Constructing Dynamic Knowledge Graph Based on Ontology Modeling and Neo4j Graph Database

Chen, Yaxi; Xing, Xuefeng

doi:10.1109/icaibd55127.2022.9820199

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…The ontology provides the shared comprehension [16] of information structures, and defines the concept of objects in the domain and the properties that can be used to describe them. There are many methods for ontology construction, including IDEF-5 [17], the seven-step method [18], skeleton method [19], TOVE method [20], loop acquisition method [21], methodology method [22], and the nine-step method [23], etc., which have been applied to various fields of ontology construction. In order to be able to efficiently perform on-satellite sensing data analysis, an accurate conceptual and relational model is crucial in constructing a spectrum knowledge graph.…”

Section: Ontology Constructionmentioning

confidence: 99%

Research on Knowledge Graph Construction and Semantic Representation of Low Earth Orbit Satellite Spectrum Sensing Data

Ma,

Liu,

Yang

et al. 2024

Electronics

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The growth of frequency-usage devices has made the electromagnetic spectrum posture complex, resulting in an urgent demand for frequency-usage posture cognition. However, the sensing of space-based platforms is limited by the transmission capacity of the satellite-to-ground link and the satellite processing capacity, which makes on-satellite data analysis and posture generation lack the efficient means. Facing the above issues, an idea of a knowledge graph construction and semantic representation for low Earth orbit (LEO) satellite spectrum sensing data is designed in this paper. In the designed construction process, technologies such as knowledge extraction, ontology construction, knowledge fusion and knowledge visualization are utilized to efficiently analyze on-satellite sensing data. Moreover, the constructed spectrum knowledge graph can be applied in the analysis and prediction of frequency-usage behavior and intelligent spectrum management, which exhibits the effectiveness of the spectrum knowledge graph. Finally, the further development of the spectrum knowledge graph is foreseen.

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Section: Ontology Constructionmentioning

confidence: 99%

Research on Knowledge Graph Construction and Semantic Representation of Low Earth Orbit Satellite Spectrum Sensing Data

Ma,

Liu,

Yang

et al. 2024

Electronics

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“…To achieve language-agnosticism, the next step involves representing the entities and relationships in the ontology using specific mathematical representations [8]. These mathematical representations are derived from the core ontology generated in the language of the input text corpus, essentially capturing the concepts in a mathematical form [9].…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, these mathematical representations are mapped to their corresponding meanings in different languages using various translation APIs like Google Translate. As a result, the base ontology is stored in a mathematical format, containing entities and relations represented in mathematical representations [9]. This format allows for transformation into any desired language, making the ontology language agnostic.…”

Section: Introductionmentioning

confidence: 99%

Language Agnostic Ontology Extension Framework

Bharara,

Rathore,

Tiwari

2023

AJRCoS

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Ontologies are powerful structures used to define the schema and organization of knowledge. They provide a framework for precisely and explicitly organizing concepts, entities, attributes, and relationships within a domain, enabling effective data management, knowledge sharing, and intelligent decisionmaking. Knowledge graphs, on the other hand, store data in a graphical form, facilitating semantically rich and interconnected data analysis, management, and exploration. Nodes represent entities, edges depict relationships between nodes, and attributes showcase node properties. Combining the strengths of ontology, which offers schema and concept-level modeling, with the data richness of knowledge graphs, one can unlock powerful reasoning and inference capabilities that closely resemble facts and truths. The symbiotic relationship between knowledge graphs and ontology, wherein ontology serves as a blueprint for representing knowledge in the graphs, creates a robust foundation for knowledge representation. Traditionally, ontologies have been created by experts or skilled teams with domain knowledge, aiming to build comprehensive ontologies supporting holistic data representation. However, these domain-specific ontologies require manual intervention for updates and remain language-specific, making it challenging to transfer them across different language formats. To address this challenge, a crucial component is an automatic generation framework for language-independent ontologies. Such a capability would provide a data-driven approach for creating necessary ontologies. The process involves taking a text corpus as input and subsequently constructing a knowledge graph through named entity recognition. This graph is then transformed into a concept-level modeling graph, where an ID-based approach is utilized to represent the concepts. The ID-based concepts can be extended to facilitate merging and updating the ontology with new relevant information. Concurrently, this approach can also be expanded to achieve language independence, enabling the conversion of the ontology to any required language. Expressing these ontologies in mathematical terms is essential to achieve language-agnosticism, ensuring completeness, relevance, and independence from specific domains, thereby making them applicable across various linguistic contexts.

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