Andrei Tamilin

DRAGO: distributed reasoning architecture for the semantic web

The paper addresses the problem of reasoning with multiple ontologies interconnected by semantic mappings. This problem is becoming more and more relevant due to the necessity of building the interoperable Semantic Web. In contrast to the so called global reasoning approach, in this paper we propose a distributed reasoning technique that accomplishes reasoning through a combination of local reasoning chunks, internally executed in each separate ontology. Using Distributed Description Logics as a formal framework for representation of multiple semantically connected ontologies, we define a sound and complete distributed tableau-based reasoning procedure which is built as an extension to standard Description Logic tableau. Finally, the paper describes the design and implementation principles of a distributed reasoning system, called DRAGO (Distributed Reasoning Architecture for a Galaxy of Ontologies), that implements such distributed decision procedure.

Reasoning Support for Mapping Revision

Finding correct semantic correspondences between heterogeneous ontologies is one of the most challenging problems in the area of semantic web technologies. As manually constructing such mappings is not feasible in realistic scenarios, a number of automatic matching tools have been developed that propose mappings based on general heuristics. As these heuristics often produce incorrect results, a manual revision is inevitable in order to guarantee the quality of generated mappings. Experiences with benchmarking matching systems revealed that the manual revision of mappings is still a very difficult problem because it has to take the semantics of the ontologies as well as interactions between mappings into account. In this article, we propose methods for supporting human experts in the task of revising automatically created mappings. In particular, we present non-standard reasoning methods for detecting and propagating implications of expert decisions on the correctness of a mapping.

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.

Logical Analysis of Mappings between Medical Classification Systems

Medical classification systems provide an essential instrument for unambiguously labeling clinical concepts in processes and services in healthcare and for improving the accessibility and elaboration of the medical content in clinical information systems. Over the last two decades the standardization efforts have established a number of classification systems as well as conversion mappings between them. Although these mappings represent the agreement reached between human specialists who devised them, there is no explicit formal reference establishing the precise meaning of the mappings. In this work we close this semantic gap by applying the results that have been recently reached in the area of AI and the Semantic Web on the formalization and analysis of mappings between heterogeneous conceptualizations. Practically, we focus on two classification systems which have received great widespread and preference within the European Union, namely ICPC-2 (International Classification of Primary Care) and ICD-10 (International Classification of Diseases). The particular contributions of this work are: the logical encoding in OWL of ICPC-2 and ICD-10 classifications; the formalization of the existing ICPC-ICD conversion mappings in terms of OWL axioms and further verification of its coherence using the logical reasoning; and finally, the outline of the other semantic techniques for automated analysis of implications of future mapping changes between ICPC and ICD classifications.

Instance migration in heterogeneous ontology environments

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.

A Methodology for Knowledge Acquisition in Consumer-Oriented Healthcare

In Consumer-oriented Healthcare Informatics it is still difficult for laypersons to find, understand, and act on health information. This is due to the communication gap between specialized medical terminology used by healthcare professionals and “lay” medical terminology used by healthcare consumers. So there is a need to create consumer-friendly terminologies reflecting the different ways consumers and patients express and think about health topics. An additional need is to map these terminologies with existing clinically-oriented terminologies. This work suggests a methodology to acquire consumer health terminology for creating a Consumer-oriented Medical Vocabulary for Italian that mitigates this gap. This resource could be used in Personal Health Records to improve users’ accessibility to their healthcare data. In order to evaluate this methodology we mapped “lay” terms with standard specialized terminologies to find overlaps. Results showed that our approach provided many “lay” terms that can be considered good synonyms for medical concepts.

Context shifting for effective search over large knowledge bases

The problem of searching large knowledge bases is becoming an important facet of the current web of steadily proliferating semantic content. By pushing the notion of a context for partitioning large knowledge bases, performance of search is improved by narrowing the search space to a context of interest. On the other hand, by restricting the search only to a particular context, some answer can be missed, downgrading the search accuracy. In order to mitigate this drawback, we propose to extend the standard query algorithms with the operation of context shifting, i.e., the operation that allows switching to a close context, if the current context does not contain satisfactory information to answer a query. The paper provides a conceptual description of shifting in contextualized knowledge bases (CKB); and a prototypical implementation of a CKB that supports context shifting. For the conceptual description we adopt and extend the context-as-a-box paradigm introduced in [15]. In such a framework, a context is identified by a set of dimensions, whose values are taken from value-sets structured in hierarchies. Context shifting allows to switch from one context to another by changing the value of one or more dimensions along the corresponding hierarchies. For the prototypical implementation of a CKB we adopt and extend Sesame RDF store in order to support context shifting.

A Lexical-Ontological Resource for Consumer Healthcare

In Consumer Healthcare Informatics it is still difficult for laypeople to find, understand and act on health information, due to the persistent communication gap between specialized medical terminology and that used by healthcare consumers. Furthermore, existing clinically-oriented terminologies cannot provide sufficient support when integrated into consumer-oriented applications, so there is a need to create consumer-friendly terminologies reflecting the different ways healthcare consumers express and think about health topics. Following this direction, this work suggests a way to support the design of an ontology-based system that mitigates this gap, using knowledge engineering and semantic web technologies. The system is based on the development of a consumer-oriented medical terminology that will be integrated with other medical domain ontologies and terminologies into a medical ontology repository. This will support consumer-oriented healthcare systems, such as Personal Health Records, by providing many knowledge services to help users in accessing and managing their healthcare data.