Abel Browarnik

Tel Aviv University, Industrial Engineering, Department Member

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Ontology Learning from Text: Why the Ontology Learning Layer Cake is not Viable

The goal of Ontology Learning from Text is to learn ontologies that represent domains or applicat... more The goal of Ontology Learning from Text is to learn ontologies that represent domains or applications that change often. Manually learning and updating such ontologies is too expensive. This is the reason for the Ontology Learning discipline's emergence. The leading approach to Ontology Learning from Text is the Ontology Learning Layer Cake. This approach splits the task into four or five sequential tasks. Each of the tasks may use diverse methods, ranging from uses of Linguistic knowledge to Machine Learning. The authors review the shortcomings of the Ontology Learning Layer Cake approach and conclude that the approach is not viable for Ontology Learning from Text. They suggest alternative approaches that may help learning ontologies in an efficient, effective way.

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Subsentence Detection with Regular Expressions

Linguistic approach to semantic extraction from text

RAEL: revista electrónica de …, 2011

Ontology learning from text is the process of distilling knowledge -both implicit and explicit. M... more Ontology learning from text is the process of distilling knowledge -both implicit and explicit. Machines acquire knowledge either through human intervention or by means of an automatic, human-less learning approach, i.e. unsupervised ontology learning, using unsupervised, automatic understanding of text. Text understanding makes resort to Machine Learning or to a Linguistics-based approach. Both approaches require that a semantic representation of the text be obtained. This paper describes the context of Ontology learning, emphasizing the extraction of semantic content. We review the possible approaches and propose a heuristics based linguistic model for the automatic extraction of semantic content. The model examines the structure of the English sentence and corpus-based facts showing that sentence length is bound. This leads to the conclusion that it is possible to use finite state automata to heuristically detect clause boundaries within sentences. We show a clause-semantics retrieval example that could not be solved using other methods currently available. The semantics of the whole sentence can be obtained by combining the semantics of each individual constituent clause, based on the sentence structure found. A further paper will present the complete automaton for clause boundary detection, together with detailed results and a comparison to other available approaches.

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NHECD - Nano Health and Environmental Commented Database

Data Mining and Knowledge Discovery Handbook, 2009

The impact of nanoparticles on health and the environment is a significant research subject, driv... more The impact of nanoparticles on health and the environment is a significant research subject, driving increasing interest from the scientific community, regulatory bodies and the general public.We present a smart repository system with text and data mining for this domain. The growing body of knowledge in this area, consisting of scientific papers and other types of publications (such as surveys

Ontology Learning from Text Departing the Ontology Layer Cake

We analyze the ontology learning objectives, reviewing the type of input one would expect to meet... more We analyze the ontology learning objectives, reviewing the type of input one would expect to meet when learning ontologies -peer-reviewed scientific papers in English, papers that undergo quality control. We analyze the Ontology Learning Layer Cake model and its shortcomings, proposing alternative models for ontology learning based on linguistic knowledge and existing, wide coverage syntactical, lexical and semantic resources, using constructs such as clauses. We conclude, after showing that the Ontology Learning Layer Cake has a low maximum F measure (probably below 0.6), that the alternatives should be explored.

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Abel Browarnik

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