20 research outputs found
Recommended from our members
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
Improving approximation of domain-focused, corpus-based, lexical semantic relatedness
Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importance in many domain-specific scenarios. The problem of most state-of-the-art methods for calculating domain-specific semantic relatedness is their dependence on highly specialized, structured knowledge resources, which makes these methods poorly adaptable for many usage scenarios. On the other hand, the domain knowledge in the fields such as Life Sciences has become more and more accessible, but mostly in its unstructured form - as texts in large document collections, which makes its use more challenging for automated processing.
In this dissertation, three new corpus-based methods for approximating domain-specific textual semantic relatedness are presented and evaluated with a set of standard benchmarks focused on the field of biomedicine. Nonetheless, the proposed measures are general enough to be adapted to other domain-focused scenarios. The evaluation involves comparisons with other relevant state-of-the-art measures for calculating semantic relatedness and the results suggest that the methods presented here perform comparably or better than other approaches.
Additionally, the dissertation also presents an experiment, in which one of the proposed methods is applied within an ontology matching system, DisMatch. The performance of the system was evaluated externally on a biomedically themed ‘Phenotype’ track of the Ontology Alignment Evaluation Initiative 2016 campaign. The results of the track indicate, that the use distributional semantic relatedness for ontology matching is promising, as the system presented in this thesis did stand out in detecting correct mappings that were not detected by any other systems participating in the track.
The work presented in the dissertation indicates an improvement achieved w.r.t. the stat-of-the-art through the domain adapted use of the distributional principle (i.e. the presented methods are corpus-based and do not require additional resources). The ontology matching experiment showcases practical implications of the presented theoretical body of work
Corporate Smart Content Evaluation
Nowadays, a wide range of information sources are available due to the
evolution of web and collection of data. Plenty of these information are
consumable and usable by humans but not understandable and processable by
machines. Some data may be directly accessible in web pages or via data feeds,
but most of the meaningful existing data is hidden within deep web databases
and enterprise information systems. Besides the inability to access a wide
range of data, manual processing by humans is effortful, error-prone and not
contemporary any more. Semantic web technologies deliver capabilities for
machine-readable, exchangeable content and metadata for automatic processing
of content. The enrichment of heterogeneous data with background knowledge
described in ontologies induces re-usability and supports automatic processing
of data. The establishment of “Corporate Smart Content” (CSC) - semantically
enriched data with high information content with sufficient benefits in
economic areas - is the main focus of this study. We describe three actual
research areas in the field of CSC concerning scenarios and datasets
applicable for corporate applications, algorithms and research. Aspect-
oriented Ontology Development advances modular ontology development and
partial reuse of existing ontological knowledge. Complex Entity Recognition
enhances traditional entity recognition techniques to recognize clusters of
related textual information about entities. Semantic Pattern Mining combines
semantic web technologies with pattern learning to mine for complex models by
attaching background knowledge. This study introduces the afore-mentioned
topics by analyzing applicable scenarios with economic and industrial focus,
as well as research emphasis. Furthermore, a collection of existing datasets
for the given areas of interest is presented and evaluated. The target
audience includes researchers and developers of CSC technologies - people
interested in semantic web features, ontology development, automation,
extracting and mining valuable information in corporate environments. The aim
of this study is to provide a comprehensive and broad overview over the three
topics, give assistance for decision making in interesting scenarios and
choosing practical datasets for evaluating custom problem statements. Detailed
descriptions about attributes and metadata of the datasets should serve as
starting point for individual ideas and approaches
Recommended from our members
Results of the ontology alignment evaluation initiative 2020
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 and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2020 campaign offered 12 tracks with 36 test cases, and was attended by 19 participants. This paper is an overall presentation of that campaign
Results of the Ontology Alignment Evaluation Initiative 2021
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 and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2021 campaign offered 13 tracks and was attended by 21 participants. This paper is an overall presentation of that campaig
OM-2017: Proceedings of the Twelfth International Workshop on Ontology Matching
shvaiko2017aInternational audienceOntology matching is a key interoperability enabler for the semantic web, as well as auseful tactic in some classical data integration tasks dealing with the semantic heterogeneityproblem. It takes ontologies as input and determines as output an alignment,that is, a set of correspondences between the semantically related entities of those ontologies.These correspondences can be used for various tasks, such as ontology merging,data translation, query answering or navigation on the web of data. Thus, matchingontologies enables the knowledge and data expressed with the matched ontologies tointeroperate
Recommended from our members
Results of the Ontology Alignment Evaluation Initiative 2021
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 and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2021 campaign offered 13 tracks and was attended by 21 participants. This paper is an overall presentation of that campaign
Ontology Matching: OM-2018: Proceedings of the ISWC Workshop
International audienceno abstrac
Recommended from our members
Results of the Ontology Alignment Evaluation Initiative 2023
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 and use different evaluation modalities. The OAEI 2023 campaign offered 15 tracks and was attended by 16 participants. This paper is an overall presentation of that campaign