Abstract
Extracting structured information from text plays a crucial role in automatic knowledge acquisition and is at the core of any knowledge representation and reasoning system. Traditional methods rely on hand-crafted rules and are restricted by the performance of various linguistic pre-processing tools. More recent approaches rely on supervised learning of relations trained on labelled examples, which can be manually created or sometimes automatically generated (referred as distant supervision). We propose a supervised method for entity typing and alignment. We argue that a rich feature space can improve extraction accuracy and we propose to exploit Linked Open Data (LOD) for feature enrichment. Our approach is tested on task-2 of the Open Knowledge Extraction challenge, including automatic entity typing and alignment. Our approach demonstrate that by combining evidences derived from LOD (e.g. DBpedia) and conventional lexical resources (e.g. WordNet) (i) improves the accuracy of the supervised induction method and (ii) enables easy matching with the Dolce+DnS Ultra Lite ontology classes.
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Notes
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Source code can be found at https://github.com/jerrygaoLondon/oke-extractor.
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We use the training data encoded in NIF format provided by the challenge organisers in this experiment. The NLP Interchange Format (NIF) is an RDF/OWL-based format that aims to achieve interoperability between Natural Language Processing (NLP) tools, language resources and annotations.
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The rdfs stands for the namespace of RDF Schema (http://www.w3.org/2000/01/ rdf-schema#).
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RDFLib: https://pypi.python.org/pypi/rdflib.
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The SMART stop-word list built by Chris Buckley and Gerard Salton, which can be obtained from goo.gl/rBQNbO.
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SPARQLWrapper is a python based wrapper around a SPARQL service, access via http://rdflib.github.io/sparqlwrapper/.
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The dul stands for the prefix for http://www.ontologydesignpatterns.org/ont/dul/DUL.owl.
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The complete SPARQL query can be found in the projects source code repository.
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Acknowledgments
Part of this research has been sponsored by the EPSRC funded project LODIE: Linked Open Data for IE, EP/J019488/1.
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Gao, J., Mazumdar, S. (2015). Exploiting Linked Open Data to Uncover Entity Types. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds) Semantic Web Evaluation Challenges. SemWebEval 2015. Communications in Computer and Information Science, vol 548. Springer, Cham. https://doi.org/10.1007/978-3-319-25518-7_5
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