Abstract
In the last recent decade, the knowledge graph become essential in many artificial intelligent applications such as link prediction, recommendations, entity resolution…etc. Knowledge graph completion aims at predicting missing triples where the embedding methods take the lion’s share. These methods embed entities and relations into continuous vector spaces and use scoring functions to compute the plausibility of triples. The knowledge graph privacy and security take an important role to protect the data and the prediction model. We propose KGChain, a new framework which combines an off-chain storage with the blockchain in both the embedding and the completion tasks. Our work has two steps: protecting the knowledge graph and the model privacy using Fabric Hyperledger and securing the completion tasks by smart contracts. The proposed framework is evaluated with several datasets using translation-based embedding.
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Djeddai, A. (2022). KGChain: A Blockchain-Based Approach to Secure the Knowledge Graph Completion. In: Chbeir, R., Manolopoulos, Y., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2021. Lecture Notes in Computer Science(), vol 13119. Springer, Cham. https://doi.org/10.1007/978-3-031-21517-9_22
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DOI: https://doi.org/10.1007/978-3-031-21517-9_22
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