Neural Relation Extraction for Knowledge Base Enrichment

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 229–240 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 229 Neural Relation Extraction for Knowledge Base Enrichment Bayu Distiawan Trisedya1, Gerhard Weikum2, Jianzhong Qi1, Rui Zhang1∗ 1 The University of Melbourne, Australia 2 Max Planck Institute for Informatics, Saarland Informatics Campus, Germany {btrisedya@student, jianzhong.qi@, rui.zhang@}unimelb.edu.au weikum@mpi-inf.mpg.de Abstract We study relation extraction for knowledge base (KB) enrichment. Specifically, we aim to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end-to-end manner. Previous stud- ies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into the KB space. This way, NED er- rors may cause extraction errors that affect the overall precision and recall. To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model. We collect high-quality training data by distant supervi- sion with co-reference resolution and para- phrase detection. We propose an n-gram based attention model that captures multi-word en- tity names in a sentence. Our model employs jointly learned word and entity embeddings to support named entity disambiguation. Finally, our model uses a modified beam search and a triple classifier to help generate high-quality triples. Our model outperforms state-of-the- art baselines by 15.51% and 8.38% in terms of F1 score on two real-world datasets. 1 Introduction Knowledge bases (KBs), often in the form of knowledge graphs (KGs), have become essential resources in many tasks including Q&A systems, recommender system, and natural language gener- ation. Large KBs such as DBpedia (Auer et al., 2007), Wikidata (Vrandecic and Kr¨otzsch, 2014) and Yago (Suchanek et al., 2007) contain millions of facts about entities, which are represented in the form of subject-predicate-object triples. However, these KBs are far from complete and mandate con- tinuous enrichment and curation. ∗Rui Zhang is the corresponding author. Input sentence: "New York University is a private university in Manhattan." Unsupervised approach output: ⟨NYU,is,private university⟩ ⟨NYU,is private university in,Manhattan⟩ Supervised approach output: ⟨NYU, instance of, Private University⟩ ⟨NYU, located in, Manhattan⟩ Canonicalized output: ⟨Q49210, P31, Q902104⟩ ⟨Q49210, P131, Q11299⟩ Table 1: Relation extraction example. Previous studies work on embedding-based model (Nguyen et al., 2018; Wang et al., 2015) and entity alignment model (Chen et al., 2017; Sun et al., 2017; Trisedya et al., 2019) to en- rich a knowledge base. Following the success of the sequence-to-sequence architecture (Bahdanau et al., 2015) for generating sentences from struc- tured data (Marcheggiani and Perez-Beltrachini, 2018; Trisedya et al., 2018), we employ this ar- chitecture to do the opposite, which is extracting triples from a sentence. In this paper, we study how to enrich a KB by relation exaction from textual sources. Specif- ically, we aim to extract triples in the form of ⟨h, r, t⟩, where h is a head entity, t is a tail en- tity, and r is a …

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