Knowledge Graph Modeling for Semantic Integration of Energy Services

Chun, Sang Sik; Jin, Xiongnan; Seo, Seungmin; Lee, Kyong-Ho; Shin, Youngmee; Lee, Il-Woo

doi:10.1109/bigcomp.2018.00138

Cited by 17 publications

(7 citation statements)

References 5 publications

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“…The popularity of KGs has grown in recent years, as shown by the increasing number of research groups and companies that work on the topic [2,6]. KGs are usually used in works that focus on the representation and use of knowledge in the Semantic Web and Natural Language Processing [5,6,16]. However, there are still a number of research challenges related to KGs, as shown in Figure 2.…”

Section: Knowledge Graphsmentioning

confidence: 99%

“…The role of KGs regarding the representation, querying and analysis of heterogeneous data, especially in dynamic contexts [5,16], needs to be more explored.…”

Section: Knowledge Graphsmentioning

confidence: 99%

“…This has been investigated in several works. Particularly, in [5], the authors observed a big potential in the connection of an enormous amount of data that are generated by the billions of devices of the Internet of Things (IoT), and in [16], the authors similarly identified the need of unify data coming from energy systems. In the case of studies on Thermal Comfort, in general they follow a traditional approach, do not exploit the use of tools for handling heterogeneous data, and they are limited to following standards such as ASHRAE 55 [17].…”

Section: Related Workmentioning

confidence: 99%

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Knowledge Graphs for Analyzing Thermal Comfort: A Proposal

Jácome-Domínguez¹,

Benítez–Guerrero²,

Molero–Castillo³

2019

RCS

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Knowledge Graphs (KGs) can be used to provide a unified, homogeneous view of heterogeneous data, which then can be queried and analyzed. In this paper, we explore the use of KGs to analyze the Thermal Comfort (TC) of users in specific environments (e.g. classrooms, hotel rooms). This implies the integration of several data sources that provide environmental variables (i.e. temperature, humidity), but also users' physiological variables (i.e. temperature). Thus, this paper proposes a method to integrate and analyse environmental and physiological data through KGs in the context of TC.

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Section: Knowledge Graphsmentioning

confidence: 99%

“…The role of KGs regarding the representation, querying and analysis of heterogeneous data, especially in dynamic contexts [5,16], needs to be more explored.…”

Section: Knowledge Graphsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge Graphs for Analyzing Thermal Comfort: A Proposal

Jácome-Domínguez¹,

Benítez–Guerrero²,

Molero–Castillo³

2019

RCS

View full text Add to dashboard Cite

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“…Chun et al. [27] proposed an energy KG to provide the semantic integration of various energy services. Since the conventional knowledge base for power information acquisition system were unable to obtain efficient decision‐making, Feng et al.…”

Section: Introductionmentioning

confidence: 99%

A survey on the development status and application prospects of knowledge graph in smart grids

Jian

Wang

et al. 2020

IET Generation Trans & Dist

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With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.

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“…The knowledge graph is an emerging knowledge service technology in the era of big data and an important part of artificial intelligence [ 8 ]. It has a graph-based data structure composed of nodes (entities) and edges (semantic relations) [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study

Xiu

Qian

2020

JMIR Med Inform

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Background With the increasing incidences and mortality of digestive system tumor diseases in China, ways to use clinical experience data in Chinese electronic medical records (CEMRs) to determine potentially effective relationships between diagnosis and treatment have become a priority. As an important part of artificial intelligence, a knowledge graph is a powerful tool for information processing and knowledge organization that provides an ideal means to solve this problem. Objective This study aimed to construct a semantic-driven digestive system tumor knowledge graph (DSTKG) to represent the knowledge in CEMRs with fine granularity and semantics. Methods This paper focuses on the knowledge graph schema and semantic relationships that were the main challenges for constructing a Chinese tumor knowledge graph. The DSTKG was developed through a multistep procedure. As an initial step, a complete DSTKG construction framework based on CEMRs was proposed. Then, this research built a knowledge graph schema containing 7 classes and 16 kinds of semantic relationships and accomplished the DSTKG by knowledge extraction, named entity linking, and drawing the knowledge graph. Finally, the quality of the DSTKG was evaluated from 3 aspects: data layer, schema layer, and application layer. Results Experts agreed that the DSTKG was good overall (mean score 4.20). Especially for the aspects of “rationality of schema structure,” “scalability,” and “readability of results,” the DSTKG performed well, with scores of 4.72, 4.67, and 4.69, respectively, which were much higher than the average. However, the small amount of data in the DSTKG negatively affected its “practicability” score. Compared with other Chinese tumor knowledge graphs, the DSTKG can represent more granular entities, properties, and semantic relationships. In addition, the DSTKG was flexible, allowing personalized customization to meet the designer's focus on specific interests in the digestive system tumor. Conclusions We constructed a granular semantic DSTKG. It could provide guidance for the construction of a tumor knowledge graph and provide a preliminary step for the intelligent application of knowledge graphs based on CEMRs. Additional data sources and stronger research on assertion classification are needed to gain insight into the DSTKG’s potential.

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Knowledge Graph Modeling for Semantic Integration of Energy Services

Cited by 17 publications

References 5 publications

Knowledge Graphs for Analyzing Thermal Comfort: A Proposal

Knowledge Graphs for Analyzing Thermal Comfort: A Proposal

A survey on the development status and application prospects of knowledge graph in smart grids

Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study

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