Leveraging Linked Data to Discover Semantic Relations Within Data Sources
| Autor: | Mohsen Taheriyan, Pedro Szekely, José Luis Ambite, Craig A. Knoblock |
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| Rok vydání: | 2016 |
| Předmět: |
Information retrieval
Computer science Data field 02 engineering and technology Linked data Ontology (information science) Semantic data model Data mapping 020204 information systems Semantic computing 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing Semantic Web |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319465227 International Semantic Web Conference (1) |
| DOI: | 10.1007/978-3-319-46523-4_33 |
| Popis: | Mapping data to a shared domain ontology is a key step in publishing semantic content on the Web. Most of the work on automatically mapping structured and semi-structured sources to ontologies focuses on semantic labeling, i.e., annotating data fields with ontology classes and/or properties. However, a precise mapping that fully recovers the intended meaning of the data needs to describe the semantic relations between the data fields too. We present a novel approach to automatically discover the semantic relations within a given data source. We mine the small graph patterns occurring in Linked Open Data and combine them to build a graph that will be used to infer semantic relations. We evaluated our approach on datasets from different domains. Mining patterns of maximum length five, our method achieves an average precision of 75 % and recall of 77 % for a dataset with very complex mappings to the domain ontology, increasing up to 86 % and 82 %, respectively, for simpler ontologies and mappings. |
| Databáze: | OpenAIRE |
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