7 research outputs found
The state of semantic technology today - overview of the first SEALS evaluation campaigns
This paper describes the first five SEALS Evaluation Campaigns over the semantic technologies covered by the SEALS project (ontology engineering tools, ontology reasoning tools, ontology matching tools, semantic search tools, and semantic web service tools). It presents the evaluations and test data used in these campaigns and the tools that participated in them along with a comparative analysis of their results. It also presents some lessons learnt after the execution of the evaluation campaigns and draws some final conclusions
Automating OAEI campaigns (first report)
trojahn2010cInternational audienceThis paper reports the first effort into integrating OAEI and SEALS evaluation campaigns. The SEALS project aims at providing standardized resources (software components, data sets, etc.) for automatically executing evaluations of typical semantic web tools, including ontology matching tools. A first version of the software infrastructure is based on the use of a web service interface wrapping the functionality of a matching tool to be evaluated. In this setting, the evaluation results can visualized and manipulated immediately in a direct feedback cycle. We describe how parts of the OAEI 2010 evaluation campaign have been integrated into this software infrastructure. In particular, we discuss technical and organizational aspects related to the use of the new technology for both participants and organizers of the OAEI
Automating OAEI Campaigns (First Report)
trojahn2010cInternational audienceThis paper reports the first effort into integrating OAEI and SEALS evaluation campaigns. The SEALS project aims at providing standardized resources (software components, data sets, etc.) for automatically executing evaluations of typical semantic web tools, including ontology matching tools. A first version of the software infrastructure is based on the use of a web service interface wrapping the functionality of a matching tool to be evaluated. In this setting, the evaluation results can visualized and manipulated immediately in a direct feedback cycle. We describe how parts of the OAEI 2010 evaluation campaign have been integrated into this software infrastructure. In particular, we discuss technical and organizational aspects related to the use of the new technology for both participants and organizers of the OAEI
Alignment Incoherence in Ontology Matching
Ontology matching is the process of generating alignments between ontologies. An alignment is a set of correspondences. Each correspondence links concepts and properties from one ontology to concepts and properties from another ontology. Obviously, alignments are the key component to enable integration of knowledge bases described by different ontologies. For several reasons, alignments contain often erroneous correspondences. Some of these errors can result in logical conflicts with other correspondences. In such a case the alignment is referred to as an incoherent alignment.
The relevance of alignment incoherence and strategies to resolve alignment incoherence are in the center of this thesis. After an introduction to syntax and semantics of ontologies and alignments, the importance of alignment coherence is discussed from different perspectives. On the one hand, it is argued that alignment incoherence always coincides with the incorrectness of correspondences. On the other hand, it is demonstrated that the use of incoherent alignments results in severe problems for different types of applications.
The main part of this thesis is concerned with techniques for resolving alignment incoherence, i.e., how to find a coherent subset of an incoherent alignment that has to be preferred over other coherent subsets. The underlying theory is the theory of diagnosis. In particular, two specific types of diagnoses, referred to as local optimal and global optimal diagnosis, are proposed. Computing a diagnosis is for two reasons a challenge. First, it is required to use different types of reasoning techniques to determine that an alignment is incoherent and to find subsets (conflict sets) that cause the incoherence. Second, given a set of conflict sets it is a hard problem to compute a global optimal diagnosis. In this thesis several algorithms are suggested to solve these problems in an efficient way.
In the last part of this thesis, the previously developed algorithms are applied to the scenarios of
- evaluating alignments by computing their degree of incoherence;
- repairing incoherent alignments by computing different types of diagnoses;
- selecting a coherent alignment from a rich set of matching hypotheses;
- supporting the manual revision of an incoherent alignment.
In the course of discussing the experimental results, it becomes clear that it is possible to create a coherent alignment without negative impact on the alignments quality. Moreover, results show that taking alignment incoherence into account has a positive impact on the precision of the alignment and that the proposed approach can help a human to save effort in the revision process
The Role of String Similarity Metrics in Ontology Alignment
Tim Berners-Lee originally envisioned a much different world wide web than the one we have today - one that computers as well as humans could search for the information they need [3]. There are currently a wide variety of research efforts towards achieving this goal, one of which is ontology alignment
Automated extension of biomedical ontologies
Developing and extending a biomedical ontology is a very demanding
process, particularly because biomedical knowledge is diverse, complex
and continuously changing and growing. Existing automated
and semi-automated techniques are not tailored to handling the issues
in extending biomedical ontologies.
This thesis advances the state of the art in semi-automated ontology
extension by presenting a framework as well as methods and
methodologies for automating ontology extension specifically designed
to address the features of biomedical ontologies.The overall strategy is
based on first predicting the areas of the ontology that are in need of
extension and then applying ontology learning and ontology matching
techniques to extend them. A novel machine learning approach for
predicting these areas based on features of past ontology versions was
developed and successfully applied to the Gene Ontology. Methods
and techniques were also specifically designed for matching biomedical
ontologies and retrieving relevant biomedical concepts from text,
which were shown to be successful in several applications.O desenvolvimento e extensão de uma ontologia biomédica é um processo
muito exigente, dada a diversidade, complexidade e crescimento
contÃnuo do conhecimento biomédico. As técnicas existentes nesta
área não estão preparadas para lidar com os desafios da extensão de
uma ontologia biomédica.
Esta tese avança o estado da arte na extensão semi-automática de ontologias,
apresentando uma framework assim como métodos e metodologias
para a automação da extensão de ontologias especificamente desenhados
tendo em conta as caracterÃsticas das ontologias biomédicas.
A estratégia global é baseada em primeiro prever quais as áreas da ontologia
que necessitam extensão, e depois usá-las como enfoque para
técnicas de alinhamento e aprendizagem de ontologias, com o objectivo
de as estender. Uma nova estratégia de aprendizagem automática
para prever estas áreas baseada em atributos de antigas versões de
ontologias foi desenvolvida e testada com sucesso na Gene Ontology.
Foram também especificamente desenvolvidos métodos e técnicas para
o alinhamento de ontologias biomédicas e extracção de conceitos relevantes
de texto, cujo sucesso foi demonstrado em várias aplicações.Fundação para a Ciência e a Tecnologi
Exploiting general-purpose background knowledge for automated schema matching
The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process.
In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources.
A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems.
One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented.
In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications