Multi-Hop Knowledge Graph Question Answer Method Based on Relation Knowledge Enhancement

Wang, Tianbin; HUANG, Ruiyang; Wang, Huansha; Zhi, Hongxin; Liu, Hongji

doi:10.3390/electronics12081905

Cited by 3 publications

(3 citation statements)

References 28 publications

(35 reference statements)

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“…The “top-down” approach involves defining a schema layer (ontology) based on the logical relationships and hierarchical structure of knowledge, and subsequently mapping data entities to this schema. On the other hand, the “bottom-up” approach entails extracting entities and attributes from diverse data sources into the data layer of the knowledge graph, consolidating the extracted entities and attributes, and optimizing the schema layer of the knowledge graph to facilitate iterative updates of the ontology model ( Wang et al, 2019 ). The top-down method ensures the professionalism and accuracy of the constructed domain ontology, while the bottom-up method ensures its practicality.…”

Section: Methodsmentioning

confidence: 99%

The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data

Zhao

Wang

Li³

et al. 2023

Front. Pharmacol.

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Background: The complexity and rapid progression of lesions in diabetic kidney disease pose significant challenges for clinical diagnosis and treatment. The advantages of Traditional Chinese Medicine (TCM) in diagnosing and treating this condition have gradually become evident. However, due to the disease’s complexity and the individualized approach to diagnosis and treatment in Traditional Chinese Medicine, Traditional Chinese Medicine guidelines have limitations in guiding the treatment of diabetic kidney disease. Most medical knowledge is currently stored in the process of recording medical records, which hinders the understanding of diseases and the acquisition of diagnostic and treatment knowledge among young doctors. Consequently, there is a lack of sufficient clinical knowledge to support the diagnosis and treatment of diabetic kidney disease in Traditional Chinese Medicine.Objective: To build a comprehensive knowledge graph for the diagnosis and treatment of diabetic kidney disease in Traditional Chinese Medicine, utilizing clinical guidelines, consensus, and real-world clinical data. On this basis, the knowledge of Traditional Chinese Medicine diagnosis and treatment of diabetic kidney disease was systematically combed and mined.Methods: Normative guideline data and actual medical records were used to construct a knowledge graph of Traditional Chinese Medicine diagnosis and treatment for diabetic kidney disease and the results obtained by data mining techniques enrich the relational attributes. Neo4j graph database was used for knowledge storage, visual knowledge display, and semantic query. Utilizing multi-dimensional relations with hierarchical weights as the core, a reverse retrieval verification process is conducted to address the critical problems of diagnosis and treatment put forward by experts.Results: 903 nodes and 1670 relationships were constructed under nine concepts and 20 relationships. Preliminarily a knowledge graph for Traditional Chinese Medicine diagnosis and treatment of diabetic kidney disease was constructed. Based on the multi-dimensional relationships, the diagnosis and treatment questions proposed by experts were validated through multi-hop queries of the graphs. The results were confirmed by experts and showed good outcomes.Conclusion: This study systematically combed the Traditional Chinese Medicine diagnosis and treatment knowledge of diabetic kidney disease by constructing the knowledge graph. Furthermore, it effectively solved the problem of “knowledge island”. Through visual display and semantic retrieval, the discovery and sharing of diagnosis and treatment knowledge of diabetic kidney disease were realized.

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Section: Methodsmentioning

confidence: 99%

The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data

Zhao

Wang

Li³

et al. 2023

Front. Pharmacol.

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“…It serves this purpose in the baseline method by enabling the model to give more weights to the most helpful parts of the passage for answering the question. However, single-hop attention is insufficient to perform more complex hierarchical reasoning [23,24]. Therefore, we proposed multihop attention.…”

Section: B: Multi-hop Attentionmentioning

confidence: 99%

Unlocking Everyday Wisdom: Enhancing Machine Comprehension with Script Knowledge Integration

Zhou,

Yue,

Liang

et al. 2023

Applied Sciences

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Harnessing commonsense knowledge poses a significant challenge for machine comprehension systems. This paper primarily focuses on incorporating a specific subset of commonsense knowledge, namely, script knowledge. Script knowledge is about sequences of actions that are typically performed by individuals in everyday life. Our experiments were centered around the MCScript dataset, which was the basis of the SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. As a baseline, we utilized our Three-Way Attentive Networks (TriANs) framework to model the interactions among passages, questions, and answers. Building upon the TriAN, we proposed to: (1) integrate a pre-trained language model to capture script knowledge; (2) introduce multi-layer attention to facilitate multi-hop reasoning; and (3) incorporate positional embeddings to enhance the model’s capacity for event-ordering reasoning. In this paper, we present our proposed methods and prove their efficacy in improving script knowledge integration and reasoning.

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“…Question answering based on the knowledge graph (QA-KG) [37][38][39] is a significant application area for knowledge reuse. The question answering of the block coating knowledge graph aims to answer some natural language questions by using the facts in the knowledge graph.…”

Section: Improving Keqa Based On Ahpmentioning

confidence: 99%

Knowledge Representation and Reuse of Ship Block Coating Based on Knowledge Graph

Bu,

Peng,

Guo

et al. 2023

Coatings

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Ship coating, as one of the three pillar processes in the shipbuilding industry, runs through the entire process of ship construction. However, there is currently a lack of effective organization, management methods, and mechanisms for ship coating process data, which not only leads to the dispersion of data but also limits the effective representation and reuse of the coating knowledge. To solve this problem, this paper takes the ship block coating process as the research object and proposes a method for knowledge modeling and reuse of coating knowledge using knowledge graph and question answering technology. Compared with existing strategies, this paper introduces the temporal knowledge graph, which allows for dynamic updating and generation of the knowledge graph specific to ship coating processes. In addition, we apply the knowledge embedding question answering (KEQA) method improved by the analytic hierarchy process (AHP) to facilitate high-quality retrieval and personalized question answering regarding ship block coating knowledge. We validate the proposed method using block coating process data from the 81200DWT bulk carrier and advanced ship coating methods and optimization data. The results demonstrate that the AHP-KEQA (KEQA method improved by the AHP) method improves the accuracy of knowledge question answering compared with KEQA, which further reinforces the effectiveness of the AHP-KEQA method for question answering of ship block coating knowledge.

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Multi-Hop Knowledge Graph Question Answer Method Based on Relation Knowledge Enhancement

Cited by 3 publications

References 28 publications

The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data

The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data

Unlocking Everyday Wisdom: Enhancing Machine Comprehension with Script Knowledge Integration

Knowledge Representation and Reuse of Ship Block Coating Based on Knowledge Graph

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