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- Paper.193_Review.2 type ReviewVersion.
- Paper.193_Review.2 issued "2001-01-17T07:53:00.000Z".
- Paper.193_Review.2 creator Paper.193_Review.2_Reviewer.
- Paper.193_Review.2 hasRating ReviewRating.2.
- Paper.193_Review.2 hasReviewerConfidence ReviewerConfidence.4.
- Paper.193_Review.2 reviews Paper.193.
- Paper.193_Review.2 issuedAt easychair.org.
- Paper.193_Review.2 issuedFor Conference.
- Paper.193_Review.2 releasedBy Conference.
- Paper.193_Review.2 hasContent "The paper presents a method for inducing class hierarchies from knowledge graphs. The authors claim that the method is "simple" and is "scalable to large datasets". The method is based on ideas from tag hierarchy induction, i.e. counting classes and their co-occurrences. The approach is demonstrated based on different use cases with known datasets (Life, dbpedia, WikiData) and it is evaluated against other tag hierarchy induction methods. I like to "simpliness" of the approach allowing for performance and scalability while still showing convincing results. The paper is well written, the experiments have been clearly described and the data used for the experiments in publicly available."".