{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T08:22:10Z","timestamp":1792743730394,"version":"4.5.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031606250","type":"print"},{"value":"9783031606267","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-60626-7_10","type":"book-chapter","created":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T23:02:38Z","timestamp":1716073358000},"page":"180-198","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Explanation of\u00a0Link Predictions on\u00a0Knowledge Graphs via\u00a0Levelwise Filtering and\u00a0Graph Summarization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3058-8692","authenticated-orcid":false,"given":"Roberto","family":"Barile","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3385-987X","authenticated-orcid":false,"given":"Claudia","family":"d\u2019Amato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5319-7933","authenticated-orcid":false,"given":"Nicola","family":"Fanizzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,19]]},"reference":[{"key":"10_CR1","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1007\/978-3-540-76298-0_52","volume-title":"ISWC 2007","author":"S Auer","year":"2007","unstructured":"Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ISWC 2007. LNCS, vol. 4825, pp. 722\u2013735. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-76298-0_52"},{"key":"10_CR2","unstructured":"Baltatzis, V., Costabello, L.: Kgex: explaining knowledge graph embeddings via subgraph sampling and knowledge distillation. arXiv preprint arXiv:2310.01065 (2023)"},{"key":"10_CR3","doi-asserted-by":"publisher","unstructured":"Betz, P., Meilicke, C., Stuckenschmidt, H.: Adversarial explanations for knowledge graph embeddings. In: IJCAI-22. International Joint Conferences on Artificial Intelligence Organization, pp. 2820\u20132826 (2022). https:\/\/doi.org\/10.24963\/ijcai.2022\/391","DOI":"10.24963\/ijcai.2022\/391"},{"key":"10_CR4","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/978-3-030-62419-4_3","volume-title":"ISWC 2020","author":"R Bhowmik","year":"2020","unstructured":"Bhowmik, R., de Melo, G.: Explainable link prediction for emerging entities in knowledge graphs. In: Pan, J.Z., et al. (eds.) ISWC 2020. LNCS, vol. 12506, pp. 39\u201355. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-62419-4_3"},{"key":"10_CR5","doi-asserted-by":"publisher","unstructured":"Bollacker, K., Cook, R., Tufts, P.: Freebase: a shared database of structured general human knowledge. In: AAAI 2007, pp. 1962\u20131963. AAAI Press (2007). https:\/\/doi.org\/10.5555\/1619797.1619981","DOI":"10.5555\/1619797.1619981"},{"key":"10_CR6","doi-asserted-by":"publisher","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, vol. 26 (2013). https:\/\/doi.org\/10.5555\/2999792.2999923","DOI":"10.5555\/2999792.2999923"},{"issue":"12","key":"10_CR7","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.14778\/2994509.2994531","volume":"9","author":"P Buneman","year":"2016","unstructured":"Buneman, P., Staworko, S.: RDF graph alignment with bisimulation. Proc. VLDB Endow. 9(12), 1149\u20131160 (2016). https:\/\/doi.org\/10.14778\/2994509.2994531","journal-title":"Proc. VLDB Endow."},{"issue":"3","key":"10_CR8","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s00778-018-0528-3","volume":"28","author":"\u0160 \u010cebiri\u0107","year":"2019","unstructured":"\u010cebiri\u0107, \u0160, et al.: Summarizing semantic graphs: a survey. VLDB J. 28(3), 295\u2013327 (2019). https:\/\/doi.org\/10.1007\/s00778-018-0528-3","journal-title":"VLDB J."},{"key":"10_CR9","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1007\/978-3-030-93413-2_61","volume-title":"Complex Networks and Their Applications X 2021","author":"S Cohen","year":"2022","unstructured":"Cohen, S., et al.: Drug repurposing using link prediction on knowledge graphs with applications to non-volatile memory. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds.) COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol. 1073, pp. 742\u2013753. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-93413-2_61"},{"key":"10_CR10","doi-asserted-by":"publisher","unstructured":"d\u2019Amato, C., Masella, P., Fanizzi, N.: An approach based on semantic similarity to explaining link predictions on knowledge graphs. In: WI-IAT 2021, pp. 170\u2013177 (2021). https:\/\/doi.org\/10.1145\/3486622.3493956","DOI":"10.1145\/3486622.3493956"},{"key":"10_CR11","doi-asserted-by":"publisher","unstructured":"Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: AAAI 2018. AAAI Press (2018). https:\/\/doi.org\/10.5555\/3504035.3504256","DOI":"10.5555\/3504035.3504256"},{"key":"10_CR12","doi-asserted-by":"publisher","unstructured":"Ding, B., Wang, Q., Wang, B., Guo, L.: Improving knowledge graph embedding using simple constraints. In: ACL 2018, pp. 110\u2013121. ACL (2018). https:\/\/doi.org\/10.18653\/v1\/P18-1011","DOI":"10.18653\/v1\/P18-1011"},{"key":"10_CR13","doi-asserted-by":"publisher","unstructured":"Dong, X.L.: Building a broad knowledge graph for products. In: IEEE-ICDE 2019, pp. 25\u201325. IEEE (2019). https:\/\/doi.org\/10.1109\/ICDE.2019.00010","DOI":"10.1109\/ICDE.2019.00010"},{"key":"10_CR14","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/3-540-44585-4_8","volume-title":"CAV 2001","author":"A Dovier","year":"2001","unstructured":"Dovier, A., Piazza, C., Policriti, A.: A fast bisimulation algorithm. In: Berry, G., Comon, H., Finkel, A. (eds.) CAV 2001. LNCS, vol. 2102, pp. 79\u201390. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44585-4_8"},{"key":"10_CR15","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1023\/A:1027328830731","volume":"31","author":"R Gentilini","year":"2003","unstructured":"Gentilini, R., Piazza, C., Policriti, A.: From bisimulation to simulation: coarsest partition problems. J. Autom. Reason. 31, 73\u2013103 (2003). https:\/\/doi.org\/10.1023\/A:1027328830731","journal-title":"J. Autom. Reason."},{"issue":"3","key":"10_CR16","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s10817-014-9305-1","volume":"53","author":"B Glimm","year":"2014","unstructured":"Glimm, B., Horrocks, I., Motik, B., Stoilos, G., Wang, Z.: HermiT: an OWL 2 reasoner. J. Autom. Reason. 53(3), 245\u2013269 (2014). https:\/\/doi.org\/10.1007\/s10817-014-9305-1","journal-title":"J. Autom. Reason."},{"issue":"5","key":"10_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2018","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. 51(5), 1\u201342 (2018). https:\/\/doi.org\/10.1145\/3236009","journal-title":"ACM Comput. Surv."},{"key":"10_CR18","doi-asserted-by":"publisher","unstructured":"Hogan, A., et al.: Knowledge Graphs. No. 22 in Synthesis Lectures on Data, Semantics, and Knowledge, Springer, Switzerland (2021). https:\/\/doi.org\/10.2200\/S01125ED1V01Y202109DSK022, https:\/\/kgbook.org\/","DOI":"10.2200\/S01125ED1V01Y202109DSK022"},{"key":"10_CR19","doi-asserted-by":"publisher","unstructured":"Koh, P.W., Liang, P.: Understanding black-box predictions via influence functions. In: ICML 2017, pp. 1885\u20131894. PMLR (2017). https:\/\/doi.org\/10.5555\/3305381.3305576","DOI":"10.5555\/3305381.3305576"},{"key":"10_CR20","doi-asserted-by":"publisher","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017). https:\/\/doi.org\/10.5555\/3295222.3295230","DOI":"10.5555\/3295222.3295230"},{"issue":"11","key":"10_CR21","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1145\/3276742","volume":"61","author":"D Monroe","year":"2018","unstructured":"Monroe, D.: AI, explain yourself. Commun. ACM 61(11), 11\u201313 (2018). https:\/\/doi.org\/10.1145\/3276742","journal-title":"Commun. ACM"},{"issue":"6","key":"10_CR22","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1137\/0216062","volume":"16","author":"R Paige","year":"1987","unstructured":"Paige, R., Tarjan, R.E.: Three partition refinement algorithms. SIAM J. Comput. 16(6), 973\u2013989 (1987). https:\/\/doi.org\/10.1137\/0216062","journal-title":"SIAM J. Comput."},{"key":"10_CR23","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1007\/978-3-030-49461-2_34","volume-title":"ESWC 2020","author":"T Pellissier Tanon","year":"2020","unstructured":"Pellissier Tanon, T., Weikum, G., Suchanek, F.: YAGO 4: a reason-able knowledge base. In: Harth, A., et al. (eds.) ESWC 2020. LNCS, vol. 12123, pp. 583\u2013596. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-49461-2_34"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Pezeshkpour, P., Tian, Y., Singh, S.: Investigating robustness and interpretability of link prediction via adversarial modifications. arXiv preprint arXiv:1905.00563 (2019)","DOI":"10.18653\/v1\/N19-1337"},{"issue":"2","key":"10_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3424672","volume":"15","author":"A Rossi","year":"2021","unstructured":"Rossi, A., Barbosa, D., Firmani, D., Matinata, A., Merialdo, P.: Knowledge graph embedding for link prediction: a comparative analysis. ACM Trans. Knowl. Discovery Data (TKDD) 15(2), 1\u201349 (2021). https:\/\/doi.org\/10.1145\/3424672","journal-title":"ACM Trans. Knowl. Discovery Data (TKDD)"},{"key":"10_CR26","doi-asserted-by":"publisher","unstructured":"Rossi, A., Firmani, D., Merialdo, P., Teofili, T.: Explaining link prediction systems based on knowledge graph embeddings. In: SIGMOD 2022, pp. 2062\u20132075 (2022). https:\/\/doi.org\/10.1145\/3514221.3517887","DOI":"10.1145\/3514221.3517887"},{"key":"10_CR27","doi-asserted-by":"publisher","unstructured":"Rousseeuw, P.J., Hampel, F.R., Ronchetti, E.M., Stahel, W.A.: Robust Statistics: The Approach Based on Influence Functions. Wiley, Hoboken (2011). https:\/\/doi.org\/10.1002\/9781118186435","DOI":"10.1002\/9781118186435"},{"key":"10_CR28","doi-asserted-by":"publisher","unstructured":"Shapley, L.S.: A value for n-person games. In: Contributions to the Theory of Games II (1953). Princeton University Press (1997). https:\/\/doi.org\/10.1515\/9781400829156-012","DOI":"10.1515\/9781400829156-012"},{"key":"10_CR29","doi-asserted-by":"publisher","unstructured":"Shi, B., Weninger, T.: Open-world knowledge graph completion. In: AAAI 2018, vol. 32 (2018). https:\/\/doi.org\/10.1609\/aaai.v32i1.11535","DOI":"10.1609\/aaai.v32i1.11535"},{"key":"10_CR30","unstructured":"Singhal, A.: Introducing the knowledge graph: things, not strings. https:\/\/blog.google\/products\/search\/introducing-knowledge-graph-things-not"},{"key":"10_CR31","doi-asserted-by":"publisher","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML 2016, pp. 2071\u20132080. PMLR (2016). https:\/\/doi.org\/10.5555\/3045390.3045609","DOI":"10.5555\/3045390.3045609"},{"key":"10_CR32","unstructured":"Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: GNNExplainer: generating explanations for graph neural networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019). https:\/\/dl.acm.org\/doi\/10.5555\/3454287.3455116"},{"key":"10_CR33","doi-asserted-by":"publisher","unstructured":"Zhang, H., et al.: Data poisoning attack against knowledge graph embedding. In: International Joint Conference on Artificial Intelligence, pp. 4853\u20134859 (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/674","DOI":"10.24963\/ijcai.2019\/674"},{"key":"10_CR34","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/978-981-15-3412-6_8","volume-title":"JIST 2019","author":"W Zhang","year":"2020","unstructured":"Zhang, W., Deng, S., Wang, H., Chen, Q., Zhang, W., Chen, H.: Xtranse: explainable knowledge graph embedding for link prediction with lifestyles in e-commerce. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds.) JIST 2019. LNCS, vol. 1157, pp. 78\u201387. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-3412-6_8"},{"key":"10_CR35","doi-asserted-by":"publisher","unstructured":"Zhang, W., Paudel, B., Zhang, W., Bernstein, A., Chen, H.: Interaction embeddings for prediction and explanation in knowledge graphs. In: WSDM 2019, pp. 96\u2013104 (2019). https:\/\/doi.org\/10.1145\/3289600.3291014","DOI":"10.1145\/3289600.3291014"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-60626-7_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T23:04:04Z","timestamp":1716073444000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-60626-7_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031606250","9783031606267"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-60626-7_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"19 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare to have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ESWC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Semantic Web Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hersonissos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"esws2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.eswc-conferences.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}