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: 30 June 2024 | Viewed by 5126

Special Issue Editor


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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

Published Papers (2 papers)

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Research

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 2 | Viewed by 1475
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 1 | Viewed by 2982
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Intelligent text mining for ontological knowledge graph refinement and patent portfolio analysis - Case study of net-zero data center innovation management
Authors: Amy J.C. Trappey; Ging-Bin Lin; L.P. Hung
Affiliation: 1. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan 2. Science & Technology Policy Research and Information Center, National Applied Research Laboratories, Taipei 106, Taiwan
Abstract: Ontological knowledge graph (OKG) is a well-formed visual representation that organizes and illustrates knowledge description in formal elements, such as entities, attributes, and interrelationships of elements. OKG is crucial for innovation management analysis as it provides a clear and comprehensive boundary to understand complex knowledge domain with detailed components and relationships. In the patent analysis field, it facilitates the definition of a well-defined patent portfolio, aiming for accurate and efficient patent retrievals and subsequent analyses. Recently, the rapid growth of the Information and Communication Technology (ICT) sector has rendered data centers (DCs) indispensable for data processing, storage, 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 its refinement and in-depth application in patent portfolio analysis. The net-zero DC domain covers five main sub-technologies. Utilizing the proposed OKG refinement and patent portfolio analysis framework, in the case study, 1,801 most recent decade’s patents related to relevant “DC net-zero technology” are retrieved and analyzed employing natural language processing (NLP) text mining methodologies for OKG refinement and detailed patent portfolio analysis, e.g., patent clustering, keyword identification, and patent semantic topic modeling. The domain OKG is refined, aiming to validate its effectiveness and accuracy in knowledge interpretation, organization, and applications iteratively. Furthermore, the research also applies technology function matrix (TFM) and technology maturity s-curve to assess the current and future development trends, patenting hotspots and opportunities, providing crucial references for forming R&D strategies and IP efforts.

Title: Enhancing Knowledge Graph based Recommendation with Dual-Graph Contrastive Learning
Authors: Runhe Huang
Affiliation: Hosei University, Japan
Abstract: Incorporating knowledge graphs as auxiliary information to enhance recommendation systems can improve the learning and representation of user and item feature vectors. Recommendation methods based on knowledge graphs can introduce user-item interaction learning into the item graph, focusing only on learning the node vector representations within a single graph; alternatively, they can treat user-item interactions and item graphs as two separate graphs and learn from each graph individually. Learning from two graphs has natural advantages in exploring original information and interaction information, but faces two main challenges: 1) in complex graph connection scenarios, how to adequately mine the self-information of each graph, and 2) how to merge interaction information from the two graphs while ensuring that user-item interaction information predominates. Existing methods do not thoroughly explore the simultaneous mining of self-information from both graphs and effective interaction information, leading to the loss of valuable insights. Considering the success of contrastive learning in mining self-information and auxiliary information, this paper proposes a contrastive dual-graph learning recommendation method based on knowledge graphs (KGDC) to explore the representation of user and item feature vectors in recommendation systems based on external knowledge graphs. In the learning process within the self-graph, KGDC has strengthened and represented the information of different connecting edges in both graphs, and extracted the existing information more fully. In interactive information learning, KGDC reinforces the interaction relationship between users and items in the external knowledge graph, realizing the leading role of the main task. We have conducted a series of experiments on three standard datasets, and the results show that the proposed method can achieve better results.

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