Enhancing online knowledge graph population with semantic knowledge
Visualitza/Obre
10.1007/978-3-030-62419-4_11
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/332082
Tipus de documentCapítol de llibre
Data publicació2020-11-01
EditorSpringer
Condicions d'accésAccés obert
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Abstract
Knowledge Graphs (KG) are becoming essential to organize, represent and store the world’s knowledge, but they still rely heavily on humanly-curated structured data. Information Extraction (IE) tasks, like disambiguating entities and relations from unstructured text, are key to automate KG population. However, Natural Language Processing (NLP) methods alone can not guarantee the validity of the facts extracted and may introduce erroneous information into the KG. This work presents an end-to-end system that combines Semantic Knowledge and Validation techniques with NLP methods, to provide KG population of novel facts from clustered news events. The contributions of this paper are two-fold: First, we present a novel method for including entity-type knowledge into a Relation Extraction model, improving F1-Score over the baseline with TACRED and TypeRE datasets. Second, we increase the precision by adding data validation on top of the Relation Extraction method. These two contributions are combined in an industrial pipeline for automatic KG population over aggregated news, demonstrating increased data validity when performing online learning from unstructured web data. Finally, the TypeRE and AggregatedNewsRE datasets build to benchmark these results are also published to foster future research in this field.
CitacióFernàndez, D. [et al.]. Enhancing online knowledge graph population with semantic knowledge. A: "The Semantic Web – ISWC: 2020 19th International Semantic Web Conference: Athens, Greece: November 2–6, 2020: Proceedings, Part I". Berlín: Springer, 2020, p. 183-200.
ISBN978-3-030-62419-4
Versió de l'editorhttps://link.springer.com/book/10.1007/978-3-030-62419-4
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