{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T07:14:48Z","timestamp":1775027688401,"version":"3.50.1"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T00:00:00Z","timestamp":1602720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Data and Information Quality"],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>\n            Entity-centric knowledge graphs (KGs) are now popular to collect facts about entities. KGs have rich schemas with a large number of different types and predicates to describe the entities and their relationships. On these rich schemas, logical rules are used to represent dependencies between the data elements. While rules are useful in query answering, data curation, and other tasks, they usually do not come with the KGs. Such rules have to be manually defined or discovered with the help of rule mining methods. We believe this rule-collection task should be done collectively to better capitalize our understanding of the data and to avoid redundant work conducted on the same KGs. For this reason, we introduce\n            <jats:italic>RuleHub<\/jats:italic>\n            , our extensible corpus of rules for public KGs. RuleHub provides functionalities for the archival and the retrieval of rules to all users, with an extensible architecture that does not constrain the KG or the type of rules supported. We are populating the corpus with thousands of rules from the most popular KGs and report on our experiments on automatically characterizing the quality of a rule with statistical measures.\n          <\/jats:p>","DOI":"10.1145\/3409384","type":"journal-article","created":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T04:07:35Z","timestamp":1602821255000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["RuleHub"],"prefix":"10.1145","volume":"12","author":[{"given":"Naser","family":"Ahmadi","sequence":"first","affiliation":[{"name":"EURECOM, France"}]},{"given":"Thi-Thuy-Duyen","family":"Truong","sequence":"additional","affiliation":[{"name":"EURECOM, France"}]},{"given":"Le-Hong-Mai","family":"Dao","sequence":"additional","affiliation":[{"name":"EURECOM, France"}]},{"given":"Stefano","family":"Ortona","sequence":"additional","affiliation":[{"name":"Meltwater, UK"}]},{"given":"Paolo","family":"Papotti","sequence":"additional","affiliation":[{"name":"EURECOM, France"}]}],"member":"320","published-online":{"date-parts":[[2020,10,15]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/2856318.2856328"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3054772"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Ziawasch Abedjan and Felix Naumann. 2014. Amending RDF entities with new facts. In ESWC. 131--143.  Ziawasch Abedjan and Felix Naumann. 2014. Amending RDF entities with new facts. 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B. Tettamanzi","author":"Tran Minh Duc","year":"2018"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/2371176"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196916"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v31i3.2303"},{"key":"e_1_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Mohamed H. Gad-Elrab Daria Stepanova Jacopo Urbani and Gerhard Weikum. 2016. Exception-enriched rule learning from knowledge graphs. In ISWC.  Mohamed H. Gad-Elrab Daria Stepanova Jacopo Urbani and Gerhard Weikum. 2016. Exception-enriched rule learning from knowledge graphs. In ISWC.","DOI":"10.1007\/978-3-319-46523-4_15"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-015-0394-1"},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Shu Guo Quan Wang Lihong Wang Bin Wang and Li Guo. 2016. Jointly embedding knowledge graphs and logical rules. In EMNLP. 192--202.  Shu Guo Quan Wang Lihong Wang Bin Wang and Li Guo. 2016. Jointly embedding knowledge graphs and logical rules. In EMNLP. 192--202.","DOI":"10.18653\/v1\/D16-1019"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/42.2.100"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358036"},{"key":"e_1_2_1_24_1","unstructured":"Internet Engineering Task Force (IETF). [n.d.]. The JavaScript Object Notation (JSON) Data Interchange Format. Retrieved from https:\/\/tools.ietf.org\/html\/std90.  Internet Engineering Task Force (IETF). [n.d.]. The JavaScript Object Notation (JSON) Data Interchange Format. Retrieved from https:\/\/tools.ietf.org\/html\/std90."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/33.3.239"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.14778\/3192965.3192968"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJMSO.2007.015073"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00108"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.14778\/3229863.3236231"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/11926078_3"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/1804669.1804675"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/1870658.1870764"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298239.3298420"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/69.553165"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242667"},{"key":"e_1_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Fabian M. Suchanek Jonathan Lajus Armand Boschin and Gerhard Weikum. 2019. Knowledge representation and rule mining in entity-centric knowledge bases. In Reasoning Web Summer School. 110--152.  Fabian M. Suchanek Jonathan Lajus Armand Boschin and Gerhard Weikum. 2019. Knowledge representation and rule mining in entity-centric knowledge bases. In Reasoning Web Summer School. 110--152.","DOI":"10.1007\/978-3-030-31423-1_4"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313584"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2362499.2362505"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2629489"},{"key":"e_1_2_1_40_1","volume-title":"RDF2Rules: Learning rules from RDF knowledge bases by mining frequent predicate cycles. CoRR abs\/1512.07734","author":"Wang Zhichun","year":"2015"},{"key":"e_1_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Dominik Wienand and Heiko Paulheim. 2014. Detecting incorrect numerical data in DBpedia. In ESWC.  Dominik Wienand and Heiko Paulheim. 2014. Detecting incorrect numerical data in DBpedia. In ESWC.","DOI":"10.1007\/978-3-319-07443-6_34"},{"key":"e_1_2_1_42_1","doi-asserted-by":"crossref","unstructured":"V\u00e1clav Zeman Tom\u00e1s Kliegr and Vojtech Sv\u00e1tek. 2018. RdfRules preview: Towards an analytics engine for rule mining in RDF knowledge graphs. In RuleML+ RR (Supplement).  V\u00e1clav Zeman Tom\u00e1s Kliegr and Vojtech Sv\u00e1tek. 2018. RdfRules preview: Towards an analytics engine for rule mining in RDF knowledge graphs. In RuleML+ RR (Supplement).","DOI":"10.29007\/nkv7"}],"container-title":["Journal of Data and Information Quality"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3409384","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3409384","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:38:40Z","timestamp":1750199920000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3409384"}},"subtitle":["A Public Corpus of Rules for Knowledge Graphs"],"short-title":[],"issued":{"date-parts":[[2020,10,15]]},"references-count":42,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,12,31]]}},"alternative-id":["10.1145\/3409384"],"URL":"https:\/\/doi.org\/10.1145\/3409384","relation":{},"ISSN":["1936-1955","1936-1963"],"issn-type":[{"value":"1936-1955","type":"print"},{"value":"1936-1963","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,15]]},"assertion":[{"value":"2019-10-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-06-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-10-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}