{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:20:04Z","timestamp":1743042004335,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030974534"},{"type":"electronic","value":"9783030974541"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-97454-1_5","type":"book-chapter","created":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T20:02:28Z","timestamp":1645646548000},"page":"57-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthetic Datasets and Evaluation Tools for Inductive Neural Reasoning"],"prefix":"10.1007","author":[{"given":"Cristina","family":"Cornelio","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Veronika","family":"Thost","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Alexe, B., Tan, W.C., Velegrakis, Y.: Stbenchmark: towards a benchmark for mapping systems. Proc. VLDB Endow. 1(1), 230\u2013244 (2008)","DOI":"10.14778\/1453856.1453886"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Arocena, P.C., Glavic, B., Ciucanu, R., Miller, R.J.: The ibench integration metadata generator. Proc. VLDB Endow. 9(3) (2015)","DOI":"10.14778\/2850583.2850586"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Benedikt, M., et al.: Benchmarking the chase. In: Proceedings of PODS. ACM, pp. 37\u201352 (2017)","DOI":"10.1145\/3034786.3034796"},{"key":"5_CR4","unstructured":"Campero, A., Pareja, A., Klinger, T., Tenenbaum, J., Riedel, S.: Logical rule induction and theory learning using neural theorem proving. CoRR abs\/1809.02193 (2018)"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Ceri, S., Gottlob, G., Tanca, L.: What you always wanted to know about datalog (and never dared to ask). In: IEEE Trans. on Knowl. and Data Eng. 1(1), 146\u2013166 (1989)","DOI":"10.1109\/69.43410"},{"key":"5_CR6","unstructured":"Dong, H., Mao, J., Lin, T., Wang, C., Li, L., Zhou, D.: Neural logic machines. In: Proceedings of ICLR (2019)"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Dong, X.L., et al.: Knowledge vault: A web-scale approach to probabilistic knowledge fusion. In: Proceedings of KDD, pp. 601\u2013610 (2014)","DOI":"10.1145\/2623330.2623623"},{"key":"5_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/11536314_6","volume-title":"Inductive Logic Programming","author":"V Estruch","year":"2005","unstructured":"Estruch, V., Ferri, C., Hern\u00e1ndez-Orallo, J., Ram\u00edrez-Quintana, M.J.: Distance based generalisation. In: Kramer, S., Pfahringer, B. (eds.) ILP 2005. LNCS (LNAI), vol. 3625, pp. 87\u2013102. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11536314_6"},{"key":"5_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1007\/978-3-642-12251-4_12","volume-title":"Functional and Logic Programming","author":"V Estruch","year":"2010","unstructured":"Estruch, V., Ferri, C., Hern\u00e1ndez-Orallo, J., Ram\u00edrez-Quintana, M.J.: An integrated distance for atoms. In: Blume, M., Kobayashi, N., Vidal, G. (eds.) FLOPS 2010. LNCS, vol. 6009, pp. 150\u2013164. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-12251-4_12"},{"key":"5_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1613\/jair.5714","volume":"61","author":"R Evans","year":"2018","unstructured":"Evans, R., Grefenstette, E.: Learning explanatory rules from noisy data. J. Artif. Intell. Res. 61, 1\u201364 (2018)","journal-title":"J. Artif. Intell. Res."},{"key":"5_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-75197-7","volume-title":"Foundations of Rule Learning","author":"J F\u00fcrnkranz","year":"2012","unstructured":"F\u00fcrnkranz, J., Gamberger, D., Lavrac, N.: Foundations of Rule Learning. Springer, Cognitive Technologies (2012)"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Gal\u00e1rraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB J. 24(6), 707\u2013730 (2015), code available at https:\/\/www.mpi-inf.mpg.de\/departments\/databases-and-information-systems\/research\/yago-naga\/amie\/","DOI":"10.1007\/s00778-015-0394-1"},{"key":"5_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1007\/978-3-030-00671-6_5","volume-title":"The Semantic Web \u2013 ISWC 2018","author":"VT Ho","year":"2018","unstructured":"Ho, V.T., Stepanova, D., Gad-Elrab, M.H., Kharlamov, E., Weikum, G.: Rule learning from knowledge graphs guided by embedding models. In: Vrande\u010di\u0107, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 72\u201390. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00671-6_5"},{"key":"5_CR14","unstructured":"ILP: ILP Applications and Datasets. https:\/\/www.doc.ic.ac.uk\/~shm\/applications.html (year na). Accessed 09 Mar 2020"},{"key":"5_CR15","unstructured":"de Jong, M., Sha, F.: Neural theorem provers do not learn rules without exploration. ArXiv abs\/1906.06805 (2019)"},{"key":"5_CR16","unstructured":"Krishnan, A.: Making search easier (2018). https:\/\/blog.aboutamazon.com\/innovation\/making-search-easier. Accessed 03 Sept 2020"},{"key":"5_CR17","unstructured":"Minervini, P., Bosnjak, M., Rockt\u00e4schel, T., Riedel, S.: Towards neural theorem proving at scale. In: Proceedings of NAMPI (2018)"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Minervini, P., Bo\u0161njak, M., Rockt\u00e4schel, T., Riedel, S., Grefenstette, E.: Differentiable reasoning on large knowledge bases and natural language. In: Proceedings of AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 5182\u20135190 (2020)","DOI":"10.1609\/aaai.v34i04.5962"},{"issue":"3&4","key":"5_CR19","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/BF03037227","volume":"13","author":"S Muggleton","year":"1995","unstructured":"Muggleton, S.: Inverse entailment and progol. New Gen. Comput. 13(3&4), 245\u2013286 (1995)","journal-title":"New Gen. Comput."},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Nienhuys-Cheng, S., de\u00a0Wolf, R.: Foundations of Inductive Logic Programming, vol. 1228. Springer (1997)","DOI":"10.1007\/3-540-62927-0"},{"key":"5_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/3540635149_50","volume-title":"Inductive Logic Programming","author":"S-H Nienhuys-Cheng","year":"1997","unstructured":"Nienhuys-Cheng, S.-H.: Distance between herbrand interpretations: a measure for approximations to a target concept. In: Lavra\u010d, N., D\u017eeroski, S. (eds.) ILP 1997. LNCS, vol. 1297, pp. 213\u2013226. Springer, Heidelberg (1997). https:\/\/doi.org\/10.1007\/3540635149_50"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Omran, P.G., Wang, K., Wang, Z.: Scalable rule learning via learning representation. In: Proceedings of IJCAI, pp. 2149\u20132155 (2018)","DOI":"10.24963\/ijcai.2018\/297"},{"key":"5_CR23","unstructured":"Preda, M.: Metrics for sets of atoms and logic programs. Ann. Univ. Craiova 33, 67\u201378 (2006)"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Quinlan, J.R.: Learning logical definitions from relations. Mach. Learn. 5, 239\u2013266 (1990). code available at http:\/\/www.cs.cmu.edu\/afs\/cs\/project\/ai-repository\/ai\/areas\/learning\/systems\/foil\/foil6\/0.html","DOI":"10.1007\/BF00117105"},{"key":"5_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-68856-3","volume-title":"Logical and Relational Learning","author":"LD Raedt","year":"2008","unstructured":"Raedt, L.D.: Logical and Relational Learning. Springer, Cognitive Technologies (2008)"},{"key":"5_CR26","unstructured":"Ren, H., Hu, W., Leskovec, J.: Query2box: reasoning over knowledge graphs in vector space using box embeddings. In: Proceedings of ICLR (2020)"},{"key":"5_CR27","unstructured":"Rockt\u00e4schel, T., Riedel, S.: End-to-end differentiable proving. In: Proceedings of NeurIPS, pp. 3791\u20133803 (2017). code available at https:\/\/github.com\/uclmr\/ntp"},{"key":"5_CR28","unstructured":"Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall Press (2002)"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Seda, A.K., Lane, M.: On continuous models of computation: towards computing the distance between (logic) programs. In: Proceedings of IWFM (2003)","DOI":"10.14236\/ewic\/IWFM2003.1"},{"key":"5_CR30","unstructured":"Sinha, K., Sodhani, S., Pineau, J., Hamilton, W.L.: Evaluating logical generalization in graph neural networks. ArXiv abs\/2003.06560 (2020)"},{"key":"5_CR31","unstructured":"Sinha, K., Sodhani, S., Pineau, J., Hamilton, W.L.: Evaluating logical generalization in graph neural networks (2020)"},{"key":"5_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/978-3-030-00338-8_6","volume-title":"Reasoning Web. Learning, Uncertainty, Streaming, and Scalability","author":"D Stepanova","year":"2018","unstructured":"Stepanova, D., Gad-Elrab, M.H., Ho, V.T.: Rule induction and reasoning over knowledge graphs. In: d\u2019Amato, C., Theobald, M. (eds.) Reasoning Web 2018. LNCS, vol. 11078, pp. 142\u2013172. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00338-8_6"},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Vaclav Zeman, T.K., Sv\u00e1tek, V.: Rdfrules: Making RDF rule mining easier and even more efficient. Semant-Web-J. 12(4), 569\u2013602 (2019)","DOI":"10.3233\/SW-200413"},{"key":"5_CR34","unstructured":"Wang, Z., Li, J.: Rdf2rules: Learning rules from RDF knowledge bases by mining frequent predicate cycles. CoRR abs\/1512.07734 (2015)"},{"key":"5_CR35","unstructured":"Yang, F., Yang, Z., Cohen, W.W.: Differentiable learning of logical rules for knowledge base reasoning. In: Proc. of NeurIPS, pp. 2316\u20132325 (2017). https:\/\/github.com\/fanyangxyz\/Neural-LP"},{"key":"5_CR36","unstructured":"Yang, Y., Song, L.: Learn to explain efficiently via neural logic inductive learning. In: Proceedings of ICLR (2020)"}],"container-title":["Lecture Notes in Computer Science","Inductive Logic Programming"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-97454-1_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T03:36:02Z","timestamp":1726716962000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-97454-1_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030974534","9783030974541"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-97454-1_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ILP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Inductive Logic Programming","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ilp2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/lr2020.iit.demokritos.gr\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"160% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}