{"type":"ScholarlyArticle","title":"Efficient Pruning of Large Knowledge Graphs","authors":[{"name":"Stefano Faralli","slug":"stefano-faralli","given":"Stefano","family":"Faralli","affiliation":"University of Rome Unitelma Sapienza; University of Mannheim ."},{"name":"Irene Finocchi","slug":"irene-finocchi","given":"Irene","family":"Finocchi","affiliation":"University of Rome Sapienza"},{"name":"Simone Paolo Ponzetto","slug":"simone-paolo-ponzetto","given":"Simone Paolo","family":"Ponzetto","affiliation":"University of Mannheim"},{"name":"Paola Velardi","slug":"paola-velardi","given":"Paola","family":"Velardi","affiliation":"University of Rome Sapienza"}],"abstract":"In this paper we present an efficient and highly accurate algorithm to prune noisy or over-ambiguous knowledge graphs given as input an extensional definition of a domain of interest, namely as a set of instances or concepts. Our method climbs the graph in a bottom-up fashion, iteratively layering the graph and pruning nodes and edges in each layer while not compromising the connectivity of the set of input nodes. Iterative layering and protection of pre-defined nodes allow to extract semantically coherent DAG structures from noisy or over-ambiguous cyclic graphs, without loss of information and without incurring in computational bottlenecks, which are the main problem of state-of-the-art methods for cleaning large, i.e., Web-scale, knowledge graphs. We apply our algorithm to the tasks of pruning automatically acquired taxonomies using benchmarking data from a SemEval evaluation exercise, as well as the extraction of a domain-adapted taxonomy from the Wikipedia category hierarchy. The results show the superiority of our approach over state-of-art algorithms in terms of both output quality and computational efficiency.","doi":"10.24963/ijcai.2018/564","url":"https://ijcai.org/proceedings/2018/564","pdf":"https://ijcai.org/proceedings/2018/0564.pdf","markdown":"https://ijcai.org/proceedings/2018/564.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/564","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":564,"pages":{"first":4055,"last":4063},"keywords":["Multidisciplinary Topics and Applications: AI and the Web","Natural Language Processing: Knowledge Extraction"],"videos":[],"references":[{"position":1,"raw":"[Berger and Shor, 1990] Bonnie Berger and Peter W. 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