Ontology-based data integration

About: Ontology-based data integration is a research topic. Over the lifetime, 11065 publications have been published within this topic receiving 216888 citations.

A mapping system for the integration of OWL-DL ontologies

Abstract: To enable interoperability between applications in distributed information systems based on heterogeneous ontologies, it is necessary to formally define the notion of a mapping between ontologies. In this paper, we define a mapping system for OWL-DL ontologies, where mappings are expressed as correspondences between conjunctive queries over ontologies. As query answering within such a general mapping system is undecidable, we identify a decidable fragment of the mapping system, which corresponds to OWL-DL extended with DL-safe rules. We further show how the mapping system can be applied for the task of ontology integration and present a query answering algorithm.

An Ontology of Data Modelling Languages: A Study Using a Common-Sense Realistic Ontology

Abstract: Data modelling languages are used in today’s information systems engineering environments. Many have a degree of hype surrounding their quality and applicability with narrow and specific justification often given in support of one over another. We want to more deeply understand the fundamental nature of data modelling languages. We thus propose a theory, based on ontology, that should allow us to understand, compare, evaluate, and strengthen data modelling languages. In this paper we present a method (conceptual evaluation) and its extension (conceptual comparison), as part of our theory. Our methods are largely independent of a specific ontology. We introduce Chisholm’s ontology and apply our methods to analyse some data modelling languages using it. We find a good degree of overlap between all of the data modelling languages analysed and the core concepts of Chisholm’s ontology, and conclude that the data modelling languages investigated reflect an ontology of commonsense-realism.

FOAM - Framework for Ontology Alignment and Mapping - Results of the Ontology Alignment Evaluation Initiative.

Abstract: This paper briefly introduces the system FOAM and its underlying techniques. We then discuss the results returned from the evaluation. They were very promising and at the same time clarifying. Concisely: labels are very important; structure helps in cases where labels do not work; dictionaries may provide additional evidence; ontology management systems need to deal with OWL-Full. The results of this paper will also be very interesting for other participants, showing specific strengths and weaknesses of our approach. 1. PRESENTATION OF THE SYSTEM 1.1 State, purpose, general statement In recent years, we have seen a range of research work on methods proposing alignments [1; 2]. When we tried to apply these methods to some of the real-world scenarios we address in other research contributions [3], we found that existing alignment methods did not suit the given requirements: • high quality results; • efficiency; • optional user-interaction; • flexibility with respect to use cases; • and easy adjusting and parameterizing. We wanted to provide the end-user with a tool taking ontologies as input and returning alignments (with explanations) as output meeting these requirements. 1.2 Specific techniques used We have observed that alignment methods like QOM [4] or PROMPT [2] may be mapped onto a generic alignment process (Figure 1). Here we will only mention the six major steps to clarify the underlying approach for the FOAM tool. We refer to [4] for a detailed description. 1. Feature Engineering, i.e. select excerpts of the overall ontology definition to describe a specific. This includes individual features, e.g. labels, structural features, e.g. subsumption, but also more complex features as used in OWL, e.g. restrictions. 2. Search Step Selection, i.e. choose two entities from the two ontologies to compare (e1,e2). 3. Similarity Assessment, i.e. indicate a similarity for a given description (feature) of two entities (e.g., simsuperConcept(e1,e2)=1.0). 4. Similarity Aggregation, i.e. aggregate the multiple similarity assessments for one pair of entities into a single measure. 5. Interpretation, i.e. use all aggregated numbers, a threshold and an interpretation strategy to propose the alignment (align(e1)=‘ e2’). This may also include a user validation. 6. Iteration, i.e. as the similarity of one alignment influences the similarity of neighboring entity pairs; the equality is propagated through the ontologies. Finally, we receive alignments linking the two ontologies. This general process was extended to meet the mentioned requirements. • High quality results were achieved through a combination of a rule-based approach and a machine learning approach. Underlying individual rules such as, if the super-concepts are similar the entities are similar, have been assigned weights by a machine learnt decision tree [5]. Especially steps 1, 3 and 4 were adjusted for this. Currently, our approach does not make use of additional background knowledge such as dictionaries here. • Efficiency was mainly achieved through an intelligent selection of candidate alignments in 2, the search step selection [4]. • User-interaction allows the user intervening during the interpretation step. By presenting the doubtable alignments (and only these) to the user, overall quality can be considerably increased. Yet this happens in a minimal invasive manner. • The system can automatically set its parameters according to a list of given use cases, such as ontology merging, versioning, ontology mapping, etc. The parameters also change according to the ontologies to align, e.g., big ontologies always require the efficient approach, whereas smaller ones do not [6]. • All these parameters may be set manually. This allows using the implementation for very specific tasks as well. • Finally, FOAM has been implemented in Java and is freely available, thus extensible. 1.3 Adaptations made for the contest No special adjustments have been made for the contest. However, some elements have been deactivated. Due to the small size of the benchmark and directory ontologies efficiency was not used, userinteraction was removed for the initiative, and no specific use case parameters were taken. A general alignment procedure was applied. The system used for the evaluation is a derivative of the ontology alignment tool used in last year’s contests I3Con [7] and EONOAC [8].

Ontology Mappings to Improve Learning Resource Search.

Abstract: This paper proposes an ontology mapping-based framewrowrk that allows searching for learning resources using multiple ontologies. The present applications of ontologies in e-learning use various ontologies (eg, domain, curriculum, context), but they do not give a solution on how to interoperate e-learning systems based on different ontologies. The proposed solution uses a mapping ontology that is a part of a recent Semantic Web initiative, the Simple Knowledge Organisation System. On top of that, we develop and implement two search algorithms. Finally, we evaluated the solution by developing a system that helps students search for relevant learning resource using a local context (ie, course curriculum) ontology.