Victor David
ABSTRACT
Reasoning on inconsistent and uncertain data is challenging, especially for Knowledge-Graphs (KG) to abide temporal consistency. Our goal is to enhance inference with more general time interval semantics that specify their validity, as regularly found in historical sciences. We propose a new Temporal Markov Logic Networks (TMLN) model which extends the Markov Logic Networks (MLN) model with uncertain temporal facts and rules. Total and partial temporal (in)consistency relations between sets of temporal formulae are examined. We then propose a new Temporal Parametric Semantics (TPS) which allows combining several sub-functions leading to different assessment strategies. Finally, we present the new NeoMaPy tool, to compute the MAP inference on MLNs and TMLNs with several TPS. We compare our performances with state-of-the-art inference tools and exhibit faster and higher quality results.
- Jacky Akoka, Isabelle Comyn-Wattiau, Cédric du Mouza, and Nicolas Prat. 2021. Mapping Multidimensional Schemas to Property Graph Models. In Advances in Conceptual Modeling, Iris Reinhartz-Berger and Shazia Sadiq (Eds.). Springer International Publishing, Cham, 3--14.Google Scholar
- Leila Amgoud and Victor David. 2018. Measuring Similarity between Logical Arguments. In Proceedings of the Sixteenth International Conference on Principles of Knowledge Representation and Reasoning KR. 98--107.Google Scholar
- Leila Amgoud and Victor David. 2021. A General Setting for Gradual Semantics Dealing with Similarity. In 35th AAAI Conference en Artificial Intelligence (AAAI 2021). AAAI : Association for the Advancement of Artificial Intelligence, AAAI Press, Virtual Conference, United States. https://hal.archives-ouvertes.fr/hal-03141729Google ScholarCross Ref
- Michele Banko, Michael J. Cafarella, Stephen Soderland, Matt Broadhead, and Oren Etzioni. 2007. Open Information Extraction from the Web. In Proceedings of the 20th International Joint Conference on Artifical Intelligence (Hyderabad, India) (IJCAI'07). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2670--2676.Google ScholarDigital Library
- L. Bertossi. 2011. Database Repairs and Consistent Query Answering. Morgan & Claypool Publishers. https://books.google.fr/books?id=leVcAQAAQBAJGoogle Scholar
- Melisachew Chekol, Giuseppe Pirrò, Joerg Schoenfisch, and Heiner Stuckenschmidt. 2017a. Marrying uncertainty and time in knowledge graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31.Google ScholarCross Ref
- Melisachew Wudage Chekol, Jakob Huber, Christian Meilicke, and Heiner Stuckenschmidt. 2016. Markov Logic Networks with Numerical Constraints. In Proceedings of the Twenty-Second European Conference on Artificial Intelligence (The Hague, The Netherlands) (ECAI'16). IOS Press, NLD, 1017--1025.Google ScholarDigital Library
- Melisachew Wudage Chekol, Giuseppe Pirro, Joerg Schoenfisch, and Heiner Stuckenschmidt. 2017b. TeCoRe: temporal conflict resolution in Knowledge Graphs. Proceedings of the VLDB Endowment, Vol. 10 (2017), Iss--12.Google Scholar
- Andrea Cohen, Sebastian Gottifredi, Alejandro J Garc'ia, and Guillermo R Simari. 2014. A survey of different approaches to support in argumentation systems. The Knowledge Engineering Review, Vol. 29, 5 (2014), 513--550.Google ScholarCross Ref
- Gwendal Daniel, Gerson Sunyé, and Jordi Cabot. 2016. UMLtoGraphDB: Mapping Conceptual Schemas to Graph Databases. In Conceptual Modeling, Isabelle Comyn-Wattiau, Katsumi Tanaka, Il-Yeol Song, Shuichiro Yamamoto, and Motoshi Saeki (Eds.). Springer International Publishing, Cham, 430--444.Google Scholar
- Victor David, Raphaël Fournier-S'niehotta, and Nicolas Travers. 2022. Parameterisation of Reasoning on Temporal Markov Logic Networks. https://doi.org/10.48550/ARXIV.2211.16414Google Scholar
- Victor David, Raphaël Fournier-S'niehotta, and Nicolas Travers. 2023. NeoMaPy: A Framework for Computing MAP Inference on Temporal Knowledge Graph. 32nd International Joint Conference on Artificial Intelligence, IJCAI.Google Scholar
- Xin Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, and Wei Zhang. 2014. Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, New York, USA) (KDD'14). ACM, New York, NY, USA, 601--610. https://doi.org/10.1145/2623330.2623623Google ScholarDigital Library
- Oren Etzioni, Michele Banko, Stephen Soderland, and Daniel S. Weld. 2008. Open Information Extraction from the Web. Commun. ACM, Vol. 51, 12 (dec 2008), 68--74. https://doi.org/10.1145/1409360.1409378Google ScholarDigital Library
- Jean H. Gallier. 1985. Logic for Computer Science: Foundations of Automatic Theorem Proving. Harper & Row Publishers, Inc., USA.Google ScholarDigital Library
- Keith W. Hipel, Liping Fang, and D. Marc Kilgour. 2020. The Graph Model for Conflict Resolution: Reflections on Three Decades of Development. Group Decision and Negotiation, Vol. 29, 1 (2020), 11--60. https://doi.org/10.1007/s10726-019-09648-zGoogle ScholarCross Ref
- Loan T.T Ho, Somjit Arch-int, and Ngamnij Arch-int. 2017. Introducing Fuzzy Temporal Description Logic. In Proceedings of the 3rd International Conference on Industrial and Business Engineering (Sapporo, Japan) (ICIBE 2017). Association for Computing Machinery, New York, NY, USA, 77--80. https://doi.org/10.1145/3133811.3133830Google ScholarDigital Library
- Mohamad Yaser Jaradeh, Kuldeep Singh, Markus Stocker, and Sören Auer. 2021. Plumber: A Modular Framework to Create Information Extraction Pipelines. In Companion Proceedings of the Web Conference 2021. ACM, New York, NY, USA, 678--679.Google ScholarDigital Library
- Erin Macdonald and Denilson Barbosa. 2020. Neural Relation Extraction on Wikipedia Tables for Augmenting Knowledge Graphs. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM'20. ACM, New York, NY, USA, 2133--2136. https://doi.org/10.1145/3340531.3412164Google ScholarDigital Library
- Feng Niu, Christopher Ré, AnHai Doan, and Jude Shavlik. 2011. Tuffy: Scaling up Statistical Inference in Markov Logic Networks Using an RDBMS. Proc. VLDB Endow., Vol. 4, 6 (mar 2011), 373--384. https://doi.org/10.14778/1978665.1978669Google ScholarDigital Library
- Jan Noessner, Mathias Niepert, and Heiner Stuckenschmidt. 2013. RockIt: Exploiting parallelism and symmetry for MAP inference in statistical relational models. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 27. 739--745.Google ScholarCross Ref
- Jay Pujara, Hui Miao, Lise Getoor, and William Cohen. 2013. Knowledge Graph Identification. In The Semantic Web -- ISWC 2013, Harith Alani, Lalana Kagal, Achille Fokoue, Paul Groth, Chris Biemann, Josiane Xavier Parreira, Lora Aroyo, Natasha Noy, Chris Welty, and Krzysztof Janowicz (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 542--557.Google ScholarDigital Library
- Sabbir M. Rashid, Amar K. Viswanathan, Ian Gross, Elisa Kendall, and Deborah L. McGuinness. 2017. Leveraging Semantics for Large-Scale Knowledge Graph Evaluation. In Proceedings of the 2017 ACM on Web Science Conference (Troy, New York, USA) (WebSci '17). ACM, New York, NY, USA, 437--442. https://doi.org/10.1145/3091478.3162385Google ScholarDigital Library
- Matthew Richardson and Pedro Domingos. 2006. Markov Logic Networks. Machine Learning, Vol. 62, 1 (01 Feb 2006), 107--136. https://doi.org/10.1007/s10994-006--5833--1Google Scholar
- Sebastian Riedel. 2008. Improving the Accuracy and Efficiency of MAP Inference for Markov Logic. In Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (Helsinki, Finland) (UAI'08). AUAI Press, Arlington, Virginia, USA, 468--475.Google ScholarDigital Library
- Romain Rincé, Romain Kervarc, and Philippe Leray. 2018. Complex Event Processing Under Uncertainty Using Markov Chains, Constraints, and Sampling. In Rules and Reasoning, Christoph Benzmüller, Francesco Ricca, Xavier Parent, and Dumitru Roman (Eds.). Springer, Cham, 147--163.Google Scholar
- Alena Rodionova, Ezio Bartocci, Dejan Nickovic, and Radu Grosu. 2016. Temporal Logic as Filtering. In Proceedings of the 19th International Conference on Hybrid Systems: Computation and Control (Vienna, Austria) (HSCC '16). Association for Computing Machinery, New York, NY, USA, 11--20. https://doi.org/10.1145/2883817.2883839Google ScholarDigital Library
- Somdeb Sarkhel, Deepak Venugopal, Parag Singla, and Vibhav Gogate. 2014. Lifted MAP inference for Markov logic networks. In Artificial Intelligence and Statistics. PMLR, 859--867.Google Scholar
- Lauro Snidaro, Ingrid Visentini, and Karna Bryan. 2015. Fusing uncertain knowledge and evidence for maritime situational awareness via Markov Logic Networks. Information Fusion, Vol. 21 (2015), 159--172.Google ScholarDigital Library
- Nasser Zalmout, Chenwei Zhang, Xian Li, Yan Liang, and Xin Luna Dong. 2021. All You Need to Know to Build a Product Knowledge Graph. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. ACM, New York, NY, USA, 4090--4091. https://doi.org/10.1145/3447548.3470825Google ScholarDigital Library
Index Terms
- NeoMaPy: A Parametric Framework for Reasoning with MAP Inference on Temporal Markov Logic Networks
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