Abstract
Dynamic environments can be modeled as a series of events and facts that interact with each other, these interactions being characterised by different relations including temporal and causal ones. These have largely been studied in knowledge management, information retrieval or natural language processing, leading to several strategies aiming at extracting these relationships in textual documents. However, more relation types exist between events, which are insufficiently covered by existing data models and datasets if one needs to train a model to recognise them. In this paper, we use semantic web technologies to design FARO, an ontology for representing event and fact relations. FARO allows representing up to 25 distinct relationships (including logical constraints), making it a possible bridge between (otherwise incompatible) datasets. We describe the modeling decision of this ontology resource. In addition, we have re-annotated two already existing datasets with some of the FARO properties.
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Notes
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We can logically imagine here that the spread the pandemic caused the lockdown, which is in its turn a measure for preventing the worsening of pandemic.
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The text sample has been taken from https://economynext.com/sri-lanka-will-repay-bonds-holders-should-appreciate-efforts-made-cabraal-83785/. Last visited: 10/06/2022.
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Acknowledgements
This work has been partially supported by the French National Research Agency (ANR) within the kFLOW project (Grant nANR-21-CE23-0028).
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Rebboud, Y., Lisena, P., Troncy, R. (2022). Beyond Causality: Representing Event Relations in Knowledge Graphs. In: Corcho, O., Hollink, L., Kutz, O., Troquard, N., Ekaputra, F.J. (eds) Knowledge Engineering and Knowledge Management. EKAW 2022. Lecture Notes in Computer Science(), vol 13514. Springer, Cham. https://doi.org/10.1007/978-3-031-17105-5_9
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