10 datasets found

Two datasets, MetaQA and FACTKG, are used to evaluate the effectiveness of the proposed method KELP. MetaQA is a critical benchmark dataset containing subsets of questions with...

ALCUNA is a benchmark for evaluating the ability of large language models (LLMs) to handle new knowledge.

FVQA is a fact-based visual question answering dataset, containing 2190 images and 5826 (question, answer) pairs, with supporting facts selected from knowledge bases.

Knowledge graphs are composed of different elements: entity nodes, relation edges, and literal nodes. Each literal node contains an entity’s attribute value (e.g. the height of...

The dataset used in the paper is Wikidata, which contains a large number of entities and their corresponding semantic types.

DBpedia is a public knowledge graph which is derived from structured information in Wikipedia, mainly infoboxes.

Knowledge graphs (KGs) are collections of facts. Some well-known knowledge graphs include Freebase (Bollacker et al., 2008), Word-Net (Miller, 1995), YAGO (Suchanek et al.,...

Knowledge graphs store a wealth of knowledge from the real world into structured graphs, which consist of collections of triplets, and each triplet (h, r, t) represents that...

Large Language Models (LLMs) have shown proficiency in question-answering tasks but often struggle to integrate real-time knowledge updates, leading to potentially outdated or...

The dataset used in this paper is a reinforcement learning dataset, specifically the SimpleQuestion dataset, which contains questions answerable using Wikidata as the knowledge...