Results of the ontology alignment evaluation initiative 2019

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2019 campaign offered 11 tracks with 29 test cases, and was attended by 20 participants. This paper is an overall presentation of that campaign

Proof Support for Common Logic

We present an extension of the Heterogeneous Tool Set HETS that enables proof support for Common Logic. This is achieved via logic translations that relate Common Logic and some of its sublogics to already supported logics and automated theorem proving systems. We thus provide the first full theorem proving support for Common Logic, including the possibility of verifying meta-theoretical relationships between Common Logic theories

Results of the Ontology Alignment Evaluation Initiative 2014

dragisic2014aInternational audienceOntology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation and consensus. OAEI 2014 offered 7 tracks with 9 test cases followed by 14 participants. Since 2010, the campaign has been using a new evaluation modality which provides more automation to the evaluation. This paper is an overall presentation of the OAEI 2014 campaign

Heterogeneous Theories and the Heterogeneous Tool Set

Heterogeneous multi-logic theories arise in different contexts: they are needed for the specification of large software systems, as well as for mediating between different ontologies. This is because large theories typically involve different aspects that are best specified in different logics (like equational logics, description logics, first-order logics, higher-order logics, modal logics), but also because different formalisms are in practical use (like RDF, OWL, EML). Using heterogeneous theories, different formalims being developed at different sites can be related, i.e. there is a formal interoperability among languages and tools. In many cases, specialized languages and tools have their strengths in particular aspects. Using heterogeneous theories, these strengths can be combined with comparably small effort. By contrast, a true combination of all the involved logics into a single logic would be too complex (or even inconsistent) in many cases. We propose to use emph{institutions} as a formalization of the notion of logical system. Institutions can be related by so-called institution morphsims and comorphisms. Any graph of institutions and (co)morphisms can be flattened to a so-called emph{Grothendieck institution}, which is kind of disjoint union of all the logics, enriched with connections via the (co)morphisms. This semantic basis for heterogeneous theories is complemented by the heterogeneous tool set, which provides tool support. Based on an object-oriented interface for institutions (using type classes in Haskell), it implements the Grothendieck institution and provides a heterogeneous parser, static analysis and proof support for heterogeneous theories. This is based on parsers, static analysers and proof support for the individual institutions, and on a heterogeneous proof calculus for theories in the Grothendieck institution. See also the Hets web page: http://www.tzi.de/cofi/het

Proceedings of The Tenth International Workshop on Ontology Matching (OM-2015)

shvaiko2016aInternational audienceno abstrac

Optique: Zooming in on Big Data

Despite the dramatic growth of data accumulated by enterprises, obtaining value out of it is extremely challenging. In particular, the data access bottleneck prevents domain experts from getting the right piece of data within a constrained time frame. The Optique Platform unlocks the access to Big Data by providing end users support for directly formulating their information needs through an intuitive visual query interface. The submitted query is then transformed into highly optimized queries over the data sources, which may include streaming data, and exploiting massive parallelism in the backend whenever possible. The Optique Platform thus responds to one major challenge posed by Big Data in data-intensive industrial settings

Breaking rules: taking Complex Ontology Alignment beyond rule­based approaches

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2021As ontologies are developed in an uncoordinated manner, differences in scope and design compromise interoperability. Ontology matching is critical to address this semantic heterogeneity problem, as it finds correspondences that enable integrating data across the Semantic Web. One of the biggest challenges in this field is that ontology schemas often differ conceptually, and therefore reconciling many real¬world ontology pairs (e.g., in geography or biomedicine) involves establishing complex mappings that contain multiple entities from each ontology. Yet, for the most part, ontology matching algorithms are restricted to finding simple equivalence mappings between ontology entities. This work presents novel algorithms for Complex Ontology Alignment based on Association Rule Mining over a set of shared instances between two ontologies. Its strategy relies on a targeted search for known complex patterns in instance and schema data, reducing the search space. This allows the application of semantic¬based filtering algorithms tailored to each kind of pattern, to select and refine the most relevant mappings. The algorithms were evaluated in OAEI Complex track datasets under two automated approaches: OAEI’s entity¬based approach and a novel element¬overlap–based approach which was developed in the context of this work. The algorithms were able to find mappings spanning eight distinct complex patterns, as well as combinations of patterns through disjunction and conjunction. They were able to efficiently reduce the search space and showed competitive performance results comparing to the State of the Art of complex alignment systems. As for the comparative analysis of evaluation methodologies, the proposed element¬overlap–based evaluation strategy was shown to be more accurate and interpretable than the reference-based automatic alternative, although none of the existing strategies fully address the challenges discussed in the literature. For future work, it would be interesting to extend the algorithms to cover more complex patterns and combine them with lexical approaches

Architectural Refinement in HETS

The main objective of this work is to bring a number of improvements to the Heterogeneous Tool Set HETS, both from a theoretical and an implementation point of view. In the first part of the thesis we present a number of recent extensions of the tool, among which declarative specifications of logics, generalized theoroidal comorphisms, heterogeneous colimits and integration of the logic of the term rewriting system Maude. In the second part we concentrate on the CASL architectural refinement language, that we equip with a notion of refinement tree and with calculi for checking correctness and consistency of refinements. Soundness and completeness of these calculi is also investigated. Finally, we present the integration of the VSE refinement method in HETS as an institution comorphism. Thus, the proof manangement component of HETS remains unmodified

Finding Data Should be Easier than Finding Oil

The competitiveness of modern enterprises heavily depends on their ability to make the right business decisions by relying on efficient and timely analysis of the right business critical data. In large and data intensive companies such as Equinor, a Norwegian multinational oil and gas company with more than 20,000 employees, gathering such data is not a trivial task due to the growing size and complexity of corporate information sources. As a result, the data gathering task is often the most time-consuming part of the decision making process, in particular when it comes to the work processes of Equinor's exploration geologists that should find in a timely manner new exploitable accumulations of oil or gas in given areas by analysing data about these areas. In this work we present our experience in addressing this data challenge tast at Equinor. We have developed and deployed at Equinor a semantic data access system that relies on the Ontology Based Data Access (OBDA) approach. Our system is based on our solid theoretical contributions and has been extensively evaluated at Equinor