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26 pages, 1255 KiB

Open AccessArticle

Building Bio-Ontology Graphs from Data Using Logic and NLP

by Theresa Gasser and Erick Chastain

Information 2024, 15(11), 669; https://doi.org/10.3390/info15110669 - 25 Oct 2024

Viewed by 765

Abstract

In this age of big data and natural language processing, to what extent can we leverage new technologies and new tools to make progress in organizing disparate biomedical data sources? Imagine a system in which one could bring together sequencing data with phenotypes, [...] Read more.

In this age of big data and natural language processing, to what extent can we leverage new technologies and new tools to make progress in organizing disparate biomedical data sources? Imagine a system in which one could bring together sequencing data with phenotypes, gene expression data, and clinical information all under the same conceptual heading where applicable. Bio-ontologies seek to carry this out by organizing the relations between concepts and attaching the data to their corresponding concept. However, to accomplish this, we need considerable time and human input. Instead of resorting to human input alone, we describe a novel approach to obtaining the foundation for bio-ontologies: obtaining propositions (links between concepts) from biomedical text so as to fill the ontology. The heart of our approach is applying logic rules from Aristotelian logic and natural logic to biomedical information to derive propositions so that we can have material to organize knowledge bases (ontologies) for biomedical research. We demonstrate this approach by constructing a proof-of-principle bio-ontology for COVID-19 and related diseases. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)

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24 pages, 4447 KiB

Open AccessArticle

LPG Semantic Ontologies: A Tool for Interoperable Schema Creation and Management

by Eleonora Bernasconi, Miguel Ceriani and Stefano Ferilli

Information 2024, 15(9), 565; https://doi.org/10.3390/info15090565 - 13 Sep 2024

Cited by 1 | Viewed by 906

Abstract

Ontologies are essential for the management and integration of heterogeneous datasets. This paper presents OntoBuilder, an advanced tool that leverages the structural capabilities of semantic labeled property graphs (SLPGs) in strict alignment with semantic web standards to create a sophisticated framework for data [...] Read more.

Ontologies are essential for the management and integration of heterogeneous datasets. This paper presents OntoBuilder, an advanced tool that leverages the structural capabilities of semantic labeled property graphs (SLPGs) in strict alignment with semantic web standards to create a sophisticated framework for data management. We detail OntoBuilder’s architecture, core functionalities, and application scenarios, demonstrating its proficiency and adaptability in addressing complex ontological challenges. Our empirical assessment highlights OntoBuilder’s strengths in enabling seamless visualization, automated ontology generation, and robust semantic integration, thereby significantly enhancing user workflows and data management capabilities. The performance of the linked data tools across multiple metrics further underscores the effectiveness of OntoBuilder. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)

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18 pages, 511 KiB

Open AccessArticle

Enhancing Knowledge-Aware Recommendation with Dual-Graph Contrastive Learning

by Jinchao Huang, Zhipu Xie, Han Zhang, Bin Yang, Chong Di and Runhe Huang

Information 2024, 15(9), 534; https://doi.org/10.3390/info15090534 - 2 Sep 2024

Viewed by 1074

Abstract

Incorporating knowledge graphs as auxiliary information to enhance recommendation systems can improve the representations learning of users and items. 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 [...] Read more.

Incorporating knowledge graphs as auxiliary information to enhance recommendation systems can improve the representations learning of users and items. 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 dual-graph contrastive learning recommendation method based on knowledge graphs (KGDC) to explore a more accurate representations of users and items in recommendation systems based on external knowledge graphs. In the learning process within the self-graph, KGDC strengthens and represents the information of different connecting edges in both graphs, and extracts 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 conducted a series of experiments on three standard datasets, and the results show that the proposed method can achieve better results. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)

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21 pages, 3243 KiB

Open AccessArticle

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

Cited by 3 | Viewed by 1931

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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33 pages, 3406 KiB

Open AccessArticle

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 2554

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

Open AccessArticle

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 4 | Viewed by 4052

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

Jump to: Research

26 pages, 2812 KiB

Open AccessReview

Integrating Knowledge Graphs into Autonomous Vehicle Technologies: A Survey of Current State and Future Directions

by Swe Nwe Nwe Htun and Ken Fukuda

Information 2024, 15(10), 645; https://doi.org/10.3390/info15100645 - 16 Oct 2024

Viewed by 1750

Abstract

Autonomous vehicles (AVs) represent a transformative innovation in transportation, promising enhanced safety, efficiency, and sustainability. Despite these promises, achieving robustness, reliability, and adherence to ethical standards in AV systems remains challenging due to the complexity of integrating diverse technologies. This survey reviews literature [...] Read more.

Autonomous vehicles (AVs) represent a transformative innovation in transportation, promising enhanced safety, efficiency, and sustainability. Despite these promises, achieving robustness, reliability, and adherence to ethical standards in AV systems remains challenging due to the complexity of integrating diverse technologies. This survey reviews literature from 2017 to 2023, analyzing over 90 papers to explore the integration of knowledge graphs (KGs) into AV technologies. Our findings indicate that KGs significantly enhance AV systems by providing structured semantic understanding, improving real-time decision-making, and ensuring compliance with regulatory standards. The paper identifies that while KGs contribute to better environmental perception and contextual reasoning, challenges remain in their seamless integration with existing systems and in maintaining processing speed. We also address the ethical dimensions of AV decision-making, advocating for frameworks that prioritize safety and transparency. This review underscores the potential of KGs to address critical challenges in AV technologies, offering a hopeful and optimistic outlook for the development of robust, reliable, and socially responsible autonomous transportation solutions. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)

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62 pages, 1897 KiB

Open AccessReview

Construction of Knowledge Graphs: Current State and Challenges

by Marvin Hofer, Daniel Obraczka, Alieh Saeedi, Hanna Köpcke and Erhard Rahm

Information 2024, 15(8), 509; https://doi.org/10.3390/info15080509 - 22 Aug 2024

Cited by 8 | Viewed by 7435

Abstract

With Knowledge Graphs (KGs) at the center of numerous applications such as recommender systems and question-answering, the need for generalized pipelines to construct and continuously update such KGs is increasing. While the individual steps that are necessary to create KGs from unstructured sources [...] Read more.

With Knowledge Graphs (KGs) at the center of numerous applications such as recommender systems and question-answering, the need for generalized pipelines to construct and continuously update such KGs is increasing. While the individual steps that are necessary to create KGs from unstructured sources (e.g., text) and structured data sources (e.g., databases) are mostly well researched for their one-shot execution, their adoption for incremental KG updates and the interplay of the individual steps have hardly been investigated in a systematic manner so far. In this work, we first discuss the main graph models for KGs and introduce the major requirements for future KG construction pipelines. Next, we provide an overview of the necessary steps to build high-quality KGs, including cross-cutting topics such as metadata management, ontology development, and quality assurance. We then evaluate the state of the art of KG construction with respect to the introduced requirements for specific popular KGs, as well as some recent tools and strategies for KG construction. Finally, we identify areas in need of further research and improvement. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)

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19 pages, 2050 KiB

Open AccessReview

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

Cited by 1 | Viewed by 2622

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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