Automated knowledge base management: A survey

The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field.

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“…In Table 2, we can see a summary of the major disciplines in which the complete cycle of knowledge (a.k.a. Knowledge Management) is divided [40].…”

Section: Automated Knowledge Base Managementmentioning

confidence: 99%

Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning

et al. 2019

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“…Semantic reasoning allows new knowledge, implied relationships, etc. to be derived (Andrew, 2004;Haav, 2004;Martinez-Gil, 2015;Obitko, 2007;Abburu, 2012).…”

Section: Introductionmentioning

confidence: 99%

Applying process mining and semantic reasoning for process model customisation in healthcare

Detro

Santos

Panetto

et al. 2019

Enterprise Information Systems

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Process flexibility plays a key role in high variability environments, such as healthcare. In this type of environment, the process model needs to change some elements to adjust to specific sets of requirements. Thus, this paper proposes a process model customizing method based on ontology and process mining. The method proposed is applied in customizing process models for acute ischemic stroke treatment. During process model customization, the method provides decision-making support for users, thereby ensuring a structurally correct process customization and enabling improves patient treatment by means of recommendations.

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“…WordNet, Gene Ontology, and Medical Subject Headings. Building and maintaining these knowledge bases is expensive and manually intensive (Martinez-Gil, 2015). Semantic taxonomy enrichment aims to aid in the maintenance process by automatically placing new terms into an existing taxonomy.…”

Section: Introductionmentioning

confidence: 99%

VCU at Semeval-2016 Task 14: Evaluating definitional-based similarity measure for semantic taxonomy enrichment

McInnes

2016

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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In this paper, we describe our proposed method for measuring semantic similarity for a given pair of words at SemEval-2017 monolingual semantic word similarity task. We use a combination of knowledge-based and corpus-based techniques. We use FarsNet, the Persian Word-Net, besides deep learning techniques to extract the similarity of words. We evaluated our proposed approach on Persian (Farsi) test data at SemEval-2017. It outperformed the other participants and ranked the first in the challenge.

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Automated knowledge base management: A survey

Cited by 22 publications

References 32 publications

Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning

Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning

Applying process mining and semantic reasoning for process model customisation in healthcare

VCU at Semeval-2016 Task 14: Evaluating definitional-based similarity measure for semantic taxonomy enrichment

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