Efficient handling of multiple inheritance hierarchies
OOPSLA '93 Proceedings of the eighth annual conference on Object-oriented programming systems, languages, and applications
Compact labeling schemes for ancestor queries
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
A comparison of labeling schemes for ancestor queries
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Informative Labeling Schemes for Graphs
MFCS '00 Proceedings of the 25th International Symposium on Mathematical Foundations of Computer Science
Benchmarking RDF Schemas for the Semantic Web
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
On labeling schemes for the semantic web
WWW '03 Proceedings of the 12th international conference on World Wide Web
Using compact encodings for path-based computations on pedigree graphs
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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We consider the problem of finding a compact labelling for large, rooted web taxonomies that can be used to encode all local path information for each taxonomy element. This research is motivated by the problem of developing standards for taxonomic data, and addresses the data intensive problem of evaluating semantic similarities between taxonomic elements. Evaluating such similarities often requires the processing of large common ancestor sets between elements. We propose a new class of compact labelling schemes, designed for directed acyclic graphs, and tailored for applications to large web taxonomies. Our labelling schemes significantly reduce the complexity of evaluating similarities among taxonomy elements by enabling the gleaning of inferences from the labels alone, without searching the data structure. We provide an analysis of the label lengths for the proposed schemes based on structural properties of the taxonomy. Finally, we provide supporting empirical evidence for the quality of these schemes by evaluating the performance on the WordNet taxonomy.