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- Paper.179_Review.0 type ReviewVersion.
- Paper.179_Review.0 issued "2001-01-13T11:45:00.000Z".
- Paper.179_Review.0 creator Paper.179_Review.0_Reviewer.
- Paper.179_Review.0 hasRating ReviewRating.1.
- Paper.179_Review.0 hasReviewerConfidence ReviewerConfidence.3.
- Paper.179_Review.0 reviews Paper.179.
- Paper.179_Review.0 issuedAt easychair.org.
- Paper.179_Review.0 issuedFor Conference.
- Paper.179_Review.0 releasedBy Conference.
- Paper.179_Review.0 hasContent "The authors present an approach to semi-autmatically learn and populate an ontology utilizing Czech language texts. The texts are first preprocessed and analyzed for surface forms representing entities and respective phrases representing the relations between the entities. Based on this extracted information several rules are applied to formulate the relationships. These relationships are presented to the user and asked for approval/rejection. The approach provides two different modi: a text is analyzed for a newly created ontology and instances, classes, properties have to be learned from the provided text. Or the ontology already contains classes/instances and the text is analyzed for specific entities found in the vocabulary and the user is presented links between these extracted entities according to the underlying ontology. The paper is concluded with an evaluation of the applicability of the formulated rules/patterns regarding recall and precision. Overall, it is eligible to provide development for entity linking within new languages. Especially, when the language differs in syntax from Germanic or Romanic family of languages. However, the authors seem to start from the very beginning of entity linking. For instance, they did not discuss previous work on Czech language by Michal Konkol: Konkol M. (2015) First Steps in Czech Entity Linking. In: Král P., Matoušek V. (eds) Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science, vol 9302. Springer, Cham The description of the identification of entities within textual information seems a bit naive as they describe that surface forms consisting of combined nouns are preferred over annotating each term separately. Here, the authors could have invested some more time on previous approaches even if they are in a different language. The authors present a set of 9 rules to be utlized for ontology learning. Unfortunately, there is no comparison to existing approaches (again, even for other languages). Is this set complete? Is it sufficient to find (almost) all relations mentioned in a text? Also, this issue is not included in the evaluation. Recall and precision is evaluated for each pattern separately, but there is no information, if all relationship contained in a text could be extraced using these rules. In addition, the authors could have mentioned results for other pattern-based approaches in ontology learning. In this way, the achieved results and the scientific contribution could be estimated although there is no other approach in Czech language."".