Papers with knowledge base construction

9 papers
Predicting Document Coverage for Relation Extraction (2022.tacl-1)

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Challenge: Existing methods for predicting document coverage for relation extraction (RE) are limited in their predictive power.
Approach: They propose a task of predicting the coverage of a text document for relation extraction . they analyze a dataset of 31,366 diverse documents for 520 entities .
Outcome: The proposed model achieves an F1 score of up to 46% on two use cases.
Generative Models for Product Attribute Extraction (2023.emnlp-industry)

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Challenge: generative models are used for product attribute extraction, a new field in information extraction and e-commerce.
Approach: They analyze generative models for product attribute extraction and demonstrate their utility . they perform experiments on Amazon and MAVE product attribute datasets .
Outcome: The proposed model can generate implicit attribute values, which state-of-the-art models are unable to extract.
CLEEK: A Chinese Long-text Corpus for Entity Linking (2020.lrec-1)

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Challenge: Entity linking is a fundamental task in natural language processing, says nigel kilgstrom . existing corpora for entity linking in china are lacking and deficient, he says . kilsmstrom: a new method for entity disambiguation can be developed for Chinese .
Approach: They build a Chinese corpus of multi-domain long text for entity linking . they evaluate the difficulty of documents with respect to entity linking using a measure .
Outcome: The proposed corpus is based on 100 documents from diverse domains and is publicly accessible.
Building an Ellipsis-aware Chinese Dependency Treebank for Web Text (L18-1)

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Challenge: ellipsis is a common linguistic phenomenon that some words are left out as they are understood from the context, especially in oral utterance.
Approach: They propose to use a Chinese dependency treebank to facilitate the parsing of web text . they propose to restore omissions and reserve contexts in the web text to improve dependency parsers .
Outcome: The proposed framework enables the parsing of web text from online microblogs.
Open Information Extraction on Scientific Text: An Evaluation (C18-1)

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Challenge: Open Information Extraction (OIE) is the unsupervised creation of structured information from text.
Approach: They propose to use two state-of-the-art OIE systems to evaluate the performance of OIE on scientific texts originating from 10 different disciplines.
Outcome: The proposed methods perform significantly worse on scientific text than encyclopedic text.
Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer (2020.findings-emnlp)

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Challenge: Existing methods for learning missing facts in knowledge graphs are limited by insufficiency of alignment information and inconsistency of described facts.
Approach: They propose a framework for embedding learning and ensemble knowledge transfer across KGs.
Outcome: The proposed framework improves state-of-the-art methods on language-specific KGs.
How to Talk to Language Models: Serialization Strategies for Structured Entity Matching (2025.findings-naacl)

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Challenge: Entity matching (EM) identifies whether two data records refer to the same entity . however, its performance heavily depends on how structured entities are “talked” through serialized text.
Approach: They propose a novel serialization scheme for entities with complex relations in knowledge graphs based on random walks and use open-source LLMs to encode sampled semantic walks for matching.
Outcome: The proposed scheme achieves leading performance on EM in canonical and heterogeneous KGs.
Regularized Attentive Capsule Network for Overlapped Relation Extraction (2020.coling-main)

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Challenge: Existing methods to extract relations from distant supervision contain low-quality instances with noisy words and overlapped relations.
Approach: They propose a Regularized Attentive Capsule Network to better identify overlapped relations in informal sentences . they embed multi-head attention into the capsule network as the low-level capsules .
Outcome: Extensive experiments show that the proposed model improves relation extraction.
Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation (2020.coling-main)

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Challenge: Existing studies on relation extraction (RE) use labeled training data for relation extraction models but it is expensive and time-consuming.
Approach: They propose a dual supervision framework which utilizes both types of data to train relation extraction models.
Outcome: The proposed framework can predict labels by human annotation and distant supervision without labeling bias since it is expensive and time-consuming.

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