CompleQA: Benchmarking the Impacts of Knowledge Graph Completion Methods on Question Answering (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have focused on Knowledge Graph Completion as an end in itself, neglecting its potential impact on subsequent applications. |
| Approach: | They propose a benchmark to assess the impact of representative KGC methods on Knowledge Graph Question Answering (KGQA) they use a knowledge graph with 3 million triplets across 5 distinct domains to evaluate their results. |
| Outcome: | The proposed benchmark compares four well-known methods with two state-of-the-art systems to assess the impact of incomplete knowledge graphs on KGQA. |
Similar Papers
A Re-evaluation of Knowledge Graph Completion Methods (2020.acl-main)
Copied to clipboard
| Challenge: | Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. |
| Approach: | They propose a protocol to evaluate KGC methods that is robust to handle bias in the model, which can substantially affect the final results. |
| Outcome: | The proposed evaluation protocol is robust to handle bias in the model, which can substantially affect the final results. |
KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models (2022.emnlp-demos)
Copied to clipboard
Haris Widjaja, Kiril Gashteovski, Wiem Ben Rim, Pengfei Liu, Christopher Malon, Daniel Ruffinelli, Carolin Lawrence, Graham Neubig
| Challenge: | Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. |
| Approach: | They propose a framework for performing fine-grained evaluation on meaningful subsets of data. |
| Outcome: | The proposed framework tests models on meaningful subsets of the data, which would have been impossible to detect with standard averaged single-score metrics. |
Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis (2022.lrec-1)
Copied to clipboard
| Challenge: | Knowledge Graph (KG) Question Answering (QA) is a rapidly growing field in research and industry. |
| Approach: | They propose to create a new leaderboard for any KGQA benchmark dataset as a focal point for the community. |
| Outcome: | The proposed model provides a central and open leaderboard for any KGQA benchmark dataset as a focal point for the community. |
Complex Logical Query Answering by Calibrating Knowledge Graph Completion Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to solve complex logical queries are not well-calibrated . CKGC is lightweight and effective, allowing the model to quickly converge . |
| Approach: | They propose a method for calibrating KGC models to adapt to complex logical queries . they map the values of predictions of KGC to the range [0, 1] . |
| Outcome: | The proposed method can significantly boost model performance in complex logical query answering task. |
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)
Copied to clipboard
Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
| Challenge: | Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs). |
| Approach: | They propose a general framework to compensate for the deficiency of contextualized knowledge by querying large language models from various perspectives. |
| Outcome: | The proposed framework improves knowledge graph completion (KGC) by querying large language models from various perspectives. |
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge graphs are far from complete with large portions of triplets missing. |
| Approach: | They propose to use Graph Neural Networks to learn powerful embeddings to improve model performance. |
| Outcome: | The proposed models achieve comparable performance to MLP models, suggesting that MP may not be as crucial as previously thought. |
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing embedding-based methods rely on triples in the KG, which is vulnerable to specious relation patterns and long-tail entities. |
| Approach: | They propose a context-enriched framework for KGC that uses a large language model to generate potential answers for each query triple. |
| Outcome: | The proposed framework improves on FB15k237 and WN18RR datasets. |
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies focus on partial aspects of knowledge abstraction, concretization, and completion (KACC). |
| Approach: | They propose a unified knowledge graph benchmark to improve existing benchmarks . they collect new datasets that contain larger concept graphs and cross-view links . |
| Outcome: | The proposed benchmark improves existing benchmarks in terms of dataset scale, task coverage, and difficulty. |
Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Knowledge graph question answering (KGQA) aims to provide factual answers to natural language questions by leveraging structured information stored in a knowledge graph. |
| Approach: | They propose a Question-guided Knowledge Graph Re-scoring method to eliminate noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge. |
| Outcome: | The proposed method eliminates noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge. |
Graph Reasoning for Question Answering with Triplet Retrieval (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to answer complex questions require reasoning over knowledge graphs (KGs) state-of-the-art methods constrain retrieved knowledge in local subgraphs and discard more diverse triplets that are disconnected but useful for question answering. |
| Approach: | They propose a method to retrieve the most relevant triplets from KGs and then rerank them, which are then concatenated with questions to be fed into language models. |
| Outcome: | The proposed method outperforms state-of-the-art methods on commonsenseQA and OpenbookQA datasets with 4.6% absolute accuracy. |