Unsupervised Ontology Induction from Text

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 296–305, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Unsupervised Ontology Induction from Text Hoifung Poon and Pedro Domingos Department of Computer Science & Engineering University of Washington hoifung,pedrod@cs.washington.edu Abstract Extracting knowledge from unstructured text is a long-standing goal of NLP. Al- though learning approaches to many of its subtasks have been developed (e.g., pars- ing, taxonomy induction, information ex- traction), all end-to-end solutions to date require heavy supervision and/or manual engineering, limiting their scope and scal- ability. We present OntoUSP, a system that induces and populates a probabilistic on- tology using only dependency-parsed text as input. OntoUSP builds on the USP unsupervised semantic parser by jointly forming ISA and IS-PART hierarchies of lambda-form clusters. The ISA hierar- chy allows more general knowledge to be learned, and the use of smoothing for parameter estimation. We evaluate On- toUSP by using it to extract a knowledge base from biomedical abstracts and an- swer questions. OntoUSP improves on the recall of USP by 47% and greatly outperforms previous state-of-the-art ap- proaches. 1 Introduction Knowledge acquisition has been a major goal of NLP since its early days. We would like comput- ers to be able to read text and express the knowl- edge it contains in a formal representation, suit- able for answering questions and solving prob- lems. However, progress has been difficult. The earliest approaches were manual, but the sheer amount of coding and knowledge engineering needed makes them very costly and limits them to well-circumscribed domains. More recently, ma- chine learning approaches to a number of key sub- problems have been developed (e.g., Snow et al. (2006)), but to date there is no sufficiently auto- matic end-to-end solution. Most saliently, super- vised learning requires labeled data, which itself is costly and infeasible for large-scale, open-domain knowledge acquisition. Ideally, we would like to have an end-to-end un- supervised (or lightly supervised) solution to the problem of knowledge acquisition from text. The TextRunner system (Banko et al., 2007) can ex- tract a large number of ground atoms from the Web using only a small number of seed patterns as guidance, but it is unable to extract non-atomic formulas, and the mass of facts it extracts is un- structured and very noisy. The USP system (Poon and Domingos, 2009) can extract formulas and ap- pears to be fairly robust to noise. However, it is still limited to extractions for which there is sub- stantial evidence in the corpus, and in most cor- pora most pieces of knowledge are stated only once or a few times, making them very difficult to extract without supervision. Also, the knowledge extracted is simply a large set of formulas with- out ontological structure, and the latter is essential for compact representation and efficient reasoning (Staab and Studer, 2004). We propose OntoUSP (Ontological USP), a sys- tem that learns an ISA hierarchy over clusters of logical expressions, and populates it by translat- ing sentences to …

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