Interaction Relation Ontology Learning

Li, Chuanxi; Wang, Rujing; Zhang, Youzhi; Huang, He; Su, Ya-Ru

doi:10.1089/cmb.2012.0009

Cited by 2 publications

(1 citation statement)

References 24 publications

(18 reference statements)

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“…In this work we use syntactic parsers to extract noun-phrases. The English corpora were parsed using the Stanford Lexicalized Parser [83] (version 3.3.1), a well known parser and widely used in relation extraction [84][85][86]. For the Portuguese corpora we applied the PALAVRAS [87] parser, which has been used in many work [82,[88][89][90].…”

Section: Parsingmentioning

confidence: 99%

Evaluating the Complementarity of Taxonomic Relation Extraction Methods Across Different Languages

Granada,

Vieira,

Trojahn

et al. 2018

Preprint

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Modern information systems are changing the idea of "data processing" to the idea of "concept processing", meaning that instead of processing words, such systems process semantic concepts which carry meaning and share contexts with other concepts. Ontology is commonly used as a structure that captures the knowledge about a certain area via providing concepts and relations between them. Traditionally, concept hierarchies have been built manually by knowledge engineers or domain experts. However, the manual construction of a concept hierarchy suffers from several limitations such as its coverage and the enormous costs of its extension and maintenance. Furthermore, keeping up with a hand-crafted concept hierarchy along with the evolution of domain knowledge is an overwhelming task, being necessary to build concept hierarchies automatically. Ontology learning, usually referred to the (semi-)automatic support in ontology development, is usually divided into steps, going from concepts identification, passing through hierarchy and non-hierarchy relations detection and, seldom, axiom extraction. It is reasonable to say that among these steps the current frontier is in the establishment of concept hierarchies, since this is the backbone of ontologies and, therefore, a good concept hierarchy is already a valuable resource for many ontology applications. A concept hierarchy is represented with a tree-structured form with specialization and generalization relations between concepts, in which lower-level concepts are more specific while higher-level concepts are more general. The automatic construction of concept hierarchies from texts is a complex task and since the 1980 decade, much work have been proposing approaches to better extract relations between concepts. These different proposals have never been contrasted against each other on the same set of data and across different languages. Such comparison is important to see whether they are complementary or incremental, also we can see whether they present different tendencies towards recall and precision, i.e., some can be very precise but with very low recall and others can achieve better recall but low precision. Another aspect concerns the variation of results for different languages. This paper evaluates these different methods on the basis of hierarchy metrics such as density and depth, and evaluation metrics such as Recall and Precision. The evaluation is performed over the same corpora, which consists of English and Portuguese parallel and comparable texts. The output of seven methods are evaluated automatically. Results shed light over the comprehensive set of methods that are the state of the art according to the literature in the area.

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