Joint learning of ontology and semantic parser from text
By Janez Starc, Dunja Mladenic. 2016
6 citations recorded by the source corpus.
Semantic parsing methods are used for capturing and representing semantic meaning of text. Meaning representation capturing all the concepts in the text may not always be available or may not be sufficiently complete. Ontologies provide a structured and reasoning-capable way to model the content of a collection of texts. In this work, we present a novel approach to joint learning of ontology and semantic parser from text. The method is based on semi-automatic induction of a context-free grammar from semantically annotated text. The grammar parses the text into semantic trees. Both, the grammar and the semantic trees are used to learn the ontology on several levels - classes, instances, taxonomic and non-taxonomic relations. The approach was evaluated on the first sentences of Wikipedia pages describing people.
Subfields: Information Extraction, Knowledge Graphs & Representation
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Citations in the corpus
- Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning (2017, background): “Researchers also investigated QA over subsets of large-scale knowledge graphs such as DBPedia (Starc & Mladenic, 2017) and Freebase (Cai & Yates, 2013; Berant et al., 2013).”