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Information
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Knowledge Graph Technology and its Applications II
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Knowledge Graph Technology and its Applications II

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 7988

Special Issue Editor

Prof. Dr. Ryutaro Ichise

E-Mail Website
Guest Editor
Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan
Interests: knowledge graph; machine learning; semantic web
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

MDPI’s Information journal is introducing a new Special Issue. Original papers related to knowledge graph technology and its applications will be considered for publication. This Special Issue aims to bring together researchers in the knowledge graph research community to present innovative research results or novel applications. In this Special Issue, we solicit papers on various aspects of knowledge graph technology from various fields, such as the semantic web, knowledge engineering, ontology, natural language processing, machine learning, and novel applications of knowledge graph technologies to promote research activities in these fields.

Prof. Dr. Ryutaro Ichise
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • knowledge base
  • knowledge graph completion
  • knowledge graph construction
  • knowledge graph embeddings
  • knowledge graph population and information extraction
  • linked data and semantic data integration
  • machine learning on knowledge graphs
  • natural language processing for knowledge graphs
  • novel applications of knowledge graph technologies
  • ontology and reasoning on knowledge graphs
  • open and enterprise knowledge graphs
  • recommendation systems with knowledge graphs
  • representation learning for knowledge graphs
  • semantic search and question answering

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Published Papers (4 papers)

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Research

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21 pages, 3243 KiB  
Article
Intelligent Text Mining for Ontological Knowledge Graph Refinement and Patent Portfolio Analysis—Case Study of Net-Zero Data Center Innovation Management
by Amy J. C. Trappey, Ging-Bin Lin and Li-Ping Hung
Information 2024, 15(7), 374; https://doi.org/10.3390/info15070374 - 28 Jun 2024
Viewed by 944
Abstract
Ontological knowledge graph (OKG) is a well-formed visual representation that depicts knowledge organization in formal elements (e.g., entities and attributes) and their interrelationships. OKG is crucial for innovation management analysis as it provides a clear boundary to understand complex knowledge domain in detail. [...] Read more.
Ontological knowledge graph (OKG) is a well-formed visual representation that depicts knowledge organization in formal elements (e.g., entities and attributes) and their interrelationships. OKG is crucial for innovation management analysis as it provides a clear boundary to understand complex knowledge domain in detail. In the patent analysis field, it facilitates the definition of a well-defined patent portfolio, aiming for accurate and complete patent retrievals and subsequent analyses. In recent decade, the rapid growth of the information and communication technology (ICT) sector has rendered data centers (DCs) indispensable for data processing, storage, and cloud computing, while ensuring security and privacy during DC operations. However, their energy-intensive operations pose challenges to global efforts toward achieving net-zero emissions goals. In response, this research develops a formal OKG refinement process and uses DC net-zero technology OKG as case study for in-depth OKG refinement and application in patent portfolio analysis. The net-zero DC domain covers five sub-technologies. Utilizing the proposed OKG refinement and patent portfolio analysis framework, the 1801 most recent decade’s patents related to relevant “DC net-zero technologies” are retrieved and analyzed. Particularly in this case, DC colocation and server-as-a-service perspectives are the newly discovered sub-domains for OKG refinement. Furthermore, the research also adopts the technology function matrix and technology maturity to assess current and future technology development trends, providing crucial insights supporting strategic innovation management. Full article
(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)
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34 pages, 3406 KiB  
Article
Evaluating Ontology-Based PD Monitoring and Alerting in Personal Health Knowledge Graphs and Graph Neural Networks
by Nikolaos Zafeiropoulos, Pavlos Bitilis, George E. Tsekouras and Konstantinos Kotis
Information 2024, 15(2), 100; https://doi.org/10.3390/info15020100 - 8 Feb 2024
Cited by 3 | Viewed by 1809
Abstract
In the realm of Parkinson’s Disease (PD) research, the integration of wearable sensor data with personal health records (PHR) has emerged as a pivotal avenue for patient alerting and monitoring. This study delves into the complex domain of PD patient care, with a [...] Read more.
In the realm of Parkinson’s Disease (PD) research, the integration of wearable sensor data with personal health records (PHR) has emerged as a pivotal avenue for patient alerting and monitoring. This study delves into the complex domain of PD patient care, with a specific emphasis on harnessing the potential of wearable sensors to capture, represent and semantically analyze crucial movement data and knowledge. The primary objective is to enhance the assessment of PD patients by establishing a robust foundation for personalized health insights through the development of Personal Health Knowledge Graphs (PHKGs) and the employment of personal health Graph Neural Networks (PHGNNs) that utilize PHKGs. The objective is to formalize the representation of related integrated data, unified sensor and PHR data in higher levels of abstraction, i.e., in a PHKG, to facilitate interoperability and support rule-based high-level event recognition such as patient’s missing dose or falling. This paper, extending our previous related work, presents the Wear4PDmove ontology in detail and evaluates the ontology within the development of an experimental PHKG. Furthermore, this paper focuses on the integration and evaluation of PHKG within the implementation of a Graph Neural Network (GNN). This work emphasizes the importance of integrating PD-related data for monitoring and alerting patients with appropriate notifications. These notifications offer health experts precise and timely information for the continuous evaluation of personal health-related events, ultimately contributing to enhanced patient care and well-informed medical decision-making. Finally, the paper concludes by proposing a novel approach for integrating personal health KGs and GNNs for PD monitoring and alerting solutions. Full article
(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)
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20 pages, 429 KiB  
Article
Intent Classification by the Use of Automatically Generated Knowledge Graphs
by Mihael Arcan, Sampritha Manjunath, Cécile Robin, Ghanshyam Verma, Devishree Pillai, Simon Sarkar, Sourav Dutta, Haytham Assem, John P. McCrae and Paul Buitelaar
Information 2023, 14(5), 288; https://doi.org/10.3390/info14050288 - 12 May 2023
Cited by 3 | Viewed by 3309
Abstract
Intent classification is an essential task for goal-oriented dialogue systems for automatically identifying customers’ goals. Although intent classification performs well in general settings, domain-specific user goals can still present a challenge for this task. To address this challenge, we automatically generate knowledge graphs [...] Read more.
Intent classification is an essential task for goal-oriented dialogue systems for automatically identifying customers’ goals. Although intent classification performs well in general settings, domain-specific user goals can still present a challenge for this task. To address this challenge, we automatically generate knowledge graphs for targeted data sets to capture domain-specific knowledge and leverage embeddings trained on these knowledge graphs for the intent classification task. As existing knowledge graphs might not be suitable for a targeted domain of interest, our automatic generation of knowledge graphs can extract the semantic information of any domain, which can be incorporated within the classification process. We compare our results with state-of-the-art pre-trained sentence embeddings and our evaluation of three data sets shows improvement in the intent classification task in terms of precision. Full article
(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)
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Review

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19 pages, 2050 KiB  
Review
Use of Knowledge Graphs for Construction Safety Management: A Systematic Literature Review
by Fansheng Kong and Seungjun Ahn
Information 2024, 15(7), 390; https://doi.org/10.3390/info15070390 - 3 Jul 2024
Viewed by 954
Abstract
Effective safety management is crucial in the construction industry. The growing interest in employing Knowledge Graphs (KGs) for safety management in construction is driven by the need for efficient computing-aided safety practices. This paper systematically reviews the literature related to automating safety management [...] Read more.
Effective safety management is crucial in the construction industry. The growing interest in employing Knowledge Graphs (KGs) for safety management in construction is driven by the need for efficient computing-aided safety practices. This paper systematically reviews the literature related to automating safety management processes through knowledge base systems, focusing on the creation and utilization of KGs for construction safety. It captures current methodologies for developing and using KGs in construction safety management, outlining the techniques for each phase of KG development, including scope identification, integration of external data, ontological modeling, data extraction, and KG completion. This provides structured guidance on building a KG for safety management. Moreover, this paper discusses the challenges and limitations that hinder the wider adoption of KGs in construction safety management, leading to the identification of goals and considerations for future research. Full article
(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)
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