MultiFarm: A benchmark for multilingual ontology matching

In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish – we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism

Distributed Reasoning Services for Multiple Ontologies

The main goal of this paper is to propose a distributed paradigm for reasoning with multiple ontologies connected by semantic mappings. The contribution of the paper to this goal is twofold. From the theoretical point of view we characterize the problem of global subsumption (i.e. the problem of subsumption in a set of local ontologies connected by semantic mappings) as a suitable fixpoint combination of operators that compute subsumptions in the local ontologies. This allows us to define a sound and complete algorithm for global subsumptions which calls black-boxes sub-routines for local subsumptions. The second contribution is the description of a prototype implementation of such algorithm in a peer-to-peer architecture

Instance Migration in Heterogeneous Ontology Environments

Abstract. In this paper we address the problem of migrating instances between heterogeneous overlapping ontologies. The instance migration problem arises when one wants to reclassify a set of instances of a source ontology into a semantically related target ontology. Our approach exploits mappings between ontologies, which are used to reconcile both conceptual and individual level heterogeneity, and further used to draw the migration process. We ground the approach on a distributed description logic (DDL), in which ontologies are formally encoded as DL knowledge bases and mappings as bridge rules and individual correspondences. From the theoretical side, we study the task of reasoning with instance data in DDL composed of SHIQ ontologies and define a correct and complete distributed tableaux inference procedure. From the practical side, we upgrade the DRAGO DDL reasoner for dealing with instances and further show how it can be used to drive the migration of instances between heterogeneous ontologies.

Reasoning with Instances in Distributed Description Logics

This report contributes to the study of a logical framework of distributed description logics (DDL) designed to formally capture the environments composed from multiple ontologies interrelated by semantic mappings. In such a framework a distributed knowledge base consists of a family of standard DL knowledge bases corresponding to each given ontology, a set of bridge rules corresponding to mapping between pairs of terminologies (T-boxes) and individual correspondences corresponding to rules for mapping individuals across instance storages (A-boxes). The main objective of this study is to investigate the problem of reasoning with instances in DDL. In particular, we disclose the role of mappings in reasoning, give a logical characterization to this role, and on the base of the given characterization define and implement a distributed tableaux reasoner DRAGO

Distributed Instance Retrieval in Heterogeneous Ontologies

The problem of ontology querying is of a crucial importance for a practically usable semantic web. Recent research in standard Description Logics has produced a number of algorithms and implementations for reasoning with instances for answering ontological queries. In thi

Modeling contextualized knowledge

Most of the knowledge available in the Semantic Web is context dependent. Examples of contextual information that is associated with knowledge are time, topic, provenance, reliability, etc. Recently, several paradigms, tools and languages have been proposed with the aim of adding context awareness into the Semantic Web. That is, enabling representation and reasoning not only with the knowledge alone, but also with the associated contextual information. Examples include RDF quadruples, named graphs, annotated RDF, and contextualized knowledge repositories. These new paradigms introduce a new dimension into knowledge engineering: in addition to individuals, concepts, properties and their relations, we also need to define the set of contexts, and we need to “split ” the knowledge between these contexts. In this paper, we propose a modeling exercise with one of the tools, for which we choose the contextualized knowledge repository. The example is complex enough to highlight many issues connected with contextualized knowledge representation, and it could possibly become the first benchmark for contextual knowledge representation tools