Ontology Visualization: An Overview

Achich, Nassira; Bouaziz, Bassem; Algergawy, Alsayed; Gargouri, Faïez

doi:10.1007/978-3-319-76348-4_84

Cited by 12 publications

(11 citation statements)

References 30 publications

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“…As structured knowledge representation models [16], ontologies are widely used in the feld of medicine/biology [16], engineering [36,51], sociology [22], computer science [42], and so on. Achich et al [1] review diferent application domains and generic visualization pipelines of ontology visualization.…”

Section: Knowledge-based Visualizationsmentioning

confidence: 99%

“…We illustrate the utility of ConceptScope by building a prototype application 1 that visualizes computer science-related documents such as research abstracts and articles using the Computer Science Ontology (CSO) as its reference. Through a set of use-case scenarios, we highlight the navigation, exploration, and comparison functions aforded by the technique, and discuss its extension to other domains and scenarios.…”

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confidence: 99%

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ConceptScope: Organizing and Visualizing Knowledge in Documents based on Domain Ontology

Zhang

Chandrasegaran

2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Current text visualization techniques typically provide overviews of document content and structure using intrinsic properties such as term frequencies, co-occurrences, and sentence structures. Such visualizations lack conceptual overviews incorporating domainrelevant knowledge, needed when examining documents such as research articles or technical reports. To address this shortcoming, we present ConceptScope, a technique that utilizes a domain ontology to represent the conceptual relationships in a document in the form of a Bubble Treemap visualization. Multiple coordinated views of document structure and concept hierarchy with text overviews further aid document analysis. ConceptScope facilitates exploration and comparison of single and multiple documents respectively. We demonstrate ConceptScope by visualizing research articles and transcripts of technical presentations in computer science. In a comparative study with DocuBurst, a popular document visualization tool, ConceptScope was found to be more informative in exploring and comparing domain-specifc documents, but less so when it came to documents that spanned multiple disciplines. CCS CONCEPTS• Human-centered computing → Information visualization.

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Section: Knowledge-based Visualizationsmentioning

confidence: 99%

mentioning

confidence: 99%

ConceptScope: Organizing and Visualizing Knowledge in Documents based on Domain Ontology

Zhang

Chandrasegaran

2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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“…A knowledge graph consists of a set of concepts (classes), a set of attributes (data type properties), relationships (object properties), and constraints to abstractly represent a specific event [34]. Visualization of MiKG4MD provides a clear overview of the hierarchy and connections within the knowledge base, which is an important step in the process of knowledge graph construction.…”

Section: Knowledge Graph Visualizationmentioning

confidence: 99%

Predicting the relationships between gut microbiota and mental disorders with knowledge graphs

Liu

Pan

Wang

et al. 2020

Health Inf Sci Syst

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Gut microbiota produce and modulate the production of neurotransmitters which have been implicated in mental disorders. Neurotransmitters may act as ‘matchmaker’ between gut microbiota imbalance and mental disorders. Most of the relevant research effort goes into the relationship between gut microbiota and neurotransmitters and the other between neurotransmitters and mental disorders, while few studies collect and analyze the dispersed research results in systematic ways. We therefore gather the dispersed results that in the existing studies into a structured knowledge base for identifying and predicting the potential relationships between gut microbiota and mental disorders. In this study, we propose to construct a gut microbiota knowledge graph for mental disorder, which named as MiKG4MD. It is extendable by linking to future ontologies by just adding new relationships between existing information and new entities. This extendibility is emphasized for the integration with existing popular ontologies/terminologies, e.g. UMLS, MeSH, and KEGG. We demonstrate the performance of MiKG4MD with three SPARQL query test cases. Results show that the MiKG4MD knowledge graph is an effective method to predict the relationships between gut microbiota and mental disorders.

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“…In particular, despite obvious differences between the expressiveness of conceptual modelling languages (CMLs) and ontology languages, many tools have partially validated this claim [9,10,11,12]. Thus, the effectiveness of using graphical syntax based on these conceptual languages for expressing ontologies, even with complex languages, is still been considered for ontology engineering environments [3,2]. Evidence for these claims indicate that the common core of UML, (E)ER and ORM 2 can be formalised in the ALN I Description Logics (DL) [13] guaranteeing tractable reasoning over them [14].…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…The lack of an adequate and seamless environment affects not only the uptake of ontologies by new users, but also the quality of artifacts created by modellers. Previous surveys [3,4] reveal that a lot of ontology engineering environments have been developed, but too few of them are webbased systems and in almost all the cases they are mere visualisers, with limited graphical features and lacking of inference services. In fact, some of them claim no well-accepted framework for common authoring tasks exist because of a poor understanding of the effectiveness of tools [2].…”

Section: Introductionmentioning

confidence: 99%

A Reference Architecture for Ontology Engineering Web Environments

Braun

Estévez

Fillottrani

2019

JC&ST

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Ontology authoring, maintenance and use are never easy tasks, mostly due to the complexity of real domains and how they dynamically change as well as different background possessed by modellers about methodologies and formal languages. However, although the needs for ontologies are well-understood, not less important is to provide editing tools to manipulate and understand them. In this context, this work proposes and documents a reference architecture for such tools running in web environments. Moreover, it provides the rationale for boosting the collaborative development of a novel tool based on this architecture, named crowd. Previous surveys reveal that few Web-based ontology engineering environments have been developed and in addition, almost all of them are mere visualisers, with limited graphical features and lacking inference services

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Ontology Visualization: An Overview

Cited by 12 publications

References 30 publications

ConceptScope: Organizing and Visualizing Knowledge in Documents based on Domain Ontology

ConceptScope: Organizing and Visualizing Knowledge in Documents based on Domain Ontology

Predicting the relationships between gut microbiota and mental disorders with knowledge graphs

A Reference Architecture for Ontology Engineering Web Environments

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