A Survey on Open Information Extraction

Proceedings of the 27th International Conference on Computational Linguistics, pages 3866–3878 Santa Fe, New Mexico, USA, August 20-26, 2018. 3866 A Survey on Open Information Extraction Christina Niklaus1, Matthias Cetto1, Andr´e Freitas2 and Siegfried Handschuh1 1 Faculty of Computer Science and Mathematics, University of Passau {christina.niklaus, matthias.cetto, siegfried.handschuh}@uni-passau.de 2 School of Computer Science, University of Manchester andre.freitas@manchester.ac.uk Abstract We provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction. We present the major challenges that such systems face, show the evolution of the suggested approaches over time and depict the specific issues they address. In addition, we provide a critique of the commonly applied evaluation procedures for assessing the performance of Open IE systems and highlight some directions for future work. 1 Introduction Information extraction (IE) turns the unstructured information expressed in natural language text into a structured representation (Jurafsky and Martin, 2009) in the form of relational tuples consisting of a set of arguments and a phrase denoting a semantic relation between them: ⟨arg1; rel; arg2⟩. Traditional ap- proaches to IE focus on answering narrow, well-defined requests over a predefined set of target relations on small, homogeneous corpora. To do so, they take as input the target relation along with hand-crafted extraction patterns or patterns learned from hand-labeled training examples (e.g., Agichtein and Gravano (2000), Brin (1999), Riloff and Jones (1999)). Consequently, shifting to a new domain requires the user to not only name the target relations, but also to manually define new extraction rules or to annotate new training data by hand. Thus, those systems rely on extensive human involvement. In order to reduce the manual effort required by IE approaches, Banko et al. (2007) introduced a new extraction paradigm: Open IE. Unlike traditional IE methods, Open IE is not limited to a small set of target relations known in advance, but rather extracts all types of relations found in a text. In that way, it facilitates the domain- independent discovery of relations extracted from text and scales to large, heterogeneous corpora such as the Web. Hence, Banko et al. (2007) identified three major challenges for Open IE systems: Automation. Open IE systems must rely on unsupervised extraction strategies, i.e. instead of specifying target relations in advance, possible relations of interest must be automatically detected while mak- ing only a single pass over the corpus. Moreover, the manual labor of creating suitable training data or extraction patterns must be reduced to a minimum by requiring only a small set of hand-tagged seed instances or a few manually defined extraction patterns. Corpus Heterogeneity. Heterogeneous datasets form an obstacle for profound linguistic tools such as syntactic or dependency parsers, since they commonly work well when trained and applied to a specific domain, but are prone to produce incorrect results when used in a different genre of text. Furthermore, Named Entity Recognition (NER) is unsuitable to target the variety and complexity of entity types on the Web. As Open IE …

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