Is Complex Query Answering Really Complex?
원문 초록
Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current benchmarks for CQA are not really complex, and the way they are built distorts our perception of progress in this field. For example, we find that in these benchmarks, most queries (up to 98% for some query types) can be reduced to simpler problems, e.g., link prediction, where only one link needs to be predicted. The performance of state-of-the-art CQA models drops significantly when such models are evaluated on queries that cannot be reduced to easier types. Thus, we propose a set of more challenging benchmarks, composed of queries that require models to reason over multiple hops and better reflect the construction of real-world KGs. In a systematic empirical investigation, the new benchmarks show that current methods leave much to be desired from current CQA methods.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
A Survey of Link Prediction in N-ary Knowledge Graphs
GenIC: An LLM-Based Framework for Instance Completion in Knowledge Graphs
Structural Alignment in Link Prediction
Social Biases in Knowledge Representations of Wikidata separates Global North from Global South
A Systematic Evaluation of Knowledge Graph Embeddings for Gene-Disease Association Prediction
토론
이 논문과 연결된 포럼 글이 아직 없습니다.