concept

Freebase

Facts (19)

Sources

Large Language Models Meet Knowledge Graphs for Question ... arxiv.org arXiv Sep 22, 2025 6 facts

referenceThe EFSUM method, proposed by Ko et al. in 2024, performs KG Fact Summarization and uses KG Helpfulness and Faithfulness Filters with GPT-3.5-Turbo, Flan-T5-XL, and Llama-2-7B-Chat models and dataset-inherent knowledge graphs (Freebase, Wikidata) for KGQA and Multi-hop QA, evaluated using Accuracy (Acc) on the WQSP and Mintaka datasets.

referenceThe KG-Adapter method, proposed by Tian et al. in 2024, utilizes parameter-efficient fine-tuning and joint reasoning with Llama-2-7B-base and Zephyr-7B-alpha language models, incorporating ConceptNet and Freebase knowledge graphs to perform KGQA, MCQA, OBQA, and CWQ tasks on the OBQA, CSQA, WQSP, and CWQ datasets, evaluated using Acc and Hits@1 metrics.

referenceThe KBIGER method, proposed by Du et al. in 2022, uses iterative instruction reasoning with an LSTM-based pre-trained model and dataset-inherent knowledge graphs or Freebase to perform multi-hop KBQA tasks, evaluated using Hits@1 and F1 metrics on WQSP, CWQ, and GrailQA datasets.

referenceThe LEGO-GraphRAG method, proposed by Cao et al. in 2024, utilizes modular graph RAG with Qwen2-72B and Sentence Transformer models, incorporating the Freebase knowledge graph to perform KBQA and CWQ tasks on the WQSP, CWQ, and GrailQA datasets, evaluated using R, F1, and Hits@1 metrics.

referenceThe InteractiveKBQA method, proposed by Xiong et al. in 2024, uses Multi-turn Interaction for Observation and Thinking with GPT-4-Turbo, Mistral-7B, and Llama-2-13B models and Freebase, Wikidata, and Movie KG knowledge graphs for KBQA and domain-specific QA, evaluated using F1, Hits@1, EM, and Acc metrics on the WQSP, CWQ, KQA Pro, and MetaQA datasets.

referenceThe GAIL method, proposed by Zhang et al. in 2024, utilizes GAIL fine-tuning with Llama-2-7B and BERTa language models, incorporating the Freebase knowledge graph to perform KGQA tasks on the WQSP, CWQ, and GrailQA datasets, evaluated using EM, F1, and Hits@1 metrics.

KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org arXiv May 20, 2024 4 facts

claimFreebase, Wikidata, and YAGO are prominent examples of Knowledge Graphs used in practice.

measurementThe ComplexWebQuestions dataset contains 34,689 complex questions, each associated with an average of 367 Google web snippets and corresponding SPARQL queries for the Freebase knowledge graph.

claimFreebase, Wikidata, and YAGO are prominent examples of Knowledge Graphs used in practice.

Combining large language models with enterprise knowledge graphs frontiersin.org Frontiers Aug 26, 2024 2 facts

claimKnowledge graphs like Freebase and YAGO are primarily derived from text corpora such as Wikipedia and aim to cover extensive real-world knowledge.

measurementThe Freebase knowledge graph contains 1,345 relation types, while the YAGO knowledge graph contains 140 relation types, according to Suchanek et al. (2023).

A Knowledge Graph-Based Hallucination Benchmark for Evaluating ... arxiv.org arXiv Feb 23, 2026 2 facts

referenceThe paper 'FreebaseQA: a new factoid QA data set matching trivia-style question-answer pairs with Freebase' presents a dataset for factoid question answering.

referenceThe paper 'Semantic parsing on freebase from question-answer pairs' was published in the Conference on Empirical Methods in Natural Language Processing.

Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Springer Apr 3, 2023 2 facts

referenceFreebase is a knowledge graph built from multiple sources that provides a structured and global resource of information.

measurementIn the Freebase knowledge graph, more than half of the person entities lack information regarding their birthplaces and parents.

Empowering GraphRAG with Knowledge Filtering and Integration arxiv.org arXiv Mar 18, 2025 2 facts

measurementThe Freebase knowledge graph consists of approximately 88 million entities, 20 thousand relations, and 126 million triples.

procedureThe framework uses LLaMA2-Chat-7B as the Large Language Model backbone, which is instruction-finetuned on the training splits of WebQSP and CWQ, and Freebase, for three epochs.

A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Springer Nov 4, 2024 1 fact

referenceThe FreebaseQA benchmark evaluates question answering using the Freebase knowledge graph by testing the ability of models to answer questions through querying, providing a measure of their ability to handle large-scale structured data.