Weakly Supervised Approaches for Ontology Population

Weakly Supervised Approaches for Ontology Population Hristo Tanev Tanev ITC-irst 38050, Povo Trento, Italy htanev@yahoo.co.uk Bernardo Magnini ITC-irst 38050, Povo Trento, Italy magnini@itc.it Abstract We present a weakly supervised approach to automatic Ontology Population from text and compare it with other two unsu- pervised approaches. In our experiments we populate a part of our ontology of Named Entities. We considered two high level categories - geographical locations and person names and ten sub-classes for each category. For each sub-class, from a list of training examples and a syntac- tically parsed corpus, we automatically learn a syntactic model - a set of weighted syntactic features, i.e. words which typ- ically co-occur in certain syntactic posi- tions with the members of that class. The model is then used to classify the unknown Named Entities in the test set. The method is weakly supervised, since no annotated corpus is used in the learning process. We achieved promising results, i.e. 65% accu- racy, outperforming significantly previous unsupervised approaches. 1 Introduction Automatic Ontology Population (OP) from texts has recently emerged as a new field of application for knowledge acquisition techniques (see, among others, (Buitelaar et al., 2005)). Although there is no a univocally accepted definition for the OP task, a useful approximation has been suggested (Bontcheva and Cunningham, 2003) as Ontology Driven Information Extraction, where, in place of a template to be filled, the goal of the task is the ex- traction and classification of instances of concepts and relations defined in a Ontology. The task has been approached in a variety of similar perspec- tives, including term clustering (e.g. (Lin, 1998a) and (Almuhareb and Poesio, 2004)) and term cat- egorization (e.g. (Avancini et al., 2003)). A rather different task is Ontology Learning (OL), where new concepts and relations are sup- posed to be acquired, with the consequence of changing the definition of the Ontology itself (see, for instance, (Velardi et al., 2005)). In this paper OP is defined in the following sce- nario. Given a set of terms T = t1, t2, ..., tn, a document collection D, where terms in T are sup- posed to appear, and a set of predefined classes C = c1, c2, ..., cm denoting concepts in an Ontol- ogy, each term ti has to be assigned to the proper class in C. For the purposes of the experiments presented in this paper we assume that (i) classes in C are mutually disjoint and (ii) each term is as- signed to just one class. As we have defined it, OP shows a strong sim- ilarity with Named Entity Recognition and Clas- sification (NERC). However, a major difference is that in NERC each occurrences of a recognized term has to be classified separately, while in OP it is the term, independently of the context in which it appears, that has to be classified. While Information Extraction, and NERC in particular, have been addressed prevalently by means of supervised approaches, Ontology Popu- lation is typically attacked in an unsupervised way. As …

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