Abstract
This chapter is the continued discussion on the last experiment in Chap. 6—Under what condition, can the precision for the Task of Membership-Validation reach 100%? We will create a new type of Geometric Connectionist Machines for Triple Classification task in Knowledge Graph reasoning. Our key question is: How shall we spatialize labeled tree structures onto vector embeddings?
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Notes
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That is to say, suppose that (\(h, r, t_x\)) holds and \(t_x\) has a sibling \(t_0\) in the training dataset such that (\(h, r, t_0\)), we initialize the \(\mathscr {N}\)-Ball of \(t_x\) the way we initialize the \(\mathscr {N}\)-Ball of \(t_0\).
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Dong, T. (2021). Geometric Connectionist Machines for Triple Classification. In: A Geometric Approach to the Unification of Symbolic Structures and Neural Networks. Studies in Computational Intelligence, vol 910. Springer, Cham. https://doi.org/10.1007/978-3-030-56275-5_7
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