Meta Operator for Complex Query Answering on Knowledge Graphs

arXiv:2403.10110v1 Announce Type: new
Abstract: Knowledge graphs contain informative factual knowledge but are considered incomplete. To answer complex queries under incomplete knowledge, learning-based Complex Query Answering (CQA) models are proposed to directly learn from the query-answer samples to avoid the direct traversal of incomplete graph data. Existing works formulate the training of complex query answering models as multi-task learning and require a large number of training samples. In this work, we explore the compositional structure of complex queries and argue …

abstract arxiv complex queries cs.ai cs.lg cs.lo data graph graph data graphs knowledge knowledge graphs learn meta queries query samples training type