태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
In inductive link prediction inference is performed on a new, unseen graph whereas classical transductive link prediction performs both training and inference on the same graph.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
A large dataset from the Inductive Link Prediction Challenge 2022. Training graph contains 46K entities, 130 relations, 202K triples. Inference graph contains 30K entities, 130 relations, 77K triples. Validation and test triples to predict belong to the inference graph.
A small dataset from the Inductive Link Prediction Challenge 2022. Training graph contains 10K entities, 96 relations, 78K triples. Inference graph contains 7K entities, 96 relations, 21K triples. Validation and test triples to predict belong to the inference graph.
Semi-inductive link prediction (LP) in knowledge graphs (KG) is the task of predicting facts for new, previously unseen entities based on context information. Although new entities can be integrated by retraining the model from scratch in principle, such an approach is infeasible for large-scale KGs, where retraining is expensive and new entities may arise frequently. In this paper, we propose and describe a large-scale benchmark to evaluate semi-inductive LP models. The benchmark is based on and extends Wikidata5M: It provides transductive, k-shot, and 0-shot LP tasks, each varying the available information from (i) only KG structure, to (ii) including textual mentions, and (iii) detailed descriptions of the entities. We report on a small study of recent approaches and found that semi-inductive LP performance is far from transductive performance on long-tail entities throughout all experiments. The benchmark provides a test bed for further research into integrating context and textual information in semi-inductive LP models.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.