Knowledge Representation

Chiang, Roger H. L.; Chua, Cecil Eng Huang

doi:10.4018/jdm.2012010101

Cited by 11 publications

(6 citation statements)

References 75 publications

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“…Topic event extraction can be divided into ontology-based topic event extraction (Lee et al, 2003;Chua et al, 2012;Wu et al, 2020) and event frame-based topic event extraction (Wand & Weber, 2017;Storey, 2017;Wu et al, 2020).…”

Section: Topic Event Extractionmentioning

confidence: 99%

Semi-Supervised Event Extraction Incorporated With Topic Event Frame

Miao

et al. 2023

Journal of Database Management

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Supervised Meta-event extraction suffers from two limitations: (1) The extracted meta-events only contain local semantic information and do not present the core content of the text; (2) model performance is easily degraded because of labeled samples with insufficient number and poor quality. To overcome these limitations, this study presents an approach called frame-incorporated semi-supervised topic event extraction (FISTEE), which aims to extract topic events containing global semantic information. Inspired by the frame-based knowledge representation, a topic event frame is developed to integrate multiple meta-events into a topic event. Combined with the tri-training algorithm, a strategy for selecting unlabeled samples is designed to expand the training sets, and labeling models based on conditional random field (CRF) are constructed to label meta-events. The experimental results show that the event extraction performance of FISTEE is better than supervised learning-based approaches. Furthermore, the extracted topic events can present the core content of the text.

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Section: Topic Event Extractionmentioning

confidence: 99%

Semi-Supervised Event Extraction Incorporated With Topic Event Frame

Miao

et al. 2023

Journal of Database Management

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“…The focus and themes of domain knowledge extraction vary from domain to domain (Kwan, M. & Cheung, P., 2012;Chua et al, 2012). In recent years, many domain knowledge extraction methods (Liu et al, 2021;Chen et al, 2021;Netti et al, 2021) have been proposed in various industries, most of them focus on specific domains but less on multi-dimensional ones.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Extraction From National Standards for Natural Resources

Ban

Wang

et al. 2023

Journal of Database Management

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National standards for natural resources (NSNR) plays an important role in promoting efficient use of China's natural resources, which sets standards for many domains such as marine and land resources. Its revision is difficult since standards in different domains may overlap or conflict. To facilitate the revision of NSNR, this paper extracts structural knowledge from the NSNR files to assist its revision. NSNR files are in multi-domain texts, where the traditional knowledge extraction methods could fall short in recalling multi-domain entities. To address this issue, this paper proposes a knowledge extraction method for multi-domain texts, including sub-domain relation discovery (SRD) and domain semantic features fusion (DSFF) module. SRD splits NSNR into sub-domains to facilitate the relation discovery. DSFF integrates relation features in the conditional random field (CRF) model to improve the capability of multi-domain entity recognition. Experimental results demonstrate that the proposed method could effectively extract structural knowledge from NSNR.

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“…Besides the model, advanced retrieval methods also hold the key to knowledge service during the design process. Knowledge service involves two main requirements, namely the computational operations by computers and the reasoning process of designers (Chua et al, 2012). The multifaceted characteristic of design knowledge makes it difficult to be stored and reused.…”

Section: Introductionmentioning

confidence: 99%

A C-RFBS model for the efficient construction and reuse of interpretable design knowledge records across knowledge networks

Zhang

Wang

Zhai³

et al. 2021

Systems Science & Control Engineering

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Effective and efficient provision and reuse of knowledge across a knowledge network in the global value chain still faces two challenges, namely the interpretability of model and the efficacy of construction. This research aims to address these challenges by proposing a novel representation model for design knowledge. First, a knowledge representation model based on the integration of the cognitive process theory and the Requirement-Function-Behavior-Structure model (C-RFBS model) is proposed to incorporate key elements from the cognitive process of designers to capture the rationale of deliberation and the context of decision-making, which the knowledge records created become more interpretable. Second, knowledge graph is employed to improve the productivity of knowledge records creation, storage and exploration. On this basis, we describe the creation of knowledge records using the C-RFBS model as well as the computational framework and methods for storing knowledge using knowledge graph. The proposed model and methods are implemented in a knowledge retrieval system on which we have conducted a fork design case study to evaluate and demonstrate the models and methods. As shown in the evaluation, the proposed model can effectively support knowledge elicitation and achieved improved performance in terms of knowledge retrieval through incorporating knowledge graph.

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Knowledge Representation

Cited by 11 publications

References 75 publications

Semi-Supervised Event Extraction Incorporated With Topic Event Frame

Semi-Supervised Event Extraction Incorporated With Topic Event Frame

Knowledge Extraction From National Standards for Natural Resources

A C-RFBS model for the efficient construction and reuse of interpretable design knowledge records across knowledge networks

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