Abstract
Knowledge graphs represent an unparalleled opportunity for machine learning, given their ability to provide meaningful context to the data through semantic representations. However, general-purpose knowledge graphs may describe entities from multiple perspectives, with some being irrelevant to the learning task. Despite the recent advances in semantic representations such as knowledge graph embeddings, existing methods are unsuited to tailoring semantic representations to a specific learning target that is not encoded in the knowledge graph.
We present evoKGsim+, a framework that can evolve similarity-based semantic representations for learning relations between knowledge graph entity pairs, which are not encoded in the graph. It employs genetic programming, where the evolutionary process is guided by a fitness function that measures the quality of relation prediction. The framework combines several taxonomic and embedding similarity measures and provides several baseline evaluation approaches that emulate domain expert feature selection and optimal parameter setting.
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Acknowledgements
CP, SS, RTS are funded by the FCT through LASIGE Research Unit, ref. UIDB/00408/2020 and ref. UIDP/00408/2020. CP and RTS are funded by project SMILAX (ref. PTDC/EEI-ESS/4633/2014), SS by projects BINDER (ref. PTDC/CCI-INF/29168/2017) and PREDICT (ref. PTDC/CCI-CIF/29877/2017), and RTS by FCT PhD grant (ref. SFRH/BD/145377/2019). It was also partially supported by the KATY project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017453.
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Sousa, R.T., Silva, S., Pesquita, C. (2021). evoKGsim+: A Framework for Tailoring Knowledge Graph-Based Similarity for Supervised Learning. In: Verborgh, R., et al. The Semantic Web: ESWC 2021 Satellite Events. ESWC 2021. Lecture Notes in Computer Science(), vol 12739. Springer, Cham. https://doi.org/10.1007/978-3-030-80418-3_26
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DOI: https://doi.org/10.1007/978-3-030-80418-3_26
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