Web in which disjunctive logic programs under the answer set semantics are tightly coupled with description logics and Bayesian probabilities. The approach has several nice features. In particular, it is a logic-based representation formalism that naturally fits into the landscape of Semantic Web languages. Tightly coupled probabilistic description logic programs can especially be used for representing mappings between ontologies, which are a common way of approaching the semantic heterogeneity problem on the Semantic Web. In this application, they allow in particular for resolving inconsistencies and for merging mappings from different matchers based on the level of confidence assigned to different rules. Furthermore, tightly coupled probabilistic description logic programs also provide a natural integration of ontologies, action languages, and Bayesian probabilities towards Web Services. We explore the computational aspects of consistency checking and query processing in tightly coupled probabilistic description logic programs. We show that these problems are decidable and computable, respectively, and that they can be reduced to consistency checking and cautious/brave reasoning, respectively, in tightly coupled disjunctive description logic programs. Using these results, we also provide an anytime algorithm for tight query processing. Furthermore, we analyze the complexity of consistency checking and query processing in the new probabilistic description logic programs, and we present a special case of these problems with polynomial data complexity.

The meaning of concepts and views defined over a database ontology can be considered as intensional objects which have a particular extension in a given possible world: for instance in the actual world. Thus, non invasive mapping between completely independent peer databases in a P2P systems can be naturally specified by the set of couples of views, which have the same meaning (intension), over two different peers. Such a kind of mapping has very different semantics from standard view-based mappings based on material implication, commonly used for Data Integration Systems. The introduction of an intensional equivalence generates the quotient intensional FOL fundamental for a query answering in P2P systems. In this paper we introduce this formal intensional FOL by fusing Bealer’s intensional algebraic FOL with a possible-world semantics of the Montague’s FOL modal approach to natural language. We modify the Bealer’s intensional algebra in order to deal with relational databases and views, by introducing the join operation of relational algebra. Then we adopt the S5 Kripke frame in order to define an intensional equivalence relation between views for peer databases. Finally, we define an embedding of P2P database system into this quotient intensional FOL, and the computing of its extensionalization mapping in the actual Montague’s world.

Although business process management has been a major area of ICT research, no coherent approach has been developed to address the problem of business process visualisation to aid workers in the process of task prioritisation. In this paper we describe the development of a new, coherent approach to worklist visualisation, via analysis and development of a resource-centric view of the worklist information. We use instances of generic resource types as workflow elements that may be considered by workers when interacting with worklists. We then propose a generic 2D framework for visualising the resources, creating an effective mapping between a task and the capabilities of the resources. This aims to aid the process of task selection and prioritisation by workers. A worklist visualisation system has been implemented as an extension to an open-source workflow system, YAWL (Yet Another Workflow Language).