{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T16:40:48Z","timestamp":1760892048560,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031822247"},{"type":"electronic","value":"9783031822254"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"content-version":"vor","delay-in-days":86,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Object-centric process mining is recognized to overcome the limitations of traditional process mining by offering approaches for the analysis of processes with multiple case notions such as collaborations. Event knowledge graphs are an effective tool for gathering, manipulating, and visualizing event and entity relations. Current approaches focus on inferring correlations between events and objects and directly-follows relationships between events correlated to the same object. However, object-to-object relations may hide one-to-many relations between events essential for understanding the actual flow among processes. We propose an approach to reveal these one-to-many causal relationships in an event knowledge graph. By defining when two events are causally related and extending the standard approach of event knowledge graphs construction to reveal them. We assess the approach using two case studies.<\/jats:p>","DOI":"10.1007\/978-3-031-82225-4_14","type":"book-chapter","created":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T02:59:19Z","timestamp":1743303559000},"page":"184-196","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Revealing One-to-Many Event Relationships in\u00a0Event Knowledge Graphs"],"prefix":"10.1007","author":[{"given":"Alessio","family":"Giacch\u00e9","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5548-9806","authenticated-orcid":false,"given":"Sara","family":"Pettinari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6872-0616","authenticated-orcid":false,"given":"Lorenzo","family":"Rossi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"14_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-30446-1_1","volume-title":"Software Engineering and Formal Methods","author":"WMP Aalst","year":"2019","unstructured":"Aalst, W.M.P.: Object-centric process mining: dealing with divergence and convergence in event data. In: \u00d6lveczky, P.C., Sala\u00fcn, G. (eds.) SEFM 2019. LNCS, vol. 11724, pp. 3\u201325. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30446-1_1"},{"key":"14_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-08848-3","volume-title":"Process Mining Handbook","author":"WMP van der Aalst","year":"2022","unstructured":"van der Aalst, W.M.P., Carmona, J.: Process Mining Handbook. Springer, Cham (2022)"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Berti, A., van\u00a0der Aalst, W.M.P.: Extracting multiple viewpoint models from relational databases. In: Data-Driven Process Discovery and Analysis. LNBIP, vol.\u00a0379, pp. 24\u201351. 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