Explainable Link Prediction for Emerging Entities in Knowledge Graphs
This addresses the challenge of handling evolving knowledge graphs with emerging entities, which is crucial for applications like recommendation systems and semantic search, though it is incremental as it builds on prior path-based and embedding-based approaches.
The paper tackles the problem of link prediction for newly emerging entities in evolving knowledge graphs, where existing methods fail due to reliance on static snapshots, and proposes an inductive representation learning framework that achieves interpretable predictions by finding reasoning paths.
Despite their large-scale coverage, cross-domain knowledge graphs invariably suffer from inherent incompleteness and sparsity. Link prediction can alleviate this by inferring a target entity, given a source entity and a query relation. Recent embedding-based approaches operate in an uninterpretable latent semantic vector space of entities and relations, while path-based approaches operate in the symbolic space, making the inference process explainable. However, these approaches typically consider static snapshots of the knowledge graphs, severely restricting their applicability for evolving knowledge graphs with newly emerging entities. To overcome this issue, we propose an inductive representation learning framework that is able to learn representations of previously unseen entities. Our method finds reasoning paths between source and target entities, thereby making the link prediction for unseen entities interpretable and providing support evidence for the inferred link.