Similarity-based ontology alignment in OWL-Lite

euzenat2004cInternational audienceInteroperability of heterogeneous systems on the Web will be admittedly achieved through an agreement between the underlying ontologies. However, the richer the ontology description language, the more complex the agreement process, and hence the more sophisticated the required tools. Among current ontology alignment paradigms, similarity-based approaches are both powerful and flexible enough for aligning ontologies expressed in languages like OWL. We define a universal measure for comparing the entities of two ontologies that is based on a simple and homogeneous comparison principle: Similarity depends on the type of entity and involves all the features that make its definition (such as superclasses, properties, instances, etc.). One-to-many relationships and circularity in entity descriptions constitute the key difficulties in this context: These are dealt with through local matching of entity sets and iterative computation of recursively dependent similarities, respectively

SIMILARITY USED IN THE IDENTIFICATION OF THE NEED TO REFACTORYZE AN ONTOLOGY

Lately, different ontologies were developed, manually, semi-automatically or automatically. Some are enriched continuously. In the case in which the ontology development process is involving too many resources, and take place in a long period of time, having as result a very large scale ontology, the quality of the ontology may suffer. And the misplaced elements must be discovered, in order to make possible the refactorization of the ontology. In this paper is proposed a method of spotting the most susceptible misplaced elements using a natural language technique, text similarity.ontology, similarity, refactorization, dictionary based methods

Descubrimiento automático de mappings

Dentro de la problemática de la integración de información, los elementos claves son los mappings, unidades que relacionan las diferentes representaciones (ontologías, bases de datos, redes semánticas, etc. ). Y dentro de toda la colección de operaciones que los mappings llevan asociadas en todo su ciclo de vida, el cuello de botella se encuentra en su descubrimiento. Con este trabajo doctoral se pretende dar un paso más en este campo realizando un nuevo modelo de mappings lo menos limitado, y a la vez funcional, posible a diferentes representaciones y lo más versátil para la combinación de técnicas de descubrimiento, de toda índole, ya existentes y de nuevo cuño de manera automática, basándose en un sistema experto previamente construido a costa de evaluaciones sobre casos de uso reales

Survey on Techniques for Ontology Interoperability in Semantic Web

Ontology is a shared conceptualization of knowledge representation of particular domain. These are used for the enhancement of semantic information explicitly. It is considered as a key element in semantic web development. Creation of global web data sources is impossible because of the dynamic nature of the web. Ontology Interoperability provides the reusability of ontologies. Different domain experts and ontology engineers create different ontologies for the same or similar domain depending on their data modeling requirements. These cause ontology heterogeneity and inconsistency problems. For more better and precise results ontology mapping is the solution. As their use has increased, providing means of resolving semantic differences has also become very important. Papers on ontology interoperability report the results on different frameworks and this makes their comparison almost impossible. Therefore, the main focus of this paper will be on providing some basics of ontology interoperability and briefly introducing its different approaches. In this paper we survey the approaches that have been proposed for providing interoperability among domain ontologies and its related techniques and tools

A Large Scale Dataset for the Evaluation of Ontology Matching Systems

Recently, the number of ontology matching techniques and systems has increased significantly. This makes the issue of their evaluation and comparison more severe. One of the challenges of the ontology matching evaluation is in building large scale evaluation datasets. In fact, the number of possible correspondences between two ontologies grows quadratically with respect to the numbers of entities in these ontologies. This often makes the manual construction of the evaluation datasets demanding to the point of being infeasible for large scale matching tasks. In this paper we present an ontology matching evaluation dataset composed of thousands of matching tasks, called TaxME2. It was built semi-automatically out of the Google, Yahoo and Looksmart web directories. We evaluated TaxME2 by exploiting the results of almost two dozen of state of the art ontology matching systems. The experiments indicate that the dataset possesses the desired key properties, namely it is error-free, incremental, discriminative, monotonic, and hard for the state of the art ontology matching systems. The paper has been accepted for publication in "The Knowledge Engineering Review", Cambridge Universty Press (ISSN: 0269-8889, EISSN: 1469-8005)

A Linear Program For Holistic Matching : Assessment on Schema Matching Benchmark

International audienceSchema matching is a key task in several applications such as data integration and ontology engineering. All application fields require the matching of several schemes also known as "holistic matching", but the difficulty of the problem spawned much more attention to pairwise schema matching rather than the latter. In this paper, we propose a new approach for holistic matching. We suggest modelling the problem with some techniques borrowed from the combinatorial optimization field. We propose a linear program, named LP4HM, which extends the maximum-weighted graph matching problem with different linear constraints. The latter encompass matching setup constraints, especially cardinality and threshold constraints; and schema structural constraints, especially superclass/subclass and coherence constraints. The matching quality of LP4HM is evaluated on a recent benchmark dedicated to assessing schema matching tools. Experimentations show competitive results compared to other tools, in particular for recall and HSR quality measure

Towards ensuring Satisfiability of Merged Ontology

AbstractThe last decade has seen researchers developing efficient algorithms for the mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. Our methodology detects inconsistencies based on structural mismatches that occur due to conflicts among the set of Generalized Concept Inclusions, and Disjoint Relations due to the differences between disjoint partitions in the local heterogeneous ontologies. We present novel methodologies to detect and repair semantic inconsistencies from the list of initial mappings. This results in global merged ontology free from ‘circulatory error in class/property hierarchy’, „common class/instance between disjoint classes error’, ‘redundancy of subclass/subproperty relations’, ‘redundancy of disjoint relations’ and other types of „semantic inconsistency’ errors. In this way, our methodology saves time and cost of traversing local ontologies for the validation of mappings, improves performance by producing only consistent accurate mappings, and reduces the user dependability for ensuring the satisfiability and consistency of merged ontology. The experiments show that the newer approach with automatic inconsistency detection yields a significantly higher precision