Feature Interaction for Temporal Knowledge Graph Extrapolation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 14874 LNCS, Page: 379-391
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
The emergence of knowledge graphs has sparked interest in temporal knowledge graph reasoning, which incorporates temporal data into static graphs. Recently, significant advancements in TKG extrapolation have focused on predicting future events using historical data. However, many existing methods overlook the complex dynamics of entity and relation interactions over time. To address this, we developed a novel method called FIM for temporal knowledge graph reasoning. FIM enhances interaction modeling by identifying interaction pairs through feature permutation, reinforcing interactions with chequer reshaping, using a refined-SENet with a gate mechanism for calibration, and utilizing circular convolution for boundary information. Rigorous experiments and comparative analysis demonstrate FIM’s exceptional performance in link prediction tasks.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85201095791&origin=inward; http://dx.doi.org/10.1007/978-981-97-5618-6_32; https://link.springer.com/10.1007/978-981-97-5618-6_32; https://dx.doi.org/10.1007/978-981-97-5618-6_32; https://link.springer.com/chapter/10.1007/978-981-97-5618-6_32
Springer Science and Business Media LLC
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