Introduction: What Is a Knowledge Graph?

Fensel, Dieter; Şimşek, Umutcan; Angele, Kevin; Huaman, Elwin; Kärle, Elias; Panasiuk, Oleksandra; Toma, Ioan; Umbrich, Jürgen; Wahler, Alexander

doi:10.1007/978-3-030-37439-6_1

Cited by 149 publications

(71 citation statements)

References 37 publications

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“…Knowledge graphs are knowledge modelling methods that support the generation of knowledge-aware explanations [2]. A KG is a network of interconnected entities, where each entity is represented by a graph node, and the relevance between the nodes is represented by relations [18]. Through the nodes and their relations, the KG is described as a set of triplets (head, relation, tail) as shown in Equation (1).…”

Section: Related Workmentioning

confidence: 99%

Transferrable Framework Based on Knowledge Graphs for Generating Explainable Results in Domain-Specific, Intelligent Information Retrieval

et al. 2022

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In modern industrial systems, collected textual data accumulates over time, offering an important source of information for enhancing present and future industrial practices. Although many AI-based solutions have been developed in the literature for a domain-specific information retrieval (IR) from this data, the explainability of these systems was rarely investigated in such domain-specific environments. In addition to considering the domain requirements within an explainable intelligent IR, transferring the explainable IR algorithm to other domains remains an open-ended challenge. This is due to the high costs, which are associated with intensive customization and required knowledge modelling, when developing new explainable solutions for each industrial domain. In this article, we present a transferable framework for generating domain-specific explanations for intelligent IR systems. The aim of our work is to provide a comprehensive approach for constructing explainable IR and recommendation algorithms, which are capable of adopting to domain requirements and are usable in multiple domains at the same time. Our method utilizes knowledge graphs (KG) for modeling the domain knowledge. The KG provides a solid foundation for developing intelligent IR solutions. Utilizing the same KG, we develop graph-based components for generating textual and visual explanations of the retrieved information, taking into account the domain requirements and supporting the transferability to other domain-specific environments, through the structured approach. The use of the KG resulted in minimum-to-zero adjustments when creating explanations for multiple intelligent IR algorithms in multiple domains. We test our method within two different use cases, a semiconductor manufacturing centered use case and a job-to-applicant matching one. Our quantitative results show a high capability of our approach to generate high-level explanations for the end users. In addition, the developed explanation components were highly adaptable to both industrial domains without sacrificing the overall accuracy of the intelligent IR algorithm. Furthermore, a qualitative user-study was conducted. We recorded a high level of acceptance from the users, who reported an enhanced overall experience with the explainable IR system.

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Section: Related Workmentioning

confidence: 99%

Transferrable Framework Based on Knowledge Graphs for Generating Explainable Results in Domain-Specific, Intelligent Information Retrieval

et al. 2022

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“…Knowledge Graphs (KGs) are a powerful data science technique to mine information from diverse data formats (Ehrlinger and Wöß, 2016;Afanasyev et al, 2019). It can be regarded as a knowledge base used by diverse AI programs to enhance their understanding and learning efficiency with information gathered from a variety of sources (Fensel et al, 2020). A KG provides a graph-structured topology to organize data and it can present interlinked descriptions of its entities, i.e., objects, events and situations, as well as abstract concepts with free-form semantics.…”

Section: Knowledge Graphmentioning

confidence: 99%

Convergence of Blockchain, Autonomous Agents, and Knowledge Graph to Share Electronic Health Records

Yao

Kshirsagar

Vaidya³

et al. 2021

Front. Blockchain

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In this article, we discuss a data sharing and knowledge integration framework through autonomous agents with blockchain for implementing Electronic Health Records (EHR). This will enable us to augment existing blockchain-based EHR Systems. We discuss how major concerns in the health industry, i.e., trust, security and scalability, can be addressed by transitioning from existing models to convergence of the three technologies – blockchain, agent-based modeling, and knowledge graph in a decentralized ecosystem. Each autonomous agent is responsible for instantiating key processes, such as user authentication and authorization, smart contracts, and knowledge graph generation through data integration among the participating stakeholders in the network. We discuss a layered approach for the design of the proposed system leading to an enhanced, safer clinical decision-making system. This can pave the way toward more informed and engaged patients and citizens by delivering personalized healthcare.

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“…Many authors tried to give a definition of knowledge graphs, but still a formal definition is missing, see [20]. In [21] the authors compared several definitions, but the only formal definition was related to RDF graphs which does not cover labeled property graphs.…”

Section: A Knowledge Graphmentioning

confidence: 99%

Knowledge Detection and Discovery using Semantic Graph Embeddings on Large Knowledge Graphs generated on Text Mining Results

Dörpinghaus

Jacobs

2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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Knowledge graphs play a central role in big data integration, especially for connecting data from different domains. Bringing unstructured texts, e.g. from scientific literature, into a structured, comparable format is one of the key assets. Here, we use knowledge graphs in the biomedical domain working together with text mining based document data for knowledge extraction and retrieval from text and natural language structures. For example cause and effect models, can potentially facilitate clinical decision making or help to drive research towards precision medicine. However, the power of knowledge graphs critically depends on context information. Here we provide a novel semantic approach towards a context enriched biomedical knowledge graph utilizing data integration with linked data applied to language technologies and text mining. This graph concept can be used for graph embedding applied in different approaches, e.g with focus on topic detection, document clustering and knowledge discovery. We discuss algorithmic approaches to tackle these challenges and show results for several applications like search query finding and knowledge discovery. The presented remarkable approaches lead to valuable results on large knowledge graphs.

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Introduction: What Is a Knowledge Graph?

Cited by 149 publications

References 37 publications

Transferrable Framework Based on Knowledge Graphs for Generating Explainable Results in Domain-Specific, Intelligent Information Retrieval

Transferrable Framework Based on Knowledge Graphs for Generating Explainable Results in Domain-Specific, Intelligent Information Retrieval

Convergence of Blockchain, Autonomous Agents, and Knowledge Graph to Share Electronic Health Records

Knowledge Detection and Discovery using Semantic Graph Embeddings on Large Knowledge Graphs generated on Text Mining Results

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