Knowledge Base Construction Method Research Articles

Research on the Construction Method of Knowledge Base of Automatic Question Answering System for Labor Law

At present, with the rapid development of China's economy, labor relations in society are becoming more and more tense. Therefore, it is urgent and necessary to develop an automatic question and answer system for labor law to solve problems in legislation, law enforcement, justice, rights protection, etc., and the construction of labor law knowledge base is the core system of the automatic question and answer system of labor law. With the development and training of large AI models, the automatic question answering system is becoming more and more mature, and this paper will use FastGPT to build a local knowledge base and build a knowledge base of the automatic question answering system for labor law.

A novel fracture-cavity reservoir outcrop geological knowledge base construction method considering parameter collection and processing, mutual transformation of data-knowledge, application and update

This study endeavors to formulate a comprehensive methodology for establishing a Geological Knowledge Base (GKB) tailored to fracture-cavity reservoir outcrops within the North Tarim Basin. The acquisition of quantitative geological parameters was accomplished through diverse means such as outcrop observations, thin section studies, unmanned aerial vehicle scanning, and high-resolution cameras. Subsequently, a three-dimensional digital outcrop model was generated, and the parameters were standardized. An assessment of traditional geological knowledge was conducted to delineate the knowledge framework, content, and system of the GKB. The basic parameter knowledge was extracted using multiscale fine characterization techniques, including core statistics, field observations, and microscopic thin section analysis. Key mechanism knowledge was identified by integrating trace elements from filling, isotope geochemical tests, and water-rock simulation experiments. Significant representational knowledge was then extracted by employing various methods such as multiple linear regression, neural network technology, and discriminant classification. Subsequently, an analogy study was performed on the karst fracture-cavity system (KFCS) in both outcrop and underground reservoir settings. The results underscored several key findings: (1) Utilization of a diverse range of techniques, including outcrop observations, core statistics, unmanned aerial vehicle scanning, high-resolution cameras, thin section analysis, and electron scanning imaging, enabled the acquisition and standardization of data. This facilitated effective management and integration of geological parameter data from multiple sources and scales. (2) The GKB for fracture-cavity reservoir outcrops, encompassing basic parameter knowledge, key mechanism knowledge, and significant representational knowledge, provides robust data support and systematic geological insights for the intricate and in-depth examination of the genetic mechanisms of fracture-cavity reservoirs. (3) The developmental characteristics of fracture-cavities in karst outcrops offer effective, efficient, and accurate guidance for fracture-cavity research in underground karst reservoirs. The outlined construction method of the outcrop geological knowledge base is applicable to various fracture-cavity reservoirs in different layers and regions worldwide.

A semantic data-driven knowledge base construction method to assist designers in design inspiration based on traditional motifs

Directing at the problem of inaccurate expression of tacit knowledge and low heuristic of retrieval results in the design inspiration drawing process of designers based on traditional motifs, a semantic data representation method of traditional motifs knowledge is put forward, and an effective knowledge base construction and knowledge retrieval method for traditional motifs are described. First, the connotation of traditional motifs is deeply explored based on the theory of symbol semantics, realizing the interpretation and transfer of the semantic features in traditional motifs. Second, shape grammar is adopted for fractal traditional motifs, the hierarchical structure of traditional motifs is built, and the parametric coding of semantic features of traditional motifs is completed, providing the semantic data representation method of traditional motifs knowledge. Third, based on the clustering algorithm, the construction method of the traditional motifs knowledge base is explained, and a traditional motifs knowledge retrieval method taking into account the user's perceptual preference is proposed. Finally, setting the traditional Chinese motifs as an example, with a tea set as the designed product, the construction process of a small traditional motif knowledge base is shown in detail, proving the feasibility of this paper’s theory. Compared with the traditional theory, the semantic data representation method of traditional pattern knowledge is greatly advantageous in semantic differences elimination and computational efficiency.

A Hazardous Chemical Knowledge Base Construction Method Based on Knowledge Graph

A Hazardous Chemical Knowledge Base Construction Method Based on Knowledge Graph

Semantic Association and Decision-Making for the Internet of Things Based on Partial Differential Fuzzy Unsupervised Models

This study presents an in-depth study and analysis of IoT semantic association and decision-making using a partial differential fuzzy unsupervised approach. It focuses on a semantic annotation framework for device metadata and a knowledge base construction method to further improve the interoperability of IoT domain knowledge by building a unified IoT domain knowledge base and designing and implementing a semantic IoT knowledge management and application generation system. The main proposal is an IoT generic domain ontology, which reuses the existing excellent ontologies of IoT as much as possible, extracts the commonly used concepts of the domain and combines them, and provides a unified semantic template for IoT applications. On the other hand, by applying the entity linking technique to the extension of the knowledge base and linking the structured metadata of devices to the corresponding entities of the background knowledge base, the domain knowledge base can be made to share the rich background knowledge. At the same time, the interoperability of heterogeneous IoT metadata between applications is enhanced by unifying data and concepts from different device applications to the same background knowledge base through entity alignment techniques. The semantic representation of events applicable to IoT application scenarios is investigated, and an IoT event ontology for representing abstract events and event relationships in IoT is designed; next, a domain ontology with IoT sensing and control event representation capability is constructed based on the IoT event ontology, in which the typical domain ontology (SSN) that can be used for IoT applications is followed by the ontology reuse principle is improved and extended to support the description of event types and interevent relationships, and the IoT event model is associated with the improved IoT base ontology through an ontology alignment approach. Finally, the IoT sensing and control ontology are validated by semantic modeling of device composition, component relationships, and operational processes based on the IoT sensing and control ontology.

A hazardous chemical knowledge base construction method based on knowledge graph

A hazardous chemical knowledge base construction method based on knowledge graph