Published Versions 3 Vol 2 (3) : 353-378 2020
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GeoLink Data Set: A Complex Alignment Benchmark from Real-world Ontology
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Abstract & Keywords
Abstract: Ontology alignment has been studied for over a decade, and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies. However, very few alignment systems focus on finding complex correspondences. One reason for this limitation may be that there are no widely accepted alignment benchmarks that contain such complex relationships. In this paper, we propose a real-world data set from the GeoLink project as a potential complex ontology alignment benchmark. The data set consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), as well as a manually created reference alignment that was developed in consultation with domain experts from different institutions. The alignment includes 1:1, 1:n, and m:n equivalence and subsumption correspondences, and is available in both Expressive and Declarative Ontology Alignment Language (EDOAL) and rule syntax. The benchmark has been expanded from its original version to contain real-world instance data from seven geoscience data providers that has been published according to both ontologies. This allows it to be used by extensional alignment systems or those that require training data. This benchmark has been incorporated into the Ontology Alignment Evaluation Initiative (OAEI) complex track to help researchers test their automated alignment systems and algorithms. This paper also analyzes the challenges inherent in effectively generating, detecting, and evaluating complex ontology alignments and provides a road map for future work on this topic.
Keywords: Complex ontology alignment; Real-world ontology; Ontology population; Benchmark
Acknowledgments
We would like to thank all of the members of the GeoLink project team. In addition, we extend our gratitude to Jerome Euzenat for providing advice regarding the conversion of rules to EDOAL. Moreover, we would like to express our appreciation to Daniel Faria for helping us to prepare the data set for evaluation on the SEALS platform..
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Article and author information
Cite As
L. Zhou, M. Cheatham, A. Krisnadhi & P. Hitzler. GeoLink data set: A complex alignment benchmark from real-world ontology. Data Intelligence 2(2020), 353-378. doi: 10.1162/dint_a_00054
Lu Zhou
This work was conceptualized during discussion among all of the authors. L. Zhou prepared the data set and wrote the first draft of the paper. All of the authors have made valuable contributions in editing and revising the final version of the article.
luzhou@ksu.edu
Lu Zhou is a PhD candidate at the Data Semantics Laboratory at Kansas State University. He is currently working with Dr. Pascal Hitzler on semantic data integration. His research interests lie within knowledge graph schema matching, natural language processing, applied machine learning, coreference resolution, and knowledge graph population. He has published papers in conferences and journals, such as World Wide Web Conference (WWW), International Semantic Web Conference (ISWC), and Knowledge Engineering Review (KER). He served as a reviewer for several conferences and journals, such as Extended Semantic Web Conference (ESWC), ISWC, KER, Transactions on Social Computing (ACMTSC), and Journal of Computers and Applications (JCA). In addition, he is one of the organizers of the conference track and the complex track for the Ontology Alignment Evaluation Initiative (OAEI), as part of the annual Ontology Matching workshop.
0000-0002-0453-9965
Michelle Cheatham
This work was conceptualized during discussion among all of the authors. M. Cheatham, A. Krisnadhi and P. Hitzler clarified concepts and contributions in the paper. All of the authors have made valuable contributions in editing and revising the final version of the article.
Michelle Cheatham is an Assistant Professor in the Department of Computer Science and Engineering at Wright State University. She has previously held positions in government and industry, focusing on machine learning and cyber security. Her current research interests focus on semantic data integration, including ontology alignment, coreference resolution and privacy concerns of data mining. She was a member of the organizing committee of the Ontology Matching workshop and assisted in evaluating results from the annual Ontology Alignment Evaluation Initiative from 2015 through 2018 and the organizing committee of the Security, Privacy and the Semantic Web workshop in 2017. Dr. Cheatham received a PhD in Computer Science and Engineering from Wright State University in 2014, a Master’s degree in that subject from the same institution in 2006, and a Master’s degree in Business Administration and a Bachelor’s degree in Computer Science from the University of Kentucky in 2001. For more information, see www.michellecheatham.com.
0000-0002-8457-6300
Adila Krisnadhi
This work was conceptualized during discussion among all of the authors. M. Cheatham, A. Krisnadhi and P. Hitzler clarified concepts and contributions in the paper. All of the authors have made valuable contributions in editing and revising the final version of the article.
Adila Krisnadhi is a lecturer at the Faculty of Computer Science, Universitas Indonesia. Since 2019, he is also a co-director of Tokopedia-UI AI Center of Excellence. He was a member of the Data Semantics Laboratory from 2010 to 2017 where he was a PhD student and postdoctoral researcher. His research interests cover a number of topics within semantic Web, data integration, knowledge representation and reasoning, and machine learning, particularly related to ontology design patterns, ontology engineering methodologies, knowledge graph modeling and construction, as well as reasoning over ontologies and knowledge graphs with more than 60 publications in conferences and journals such as Semantic Web journal, Big Earth Data, International Semantic Web Conference(ISWC), Extended Semantic Web Conference (ESWC) and World Wide Web conference (WWW). He is a board member of the Association for Ontology Design & Patterns and co-chaired the 2019 edition of Workshop of Ontology Design and Patterns (WOP). He served as a reviewer for several journals: Journal of Web Semantics, Semantic Web journal, Artificial Intelligence, and Journal of Applied Ontology. Also, he has been a PC member of several major conferences such as ISWC, ESWC, AAAI, and WWW.
0000-0003-0745-6804
Pascal Hitzler
This work was conceptualized during discussion among all of the authors. M. Cheatham, A. Krisnadhi and P. Hitzler clarified concepts and contributions in the paper. All of the authors have made valuable contributions in editing and revising the final version of the article.
Pascal Hitzler is Professor and endowed Lloyd T. Smith Creativity in Engineering Chair at the Department of Computer Science at Kansas State University and Director of the Data Semantics (DaSe) Laboratory. His research record lists over 400 publications in such diverse areas as semantic Web, artificial intelligence, neural-symbolic integration, knowledge representation and reasoning, machine learning, denotational semantics and set-theoretic topology. His research is highly cited. He is founding Editor-in-chief of the Semantic Web journal, the leading journal in the field, and of the IOS Press book series studies on the semantic Web. He is co-author of the W3C Recommendation OWL 2 Primer, and of the book Foundations of Semantic Web Technologies by CRC Press, 2010, which was named as one out of seven Outstanding Academic Titles 2010 in Information and Computer Science by the American Library Association’s Choice Magazine, and has translations into German and Chinese. He is on the editorial board of several journals and book series and a founding steering committee member of the Neural-Symbolic Learning and Reasoning Association and the Association for Ontology Design and Patterns. For more information about him, see http://www.pascal-hitzler.de.
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Published: Sept. 28, 2020 (Versions3
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