Ontology Visualization: Approaches and Software Tools for Visual Representation of Large Ontologies in Learning

Anikin, Anton; Litovkin, Dmitry; Kultsova, Marina; Sarkisova, Elena; Petrova, Tatyana

doi:10.1007/978-3-319-65551-2_10

Cited by 13 publications

(10 citation statements)

References 19 publications

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“…We compare the notations with respect to design principles for visual notations [43] derived from several seminal works in human cognition [4,23,58,61,65] and evaluate them in a comparative user study. 3 We implemented both visual notations in UnSHACLed to allow creating and editing constraints in a constraint language independent way. Users can switch between visual notations and use the created RDF constraints to validate input data from within the same editor.…”

Section: Contributionsmentioning

confidence: 99%

Visual notations for viewing RDF constraints with UnSHACLed

Lieber

Meester

Heyvaert

et al. 2022

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The quality of knowledge graphs can be assessed by a validation against specified constraints, typically use-case specific and modeled by human users in a manual fashion. Visualizations can improve the modeling process as they are specifically designed for human information processing, possibly leading to more accurate constraints, and in turn higher quality knowledge graphs. However, it is currently unknown how such visualizations support users when viewing RDF constraints as no scientific evidence for the visualizations’ effectiveness is provided. Furthermore, some of the existing tools are likely suboptimal, as they lack support for edit operations or common constraints types. To establish a baseline, we have defined visual notations to represent RDF constraints and implemented them in UnSHACLed, a tool that is independent of a concrete RDF constraint language. In this paper, we (i) present two visual notations that support all SHACL core constraints, built upon the commonly used visualizations VOWL and UML, (ii) analyze both notations based on cognitive effective design principles, (iii) perform a comparative user study between both visual notations, and (iv) present our open source tool UnSHACLed incorporating our efforts. Users were presented RDF constraints in both visual notations and had to answer questions based on visualization task taxonomies. Although no statistical significant difference in mean error rates was observed, all study participants preferred ShapeVOWL in a self assessment to answer RDF constraint-related questions. Furthermore, ShapeVOWL adheres to more cognitive effective design principles according to our performed comparison. Study participants argued that the increased visual features of ShapeVOWL made it easier to spot constraints, but a list of constraints – as in ShapeUML – is easier to read. However, also that more deviations from the strict UML specification and introduction of more visual features can improve ShapeUML. From these findings we conclude that ShapeVOWL has a higher potential to represent RDF constraints more effective compared to ShapeUML. But also that the clear and efficient text encoding of ShapeUML can be improved with visual features. A one-size-fits-all approach to RDF constraint visualization and editing will be insufficient. Therefore, to support different audiences and use cases, user interfaces of RDF constraint editors need to support different visual notations.

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Section: Contributionsmentioning

confidence: 99%

Visual notations for viewing RDF constraints with UnSHACLed

Lieber

Meester

Heyvaert

et al. 2022

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“…In OntoSearch, the search results, i.e. target 3-tuples were merged by logical rules and visualized as a graphic comprehensibly [10]- [12]. On this basis, researchers developed OntoSearch2 which improved some functions of OntoSearch [13].…”

Section: A Ontology Searchmentioning

confidence: 99%

Building Real-Time Ontology Based on Adaptive Filter for Multi-Domain Knowledge Organization

Jianhui

Song

et al. 2021

IEEE Access

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Multi-domain knowledge organization is an effective way of correlating cross-domain knowledge or intercommunicating between cross-domain knowledge systems. As a knowledge organization model, ontology is widely used in information and management systems. To organize multi-domain knowledge, ontologies in different domains correlate to each other directly or indirectly. Generally, matching and integrating ontologies of different domain into a large scale ontology is the common way of directly correlating, while building a top level ontology is the main method for indirectly correlating. As the scale of domain ontologies get larger and larger, both direct and indirect methods become more difficult and time-consuming. In order to improve the organization of multi-domain knowledge, this paper presents a novel ontology organization method to build real-time ontology by adaptive filter while user presenting requirements. Only the entities related to user requirements are integrated, while building a real-time ontology. Firstly, the method searches domain ontologies that are related to user requirements. Then subontologies are extracted from search results by filter, and they are integrated into a new ontology under direction of filter, i.e. real-time ontology. Finally, four criteria are introduced to evaluate real-time ontology. The experiment results illuminate that real-time ontology perform excellently in accuracy, recall, correctness and especially time-consuming.INDEX TERMS Real-time ontology, multi-domain knowledge organization, ontology matching, ontology integration, knowledge engineering

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“…Among the W3C standard language, OWL is reportedly most popular because of its expressiveness [32,41,42]. The ontology editor tools also include Proté gé [43,44,45,46], FAO AGROVOC Concept Server Workbench Tool [47,48]; OBO-Edit [49,50]; SWOOP [51,52]; Apollo [53,54]; IsaViz [55,56]; TopBraidComposer [57,58] and citrus ontology developed from scratch using Gruff on AllegroGraph [59].…”

Section: Ontology Developmentmentioning

confidence: 99%

A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for various Domains

Aminu¹,

Oyefolahan²,

Abdullahi³

et al. 2020

IJIEEB

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The success of machine represented web known as semantic web largely hinges on ontologies. Ontology is a data modeling technique for structured data repository premised on collection of concepts with their semantic relationships and constraints on domain. There are existing methodologies to aid ontology development process. However, there is no single correct ontology design methodology. Therefore, this paper aims to present a review on existing ontology development approaches for different domains with the goal of identifying individual methodology's weakness and suggests for hybridization in order to strengthen ontology development in terms of its content and constructions correctness. The analysis and comparison of the review were carried out by considering these criteria but not limited to: activities of each method, the initial domain of the methodology, ontology created from scratch or reuse, frequently used ontology management tools based on literature, subject granularity, and usage across different platforms. This review based on the literature showed some approaches that exhibit the required principles of ontology engineering in tandem with software development principles. Nonetheless, the review still noted some gaps among the methodologies that when bridged or hybridized a better correctness of ontology development would be achieved in building intelligent system.

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Ontology Visualization: Approaches and Software Tools for Visual Representation of Large Ontologies in Learning

Cited by 13 publications

References 19 publications

Visual notations for viewing RDF constraints with UnSHACLed

Visual notations for viewing RDF constraints with UnSHACLed

Building Real-Time Ontology Based on Adaptive Filter for Multi-Domain Knowledge Organization

A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for various Domains

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