2010/03/13 by Joanna Józefowska, Jozefowska, Joanna, Agnieszka Ławrynowicz +3
Computer Science · #68T27 #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #Rough Sets and Fuzzy Logic #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.1003.2700
openalex publication_date 2010/03/13 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
We propose a new method for mining frequent patterns in a language that\ncombines both Semantic Web ontologies and rules. In particular we consider the\nsetting of using a language that combines description logics with DL-safe\nrules. This setting is important for the practical application of data mining\nto the Semantic Web. We focus on the relation of the semantics of the\nrepresentation formalism to the task of frequent pattern discovery, and for the\ncore of our method, we propose an algorithm that exploits the semantics of the\ncombined knowledge base. We have developed a proof-of-concept data mining\nimplementation of this. Using this we have empirically shown that using the\ncombined knowledge base to perform semantic tests can make data mining faster\nby pruning useless candidate patterns before their evaluation. We have also\nshown that the quality of the set of patterns produced may be improved: the\npatterns are more compact, and there are fewer patterns. We conclude that\nexploiting the semantics of a chosen representation formalism is key to the\ndesign and application of (onto-)relational frequent pattern discovery methods.\nNote: To appear in Theory and Practice of Logic Programming (TPLP)\n