Wei Liu, Meijuan Yin^*, Jialong Zhang, Lunchong Cui

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 975-997, 2024, DOI:10.32604/cmc.2023.046475

Abstract The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities, and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation. However, this method has some problems, such as relying on expert experience and poor portability. Inspired by the rule-based entity relation extraction method, this paper proposes a joint entity relation extraction model based on a relation semantic template automatically constructed, which is abbreviated as… More >

Zhigang Zhao¹, Xiong Luo^1,2,3,*, Maojian Chen^1,2,3, Ling Ma¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 225-257, 2024, DOI:10.32604/cmes.2023.031513

Abstract Knowledge graph (KG) serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework. This framework facilitates a transformation in information retrieval, transitioning it from mere string matching to far more sophisticated entity matching. In this transformative process, the advancement of artificial intelligence and intelligent information services is invigorated. Meanwhile, the role of machine learning method in the construction of KG is important, and these techniques have already achieved initial success. This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning. With a profound amalgamation… More >

Yuexin Huang^1,2, Suihuai Yu¹, Jianjie Chu^1,*, Zhaojing Su^1,3, Yangfan Cong¹, Hanyu Wang¹, Hao Fan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 167-200, 2024, DOI:10.32604/cmes.2023.028268

Abstract The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design. This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph. Specifically, the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data, and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design. Moreover, the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module, and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity… More >

Bin He, Hao Meng, Zhejin Zhang, Rui Liu, Ting Zhang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 403-419, 2023, DOI:10.32604/cmes.2023.023242

Abstract A qualia role-based entity-dependency graph (EDG) is proposed to represent and extract quantity relations for solving algebra story problems stated in Chinese. Traditional neural solvers use end-to-end models to translate problem texts into math expressions, which lack quantity relation acquisition in sophisticated scenarios. To address the problem, the proposed method leverages EDG to represent quantity relations hidden in qualia roles of math objects. Algorithms were designed for EDG generation and quantity relation extraction for solving algebra story problems. Experimental result shows that the proposed method achieved an average accuracy of 82.2% on quantity relation extraction compared to 74.5% of baseline… More >

Bin Ji¹, Hao Xu¹, Jie Yu¹, Shasha Li¹, Jun Ma¹, Yuke Ji^2,*, Huijun Liu¹

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1303-1318, 2023, DOI:10.32604/cmc.2023.032168

Abstract An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task. However, these models sample a large number of negative entities and negative relations during the model training, which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance. In order to address the above issues, we propose a two-phase paradigm for the span-based joint entity and relation extraction, which involves classifying the entities and relations in the first phase, and predicting the types of these entities and relations in the second phase. The two-phase paradigm enables… More >

Huiyu Sun^*, Ralph Grishman

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1415-1423, 2022, DOI:10.32604/iasc.2022.030794

Abstract Active learning methods which present selected examples from the corpus for annotation provide more efficient learning of supervised relation extraction models, but they leave the developer in the unenviable role of a passive informant. To restore the developer’s proper role as a partner with the system, we must give the developer an ability to inspect the extraction model during development. We propose to make this possible through a representation based on lexicalized dependency paths (LDPs) coupled with an active learner for LDPs. We apply LDPs to both simulated and real active learning with ACE as evaluation and a year’s newswire… More >

Jianquan Ouyang^1,*, Jing Zhang¹, Tianming Liu²

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1707-1723, 2022, DOI:10.32604/iasc.2022.028352

Abstract Joint entity and relation extraction (JERE) is an important foundation for unstructured knowledge extraction in natural language processing (NLP). Thus, designing efficient algorithms for it has become a vital task. Although existing methods can efficiently extract entities and relations, their performance should be improved. In this paper, we propose a novel model called Attention and Span-based Entity and Relation Transformer (ASpERT) for JERE. First, differing from the traditional approach that only considers the last hidden layer as the feature embedding, ASpERT concatenates the attention head information of each layer with the information of the last hidden layer by using an… More >

Huiyu Sun^*, Ralph Grishman

Computer Systems Science and Engineering, Vol.43, No.3, pp. 861-870, 2022, DOI:10.32604/csse.2022.030759

Abstract Log-linear models and more recently neural network models used for supervised relation extraction requires substantial amounts of training data and time, limiting the portability to new relations and domains. To this end, we propose a training representation based on the dependency paths between entities in a dependency tree which we call lexicalized dependency paths (LDPs). We show that this representation is fast, efficient and transparent. We further propose representations utilizing entity types and its subtypes to refine our model and alleviate the data sparsity problem. We apply lexicalized dependency paths to supervised learning using the ACE corpus and show that… More >

Kun Ding¹, Shanshan Liu¹, Yuhao Zhang², Hui Zhang¹, Xiaoxiong Zhang^1,*, Tongtong Wu^2,3, Xiaolei Zhou¹

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 377-389, 2021, DOI:10.32604/cmc.2021.016301

Abstract The joint extraction of entities and their relations from certain texts plays a significant role in most natural language processes. For entity and relation extraction in a specific domain, we propose a hybrid neural framework consisting of two parts: a span-based model and a graph-based model. The span-based model can tackle overlapping problems compared with BILOU methods, whereas the graph-based model treats relation prediction as graph classification. Our main contribution is to incorporate external lexical and syntactic knowledge of a specific domain, such as domain dictionaries and dependency structures from texts, into end-to-end neural models. We conducted extensive experiments on… More >

Qi Yue, Xiang Li, Dan Li^*

Computer Systems Science and Engineering, Vol.37, No.3, pp. 423-442, 2021, DOI:10.32604/csse.2021.014448

Abstract Forestry work has long been weak in data integration; its initial state will inevitably affect the forestry project development and decision-quality. Knowledge Graph (KG) can provide better abilities to organize, manage, and understand forestry knowledge. Relation Extraction (RE) is a crucial task of KG construction and information retrieval. Previous researches on relation extraction have proved the performance of using the attention mechanism. However, these methods focused on the representation of the entire sentence and ignored the loss of information. The lack of analysis of words and syntactic features contributes to sentences, especially in Chinese relation extraction, resulting in poor performance.… More >