Improving question answering over incomplete knowledge graphs with relation prediction

Zhao, Fen; Li, Yinguo; Hou, Jie; Bai, Ling

doi:10.1007/s00521-021-06736-7

Cited by 15 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 1 illustrates the disparities in attribute values for a same product in two diferent online shopping KBs. When integrating KBs to build recommendation systems or question-answering systems [3][4][5], these disparities can lead to increased redundancy and reduced performance in downstream tasks. Entity Matching (EM), as a fundamental knowledge extraction task in Natural Language Processing (NLP), aims to determine whether two entity records from diferent KBs refer to the same real-world entity, thereby addressing the aforementioned challenge [6].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Pretrained Language Models for Enhanced Entity Matching: A Comprehensive Study of Fine-Tuning and Prompt Learning Paradigms

Wang,

Zhou,

Wang

et al. 2024

International Journal of Intelligent Systems

View full text Add to dashboard Cite

Pretrained Language Models (PLMs) acquire rich prior semantic knowledge during the pretraining phase and utilize it to enhance downstream Natural Language Processing (NLP) tasks. Entity Matching (EM), a fundamental NLP task, aims to determine whether two entity records from different knowledge bases refer to the same real-world entity. This study, for the first time, explores the potential of using a PLM to boost the EM task through two transfer learning techniques, namely, fine-tuning and prompt learning. Our work also represents the first application of the soft prompt in an EM task. Experimental results across eleven EM datasets show that the soft prompt consistently outperforms other methods in terms of F1 scores across all datasets. Additionally, this study also investigates the capability of prompt learning in few-shot learning and observes that the hard prompt achieves the highest F1 scores in both zero-shot and one-shot context. These findings underscore the effectiveness of prompt learning paradigms in tackling challenging EM tasks.

show abstract

Section: Introductionmentioning

confidence: 99%

Leveraging Pretrained Language Models for Enhanced Entity Matching: A Comprehensive Study of Fine-Tuning and Prompt Learning Paradigms

Wang,

Zhou,

Wang

et al. 2024

International Journal of Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…STS measures the degree of semantic overlap between two texts 2 . The ability to determine the semantic relationship between two texts is an integral part of machines that understand and infer natural language 3 hence STS is a directly or indirectly significant component of many applications such as information retrieval 4 , recognition of paraphrases 5 , textual entailment 6 , question answering 7 , text summarization 8 , measuring the degree of equivalence between a machine translation output and a reference translation 9 and also text summarization evaluation, text classification, document clustering, topic tracking, essay scoring, short answer scoring, etc. STS is also closely related to paraphrase identification and textual entailment recognition.…”

Section: Introductionmentioning

confidence: 99%

Computing semantic similarity of texts based on deep graph learning with ability to use semantic role label information

Mohebbi

Razavi

Balafar

2022

Sci Rep

View full text Add to dashboard Cite

We propose a deep graph learning approach for computing semantic textual similarity (STS) by using semantic role labels generated by a Semantic Role Labeling (SRL) system. SRL system output has significant challenges in dealing with graph-neural networks because it doesn't have a graph structure. To address these challenges, we propose a novel SRL graph by using semantic role labels and dependency grammar. For processing the SRL graph, we proposed a Deep Graph Neural Network (DGNN) based graph-U-Net model that is placed on top of the transformers to use a variety of transformers to process representations obtained from them. We investigate the effect of using the proposed DGNN and SRL graph on the performance of some transformers in computing STS. For the evaluation of our approach, we use STS2017 and SICK datasets. Experimental evaluations show that using the SRL graph accompanied by applying the proposed DGNN increases the performance of the transformers used in the DGNN.

show abstract

“…It can help people to understand the underlying evolution principles of human behavior and social development from a variety of text corpus. Therefore it has played an important role in a wide range of NLP tasks, such as story generation, 15 question answering, 16 and intention recognition 17 . Generally, different from the coarse‐grained topic event, 18–20 event in the script is a fine‐grained event form, composed of a trigger verb phrase and some other necessary components 21 .…”

Section: Introductionmentioning

confidence: 99%

What happens next? Combining enhanced multilevel script learning and dual fusion strategies for script event prediction

Zhou

Wang

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

Improving question answering over incomplete knowledge graphs with relation prediction

Cited by 15 publications

References 33 publications

Leveraging Pretrained Language Models for Enhanced Entity Matching: A Comprehensive Study of Fine-Tuning and Prompt Learning Paradigms

Leveraging Pretrained Language Models for Enhanced Entity Matching: A Comprehensive Study of Fine-Tuning and Prompt Learning Paradigms

Computing semantic similarity of texts based on deep graph learning with ability to use semantic role label information

What happens next? Combining enhanced multilevel script learning and dual fusion strategies for script event prediction

Contact Info

Product

Resources

About