Ontology Learning and Population from Text

Algorithms, Evaluation and Applications

  • Philipp Cimiano

Table of contents

  1. Front Matter
    Pages i-xxviii
  2. Preliminaries

    1. Front Matter
      Pages 1-1
    2. Pages 3-7
    3. Pages 9-17
    4. Pages 35-75
    5. Pages 77-81
  3. Methods and Applications

    1. Front Matter
      Pages 83-83
    2. Pages 233-280
    3. Pages 281-305
  4. Conclusion

    1. Front Matter
      Pages 307-307
    2. Pages 309-310
    3. Pages 311-312
  5. Back Matter
    Pages 313-347

About this book


Standard formalisms for knowledge representation such as RDFS or OWL have been recently developed by the semantic web community and are now in place. However, the crucial question still remains: how will we acquire all the knowledge available in people's heads to feed our machines?

Natural language is THE means of communication for humans, and consequently texts are massively available on the Web. Terabytes and terabytes of texts containing opinions, ideas, facts and information of all sorts are waiting to be mined for interesting patterns and relationships, or used to annotate documents to facilitate their retrieval. A semantic web which ignores the massive amount of information encoded in text, might actually be a semantic, but not a very useful, web. Knowledge acquisition, and in particular ontology learning from text, actually has to be regarded as a crucial step within the vision of a semantic web.

Ontology Learning and Population from Text: Algorithms, Evaluation and Applications presents approaches for ontology learning from text and will be relevant for researchers working on text mining, natural language processing, information retrieval, semantic web and ontologies. Containing introductory material and a quantity of related work on the one hand, but also detailed descriptions of algorithms, evaluation procedures etc. on the other, this book is suitable for novices, and experts in the field, as well as lecturers.

Datasets, algorithms and course material can be downloaded at Ontology Learning and Population from Text: Algorithms, Evaluation and Applications is designed for practitioners in industry, as well researchers and graduate-level students in computer science.


algorithms classification computer computer science knowledge management learning ontology semantic web

Authors and affiliations

  • Philipp Cimiano
    • 1
  1. 1.Inst. AIFBUniv. KarlsruheKarlsruheGermany

Bibliographic information