DynaSemble: Dynamic Ensembling of Textual and Structure-Based Models for Knowledge Graph Completion (2024.acl-short)
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| Challenge: | Existing approaches to Knowledge Graph Completion use textual descriptions of the KG entities and relations to perform the task. |
| Approach: | They propose a method to combine two popular approaches to Knowledge Graph Completion . structure-based models perform better when gold answer is easily reachable . textual models exploit textual descriptions to give good performance . |
| Outcome: | The proposed method achieves 6.8 pt MRR and 8.3 pTits@1 gains over the best baseline model for WN18RR dataset. |
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| Challenge: | Knowledge Graph Completion (KGC) has been extended to multiple knowledge graph (KG) structures, initiating new research directions, e.g. static KGC, temporal KGC and few-shot KGC. |
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| Challenge: | Existing embedding-based methods rely on triples in the KG, which is vulnerable to specious relation patterns and long-tail entities. |
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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. |
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ENT-DESC: Entity Description Generation by Exploring Knowledge Graph (2020.emnlp-main)
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| Challenge: | Existing models for knowledge-to-text generation use RDF triples or key-value pairs to generate a natural language description. |
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| Challenge: | Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs . current approaches encode graph context in textual form, which fails to exploit its potential . |
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| Challenge: | Existing knowledge graph completion methods ignore inconsistent representation spaces between natural language and graph structures, leading to duplicate works and time-consuming processes. |
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| Challenge: | Existing methods to solve knowledge hypergraph link prediction problem are limited by their ability to generate chain-of-thought (CoT) representations. |
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| Challenge: | Text-based knowledge graph completion methods neglect knowledge contexts in inferring process. |
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| Challenge: | Existing knowledge graph completion methods are lacking in ranking metrics such as Hits@k . despite the high performance, the proposed method is still behind state-of-the-art models. |
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StATIK: Structure and Text for Inductive Knowledge Graph Completion (2022.findings-naacl)
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Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Murali Annavaram, Aram Galstyan, Greg Ver Steeg
| Challenge: | Knowledge graphs (KGs) represent incomplete knowledge bases. |
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