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Ontology Learning and Population from Text Algorithms, Evaluation and Applications von Cimiano, Philipp (eBook)

  • Erscheinungsdatum: 11.12.2006
  • Verlag: Springer-Verlag
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Ontology Learning and Population from Text

In the last decade, ontologies have received much attention within computer science and related disciplines, most often as the semantic web. Ontology Learning and Population from Text: Algorithms, Evaluation and Applications discusses ontologies for the semantic web, as well as knowledge management, information retrieval, text clustering and classification, as well as natural language processing. Ontology Learning and Population from Text: Algorithms, Evaluation and Applications is structured for research scientists and practitioners in industry. This book is also suitable for graduate-level students in computer science.


    Format: PDF
    Kopierschutz: AdobeDRM
    Seitenzahl: 347
    Erscheinungsdatum: 11.12.2006
    Sprache: Englisch
    ISBN: 9780387392523
    Verlag: Springer-Verlag
    Größe: 19571kBytes
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Ontology Learning and Population from Text

10 Contribution and Outlook (p. 309-310)

This book contributes to the state-of-the-art in ontology learning in several ways. First, we have provided a formal definition of ontology learning tasks with respect to a well-defined ontology model. The ontology learning layer cake, a model for representing the diverse subtasks in ontology learning has been introduced. In addition, evaluation measures for the concept hierarchy induction, relation learning as well as ontology population tasks have been defined. These evaluation measures provide a basis in order to compare different approaches performing a certain task. Most importantly, several original and novel approaches performing a certain task have been presented and compared to other state-of-the-art approaches from the literature using the defined evaluation measures.

Concerning the concept hierarchy induction task, we have presented a novel approach based on Formal Concept Analysis, an original guided agglomerative clustering method as well as a combination approach for the induction of concept hierarchies from text. All the approaches have been evaluated and have been demonstrated to actually outperform current state-of-the-art methods. We have further introduced and discussed several approaches to learning attributes and relations. In particular, we have presented approaches to learn i) attributes, ii) the appropriate domain and range for relations, as well as iii) specific relations using a pattern-based approach. Several approaches to automatically populate an ontology with instances have also been described. We have in particular examined a similarity-based approach as well as introduced the original approach of Learning By Googling. Corresponding evaluations have also been provided. Finally, we have have also discussed applications for ontology learning approaches and demonstrated for two concrete applications that the techniques developed in the context of this book are indeed useful. Throughout the book, we have also provided a thorough overview of related work.

Fortunately, there are a number of open issues which require further research. On the one hand, though we have undertaken a first step towards combining different ontology learning paradigms via a machine-learning approach. further research is needed in this direction to unveil the full potential of such a combination. In particular, other paradigms than our classification-based approach could be explored. One could imagine to train classifiers for each type of basic ontological relation, i.e. isa, part-of, etc. using different methods and then use a calculus as envisioned by Heyer and colleagues [Heyer et al., 2001] as well as Ogata and Collier [Ogata and Collier, 2004] to combine the results of these classifiers and reason on different types of extracted ontological relations. Such a post-extraction reasoning is in fact crucial as the different approaches can produce contradicting information and thus producing a consistent ontology needs some kind of contradiction resolution approach. In fact, one important problem is to generate the optimal ontology maximizing a certain criterion given a certain amount of - possibly contradicting - relations. Initial blueprints for such an approach can be found, for example, in the work of Haase and Volker [Haase and Volker, 2005]. A lot of further research is however needed in this direction.

Another important issue to be clarified is which similarity measures, which weighting measures and which features work best for the task of clustering words. Though we have provided some insights in the present book, much more work is needed to clarify these issues. In the same vein, further experiments are necessary to clarify the relation between syntactic and semantic similarity such as perceived by humans. These issues can only be approached from an experimental perspective. Though ther

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