| Challenge: | Knowledge graph completion benchmarks for knowledge graphs are often incomplete . however, the field has remained static over the past decade . |
| Approach: | They propose to use Wikidata and Wikipedia to improve on existing benchmarks . they analyze logical relation patterns, then perform baseline link prediction and triple classification . |
| Outcome: | The proposed datasets improve upon existing benchmarks in scope and difficulty. |
Similar Papers
Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion (2021.acl-long)
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| Challenge: | Existing benchmarks for Knowledge Graph Completion (KGC) are unsatisfactory . |
| Approach: | They propose to use rule-guided train/test generation instead of conventional random split to ensure that each testing sample is predictable with supportive data in the training set. |
| Outcome: | The proposed model improves on existing benchmarks in inferential ability, assumptions, and patterns. |
CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases (2025.naacl-long)
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Xiangyan Liu, Bo Lan, Zhiyuan Hu, Yang Liu, Zhicheng Zhang, Fei Wang, Michael Qizhe Shieh, Wenmeng Zhou
| Challenge: | Large Language Models excel in stand-alone code tasks but struggle with handling entire code repositories. |
| Approach: | They propose a system that integrates LLM agents with graph database interfaces extracted from code repositories. |
| Outcome: | The proposed system integrates LLM agents with graph database interfaces extracted from code repositories. |
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion (2022.acl-long)
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| Challenge: | Existing knowledge graph embedding techniques rely on fact-view data to predict missing links between entities, limiting their performance. |
| Approach: | They propose a commonsense-aware knowledge embedding framework which generates commonsensense from factual triples with entity concepts for a KGC task. |
| Outcome: | The proposed framework could produce high-quality negative triples and joint commonsense and fact-view link prediction. |
A Framework for Adapting Pre-Trained Language Models to Knowledge Graph Completion (2022.emnlp-main)
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| Challenge: | Recent work has demonstrated that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge graphs. |
| Approach: | They propose unsupervised and supervised methods to extract more informative representations from pre-trained language models to develop knowledge graph completion models. |
| Outcome: | The proposed model outperforms recent neural models in terms of performance and unsupervised processing methods. |
Efficient and Robust Knowledge Graph Construction (2022.aacl-tutorials)
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| Challenge: | Knowledge graph construction has appealed to the NLP community but has encountered similar issues such as efficiency and robustness. |
| Approach: | They propose to introduce efficient and robust knowledge graph construction techniques and discuss their results. |
| Outcome: | This tutorial will provide an overview of the latest and ongoing techniques for efficient and robust knowledge graph construction. |
End-to-End Construction of NLP Knowledge Graph (2021.findings-acl)
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| Challenge: | a new schema for NLP knowledge about tasks, datasets and metrics is proposed. |
| Approach: | They propose a new schema that represents knowledge about tasks, datasets and metrics in the NLP domain. |
| Outcome: | The proposed framework can be automatically built into scientific leaderboards . the proposed system achieves reasonable results for all relation types on this small-scale graph . |
A Re-evaluation of Knowledge Graph Completion Methods (2020.acl-main)
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| 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. |
Knowledge Context Modeling with Pre-trained Language Models for Contrastive Knowledge Graph Completion (2024.findings-acl)
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| Challenge: | Text-based knowledge graph completion methods neglect knowledge contexts in inferring process. |
| Approach: | They propose a framework which models the knowledge context as additional prompts with pre-trained language models for knowledge graph completion. |
| Outcome: | The proposed framework achieves state-of-the-art on FB15k-237, WN18RR and Wikidata5M datasets. |
GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasets (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have demonstrated significant capabilities in processing and understanding text data. |
| Approach: | They propose a structure-based instruction-based method to enhance LLM performance on complex graph tasks. |
| Outcome: | The proposed framework outperforms open-source models on graph problem-solving, but the gap is narrowing. |
Don’t Mess with Mister-in-Between: Improved Negative Search for Knowledge Graph Completion (2023.eacl-main)
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| Challenge: | Existing methods for knowledge graph completion use a dual-encoding framework with a bottleneck that allows for fast approximate search over a vast collection of candidates. |
| Approach: | They propose to use a dual-encoder framework to find more informative negatives by searching for candidates with high lexical overlaps. |
| Outcome: | The proposed methods improve on the large-scale Wikidata5M dataset and combine different kinds of strategies to achieve state-of-the-art performance. |