Abstract
Information Extraction (IE) is a pervasive task in the industry that allows to obtain automatically structured data from documents in natural language. Current software systems focused on this activity are able to extract a large percentage of the required information, but they do not usually focus on the quality of the extracted data. In this paper we present an approach focused on validating and improving the quality of the results of an IE system. Our proposal is based on the use of ontologies which store domain knowledge, and which we leverage to detect and solve consistency errors in the extracted data. We have implemented our approach to run against the output of the AIS system, an IE system specialized in analyzing legal documents and we have tested it using a real dataset. Preliminary results confirm the interest of our approach.
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Notes
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A Named Entity is a unique identifier of an entity in a text, e.g.,’Marie Curie’ is a NE of a person.
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R2RML: RDB to RDF Mapping Language, https://www.w3.org/TR/r2rml/.
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This example is directly taken from the experiments dataset. Proper names and specific data have been altered for reasons of privacy.
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In Spain, people have a first name, an optional middle name, and two mandatory last names, the first one if the father family name and the second one is the mother family name, although legally this order can be interchanged.
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Acknowledgements
This research work has been supported by projects TIN2013-46238-C4-4-R, TIN2016-78011-C4-3-R (AEI/FEDER, UE), and DGA/FEDER.
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Buey, M.G., Roman, C., Garrido, A.L., Bobed, C., Mena, E. (2019). Automatic Legal Document Analysis: Improving the Results of Information Extraction Processes Using an Ontology. In: Bembenik, R., Skonieczny, Ł., Protaziuk, G., Kryszkiewicz, M., Rybinski, H. (eds) Intelligent Methods and Big Data in Industrial Applications. Studies in Big Data, vol 40. Springer, Cham. https://doi.org/10.1007/978-3-319-77604-0_24
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