| Challenge: | Existing knowledge bases are incomplete, resulting in poor answers and incompleteness. |
| Approach: | They propose a method to extract Wikipedia infobox tables to populate an existing KB. |
| Outcome: | The proposed method improves accuracy and completeness of the final KB significantly compared to DBpedia's baseline method. |
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| Challenge: | Using unsupervised entity linking, we solve named entity recognition, coreference resolution and relation extraction tasks together. |
| Approach: | They propose to use a knowledge base to inject information into a joint IE model by using unsupervised entity linking. |
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Pattern-revising Enhanced Simple Question Answering over Knowledge Bases (C18-1)
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| Challenge: | Simple question answering over knowledge bases is one of the most important natural language processing tasks. |
| Approach: | They propose to conduct pattern extraction and entity linking first and put forward pattern revising procedure to mitigate the error propagation problem. |
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Improving Entity Disambiguation by Reasoning over a Knowledge Base (2022.naacl-main)
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FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge (2023.emnlp-main)
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Knowledge Extraction From Texts Based on Wikidata (2022.naacl-industry)
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| Challenge: | Existing knowledge extraction pipelines for English are not suitable for enterprise use. |
| Approach: | They propose a knowledge extraction pipeline for English which can be further used for building an entreprise-specific knowledge base. |
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Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction (N19-1)
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| Challenge: | Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent. |
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Neural Relation Extraction for Knowledge Base Enrichment (P19-1)
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| Challenge: | Existing studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into knowledge base (KB) enrichment. |
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OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference (N19-1)
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| Challenge: | Existing methods for knowledge extraction and alignment are limited in quality and performance. |
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WebIE: Faithful and Robust Information Extraction on the Web (2023.acl-long)
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| Challenge: | Existing closed IE datasets are built using Wikipedia, but they have limitations when applied to web domains. |
| Approach: | They propose to annotate 25K triples from WebIE through crowdsourcing and introduce mWebIE, a translation of the annotated set in four other languages. |
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Rethinking Document-Level Relation Extraction: A Reality Check (2023.findings-acl)
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| Challenge: | Recent efforts push up performance boundaries of document-level relation extraction (DocRE) but these efforts are not promising. |
| Approach: | They construct four types of entity mention attacks to examine model robustness . they also have a close check on model usability in a more realistic setting . |
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