TY - CHAP
T1 - Departing the ontology layer cake
AU - Browarnik, Abel
AU - Maimon, Oded
N1 - Publisher Copyright:
© 2015, IGI Global. All rights reserved.
PY - 2015/7/17
Y1 - 2015/7/17
N2 - In this chapter we analyze Ontology Learning and its goals, as well as the input expected when learning ontologies - peer-reviewed scientific papers in English. After reviewing the Ontology Learning Layer Cake model's shortcomings we suggest an alternative model based on linguistic knowledge. The suggested model would find the meaning of simple components of text - statements. From them it is easy to derive cases and roles that map the reality as a set of entities and relationships or RDF triples, somehow equivalent to Entity-relationship diagrams. Time complexity for the suggested ontology learning framework is constant (O(1)) for a sentence, and O(n) for an ontology with n sentences. We conclude that the Ontology Learning Layer Cake is not adequate for Ontology Learning from text.
AB - In this chapter we analyze Ontology Learning and its goals, as well as the input expected when learning ontologies - peer-reviewed scientific papers in English. After reviewing the Ontology Learning Layer Cake model's shortcomings we suggest an alternative model based on linguistic knowledge. The suggested model would find the meaning of simple components of text - statements. From them it is easy to derive cases and roles that map the reality as a set of entities and relationships or RDF triples, somehow equivalent to Entity-relationship diagrams. Time complexity for the suggested ontology learning framework is constant (O(1)) for a sentence, and O(n) for an ontology with n sentences. We conclude that the Ontology Learning Layer Cake is not adequate for Ontology Learning from text.
UR - http://www.scopus.com/inward/record.url?scp=84957679559&partnerID=8YFLogxK
U2 - 10.4018/978-1-4666-8690-8.ch007
DO - 10.4018/978-1-4666-8690-8.ch007
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AN - SCOPUS:84957679559
SN - 1466686901
SN - 9781466686908
SP - 167
EP - 203
BT - Modern Computational Models of Semantic Discovery in Natural Language
PB - IGI Global
ER -