Ontology Visualization Research Articles

Predictive Gaze Analytics: A Comparative Case Study of the Foretelling Signs of User Performance during Interaction with Visualizations of Ontology Class Hierarchies

The current research landscape in ontology visualization has largely focused on tool development, yielding an extensive array of visualization tools. Although many existing solutions provide multiple ontology visualization layouts, there is limited research in adapting to an individual user’s performance, despite successful applications of adaptive technologies in related fields, including information visualization. In an effort to innovate beyond traditional one-size-fits-all visualizations, this paper contributes one step towards realizing user adaptive visualization by recognizing timely moments when users may potentially need intervention, as real-time adaptation can only occur if it is possible to correctly predict user success and failure during an interaction in the first place. In addition, an open-source, reusable, and extensible software: Beach Environment for the Analytics of Human Gaze (BEACH-Gaze) is made available to the broader scientific community interested in descriptive and predictive gaze analytics. Building on a wealth of research in eye tracking, this paper compares four approaches to predictive gaze analytics through a series of experiments that utilize scheduled gaze digests, irregular gaze events, the last known gaze status, as well as all gaze captured for a user at a given moment in time. The results from a set of experimental trials suggest that irregular gaze events are most informative of early predictions of user performance, whereas cognitive workload appears to be most indicative of overall user performance in the task scenario presented in this paper. These empirical findings highlight the importance of an analytical approach to gaze on user predictions and indicate careful consideration when applying.

Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning

The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains where collecting large volumes of annotated data is not trivial. Biomedical data include several modalities containing complementary information, such as medical images and reports: images are often large and encode low-level information, while reports include a summarized high-level description of the findings identified within data and often only concerning a small part of the image. However, only a few methods allow to effectively link the visual content of images with the textual content of reports, preventing medical specialists from properly benefitting from the recent opportunities offered by deep learning models. This paper introduces a multimodal architecture creating a robust biomedical data representation encoding fine-grained text representations within image embeddings. The architecture aims to tackle data scarcity (combining supervised and self-supervised learning) and to create multimodal biomedical ontologies. The architecture is trained on over 6,000 colon whole slide Images (WSI), paired with the corresponding report, collected from two digital pathology workflows. The evaluation of the multimodal architecture involves three tasks: WSI classification (on data from pathology workflow and from public repositories), multimodal data retrieval, and linking between textual and visual concepts. Noticeably, the latter two tasks are available by architectural design without further training, showing that the multimodal architecture that can be adopted as a backbone to solve peculiar tasks. The multimodal data representation outperforms the unimodal one on the classification of colon WSIs and allows to halve the data needed to reach accurate performance, reducing the computational power required and thus the carbon footprint. The combination of images and reports exploiting self-supervised algorithms allows to mine databases without needing new annotations provided by experts, extracting new information. In particular, the multimodal visual ontology, linking semantic concepts to images, may pave the way to advancements in medicine and biomedical analysis domains, not limited to histopathology.

Sentiment Analysis: A Hybrid Approach on Twitter Data

In the present scenario, Internet communities, forum and blogging sites play a very crucial role to present opinions, views and the comments on various events. As the reachability of sites are beyond the control of national boundaries, sometimes this leads to the conviction and persuasion of thoughts without considering any legal subsequences, and also influence the belief of others as well, thus finding or identifying the sentiments of public from the social media content is one of the major research issue. Analysis of the sentiments of social media data is very difficult to understand as this typically does not exhibit a suspicious pattern in the flow of information like individual's actual opinion on a specific occasion etc. With the advancement of methodology and easy to use, the users of social media sites are now expressing their opinions, sharing their views and experiences through images, text, animation, audio, video etc. Because of the complexity the conventional text based sentiment analysis procedure, a lot of methods have been evolved, however studies suggest mostly more complicated procedure and approaches. Twitter® (or X), is one of the most common platform used widely by individual to express their opinions and sentiments on various events. Twitter sentiment analysis basically deals with the analysis of twitter quotes to find the hidden pattern in the sentiments expressed by the users in past. This paper aims to takes the challenges regarding social media sentiments analysis and developed a hybrid approach (Text and visual sentiment) on twitter data for sentiment analysis by using NLP-based opinion clustering, textual mining, emotion API and some machine learning techniques for visual ontology. Simulation result shows the significance of work.

Multidimensional Concept Map Representation of the Learning Objects Ontology Model for Personalized Learning

<p>This work presents the creation and representation of an ontology model for the domain knowledge used for learning objects. The purpose of the developed ontology model is to define relations between learning objects that can be applied for their effective search and visualization. As the number of learning objects increases, the representation of the knowledge domain becomes challenging. In this paper, the authors propose the application of multidimensional concept maps (MCMs) for domain knowledge representation. The definition of different attributes used in the ontology model allow for defining the different dimensions needed for MCM ontology visualization. In order to achieve integration of the defined ontology model and MCMs, a software tool named Ontology-based system for learning objects retrieval (OBSLO) was developed. OBSLO’s role is to dynamically generate MCMs given the defined ontology with its relations and attributes, while also providing a content delivery environment and working space for learners. Proposed OBSLO architecture with integrated ontology model and MCMs was evaluated and compared to the learning management system where ontology and MCMs were not used. It was shown that learners using OBSLO showed better success rate in learning and positive level of satisfaction.</p> <p> </p>

Flat design as visualization of flat ontologies

The object of this research is design as a semiotic embodiment of the modern worldview, the quintessence of which are flat ontologies. The subject of the study is parallelism in the philosophy of flat ontologies and modern flat design. The purpose of this study is to explicate the philosophical problem of modern design, which is a consequence of the unconscious expression by designers of the philosophy of flat ontologies. The philosophy of flat ontologies struggles with anthropocentrism, insisting that a person should not have a privileged ontological status compared to other objects. In the philosophy of flat ontologies, a machine or a neural network are agents of action in the same way as a person. Modern flat design is an expression of this position, since the modern visual information environment is less and less adapted to human organs of perception, more and more transhumanistic. Both flat design and flat ontologies are a consequence of the horizontally oriented, materialistic line of philosophy, which forms the worldview of modern society. Thanks to the explication of the problem of flat design, it can be understood that the horizontally oriented line of philosophy is problematic for the further existence of man. The main conclusion of the study is that the horizontally oriented line in philosophy – materialism and positivism – in their logical development lead to the philosophy of flat ontologies, which postulates the same ontological status for human and non-human agents. Such a position is antihuman and transhumanistic, and this becomes evident in the philosophical analysis of modern design. The main stylistics of modern design is a flat design, which uses fourth-order simulacra as the main type of signs. The use of simulacrum signs in design creates an environment that is increasingly less adapted to human organs of perception, a transhumanistic environment.

Research on ontology construction of traditional Chinese handmade paper: Take Kaihua paper as an example

Abstract Digital technology has broadened the research methods in the field of cultural heritage protection, and promoted the online organization and utilization of different resources. New digital technology is used to further explore the resources related to traditional Chinese handmade paper (TCHP) in order to comprehensively and systematically realize the knowledge association of different types of handmade paper. In this article, the characteristics of TCHP are combined to existing mature and generic ontologies to design a new ontology model of TCHP, and Protégé software is used to construct the visual ontology. The model is verified by analyzing the digital resources of Kaihua paper. Constructing the ontology of TCHP can realize the knowledge organization and visual presentation of related resources, and also promote the further utilization of the resources and wider spread of the culture of TCHP.

OntoTrek: 3D visualization of application ontology class hierarchies.

An application ontology often reuses terms from other related, compatible ontologies. The extent of this interconnectedness is not readily apparent when browsing through larger textual presentations of term class hierarchies, be it Manchester text format OWL files or within an ontology editor like Protege. Users must either note ontology sources in term identifiers, or look at ontology import file term origins. Diagrammatically, this same information may be easier to perceive in 2 dimensional network or hierarchical graphs that visually code ontology term origins. However, humans, having stereoscopic vision and navigational acuity around colored and textured shapes, should benefit even more from a coherent 3-dimensional interactive visualization of ontology that takes advantage of perspective to offer both foreground focus on content and a stable background context. We present OntoTrek, a 3D ontology visualizer that enables ontology stakeholders-students, software developers, curation teams, and funders-to recognize the presence of imported terms and their domains, ultimately illustrating how projects can capture knowledge through a vocabulary of interwoven community-supported ontology resources.

A Semantic and Smart Framework for Handling Multilingual Linguistic Knowledge

The authors propose a semantic and smart assistance framework for handling linguistic knowledge, called LingFramework. It targets both expert and novice users. It aims to assist the user in understanding the different aspects of the linguistic domain and ease the process of proposing lingware applications. LingFramework is based on a multilingual linguistic domain ontology called LingOnto. It allows (1) representing linguistic data, linguistic processing functionalities and linguistic processing features, and (2) reasoning, via a SWRL-based reasoning engine, about the linguistic knowledge. Currently, it covers English, French, and Arabic languages. To facilitate the interaction with LingOnto, an ontology visualization tool called LingGraph is proposed. It offers an easy-to-use interface for users not familiar with ontologies. It provides a SPARQL pattern-based approach to allow a smart search interaction functionality. LingFramework is applied to assist the user in identifying valid linguistic processing pipelines related to lingware applications. The evaluation results are promising.

Ontopanel: A Tool for Domain Experts Facilitating Visual Ontology Development and Mapping for FAIR Data Sharing in Materials Testing

In recent years, the design and development of materials are strongly interconnected with the development of digital technologies. In this respect, efficient data management is the building block of material digitization and, in the field of materials science and engineering (MSE), effective solutions for data standardization and sharing of different digital resources are needed. Therefore, ontologies are applied that represent a map of MSE concepts and relationships between them. Among different ontology development approaches, graphical editing based on standard conceptual modeling languages is increasingly used due to its intuitiveness and simplicity. This approach is also adopted by the Materials-open-Laboratory project (Mat-o-Lab), which aims to develop domain ontologies and method graphs in accordance with testing standards in the field of MSE. To suit the actual demands of domain experts in the project, Ontopanel was created as a plugin for the popular open-source graphical editor diagrams.net to enable graphical ontology editing. It includes a set of pipeline tools to foster ontology development in diagrams.net, comprising imports and reusage of ontologies, converting diagrams to Web Ontology Language (OWL), verifying diagrams using OWL rules, and mapping data. It reduces learning costs by eliminating the need for domain experts to switch between various tools. Brinell hardness testing is chosen in this study as a use case to demonstrate the utilization of Ontopanel.

LncRNA MEG3 regulates the interplay between Th17 and Treg cells in Behçet's disease and systemic lupus erythematosus

BackgroundBehçet's disease (BD) and systemic lupus erythematosus (SLE) are two autoimmune inflammatory diseases of indefinite etiology. However, up till now, no study has explored the exact regulatory mechanisms of lncRNA maternally expressed gene-3 (MEG3) over the balance between regulatory T-cells (Treg) and T helper-17 (Th17) cells in BD and SLE. AimThe current study aimed to investigate the role of lncRNA MEG3 in the interplay between the anti-inflammatory Treg/transcription factor forkhead box P3 (FOXP3) axis versus the pro-inflammatory Th17/retinoic acid orphan receptor-γt (RORγt) axis. Main methods100 subjects, 35 with BD and 35 with SLE in addition to 30 healthy participants were included in the study. Gene expression analysis was performed and ShinyGO database was utilized for in-depth analysis and graphical visualization of the gene ontology (GO) and pathway enrichment analysis for lncRNA and the other target genes. Key findingsThe current results demonstrate the upregulation of lncRNA MEG3 in BD but not SLE patients. Moreover, significant differences in RORγt and FOXP3 were found between BD and SLE patients. The present findings linked lncRNA MEG3 to BD activity scores as well as CRP levels. Finally, lncRNA MEG3 showed excellent diagnostic power for BD, in addition to adequate discriminative power that can be used to differentiate between BD and SLE. SignificanceThe current study objectively elucidated a framework for the involvement of Treg/Th17 through transcription factors RORγt and FOXP3, in addition to their links to the downstream cytokines network including TGF-ꞵ, IL-10, IL-17 and IL-23 in BD and SLE pathogenesis and activity.

An Ontology based Smart Management of Linguistic Knowledge

Natural language processing provides a very significant contribution to various application areas such as multilingual big data, information retrieval, data integration and multilingual web. However, handling linguistic knowledge to develop such lingware applications is a crucial issue, especially for linguistic novice users. To deal with this issue, a "smart" linguistic knowledge management may help the users to understand the meaning, scope and especially the use of related techniques and algorithms. In this paper, (1) we propose a semantic processing of linguistic knowledge based on a multilingual linguistic domain ontology, called LingOnto. Compared to related work, LingOnto does not only handles linguistic data, but also linguistic processing functionalities and linguistic processing features. Besides, it allows, via a reasoning engine, inferring new linguistic knowledge and assisting in the process of proposing lingware applications. This is particularly useful for novice users, but can also provide new perspectives for the expert ones. LingOnto covers the French, English and Arabic languages. (2) We propose also an assisted user friendly ontology visualization tool called LingGraph. It facilitates the interaction with LingOnto. It offers an easy to use interface for users not familiar with ontologies. It is based on a SPARQL pattern-based approach to allow a smart search interaction functionality to visualize only the ontological view corresponding to the user’s needs and preferences. In order to evaluate LingOnto, we apply it to a framework of identifying valid natural language processing pipelines. Finally, we give the results of the carried-out experiments.

Domain Concept Extraction and Ontology Visualization

A semantic navigation method based on ontology was proposed to solve the problem of digital library information trek. In this paper, the domain concept was extracted based on the knowledge base, linguistics, combined word length ratio, domain correlation and domain consistency.Finally, the domain ontology visualization scheme was put forward, and the related updating technology, wide-angle and aggregation technology were analyzed.The process of using Touch Graph to realize the navigation of ontology visual information was also analyzed.

Similarity Calculation, Enrichment Analysis, and Ontology Visualization of Biomedical Ontologies using UFO.

The rapid growth of biomedical ontologies observed in recent years has been reported to be useful in various applications. In this article, we propose two main-function protocols-term-related and entity-related-with the three most common ontology analyses, including similarity calculation, enrichment analysis, and ontology visualization, which can be done by separate methods. Many previously developed tools implementing those methods run on different platforms and implement a limited number of the methods for similarity calculation and enrichment analysis tools for a specific type of biomedical ontology, although any type can be acceptable. Moreover, depending on each application, methods have distinct advantages; thus, the greater the number of methods a tool has, the better decisions that users make. The protocol here implements all the analyses above using an advanced popular tool called UFO. UFO is a Cytoscape app that unifies most of the semantic similarity measures for between-term and between-entity similarity calculation for biomedical ontologies in OBO format, which can calculate the similarity between two sets of entities and weigh imported entity networks, as well as generate functional similarity networks. The complete protocol can be performed in 30 min and is designed for use by biologists with no prior bioinformatics training. © 2021 Wiley Periodicals LLC. Basic Protocol: Running UFO using a list of input Gene Ontology, Disease Ontology, or Human Phenotype Ontology data.

MLGrafViz: multilingual ontology visualization plug-in for protégé

Natural Language Processing (NLP) is rapidly increasing in all domains of knowledge acquisition to facilitate different language user. It is required to develop knowledge based NLP systems to provide better results. Knowledge based systems can be implemented using ontologies where ontology is a collection of terms and concepts arranged taxonomically. The concepts that are visualized graphically are more understandable than in the text form. In this research paper, new multilingual ontology visualization plug-in MLGrafViz is developed to visualize ontologies in different natural languages. This plug-in is developed for protégé ontology editor. This plug-in allows the user to translate and visualize the core ontology into 135 languages.

MLGrafViz: multilingual ontology visualization plug-in for Protégé

Natural language processing (NLP) is rapidly increasing in all domains of knowledge acquisition to facilitate different language user. It is required to develop knowledge-based NLP systems to provide better results. Knowledge based systems can be implemented using ontologies where ontology is a collection of terms and concepts arranged taxonomically. The concepts that are visualized graphically are more understandable than in the text form. In this research paper, new multilingual ontology visualization plug-in MLGrafViz is developed to visualize ontologies in different natural languages. This plug-in is developed for Protégé ontology editor. This plug-in allows the user to translate and visualize the core ontology into 135 languages.

Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage

Due to the increasing use of data analytics, information visualization is getting more and more important. However, as data get more complex, so does visualization, often leading to ad hoc and cumbersome solutions. A recent alternative is the use of the so-called knowledge-assisted visualization tools. In this paper, we present STMaps (Spatio-Temporal Maps), a multipurpose knowledge-assisted ontology-based visualization tool of spatio-temporal data. STMaps has been (originally) designed to show, by means of an interactive map, the content of the SILKNOW project, a European research project on silk heritage. It is entirely based on ontology support, as it gets the source data from an ontology and uses also another ontology to define how data should be visualized. STMaps provides some unique features. First, it is a multi-platform application. It can work embedded in an HTML page and can also work as a standalone application over several computer architectures. Second, it can be used for multiple purposes by just changing its configuration files and/or the ontologies on which it works. As STMaps relies on visualizing spatio-temporal data provided by an ontology, the tool could be used to visualize the results of any domain (in other cultural and non-cultural contexts), provided that its datasets contain spatio-temporal information. The visualization mechanisms can also be changed by changing the visualization ontology. Third, it provides different solutions to show spatio-temporal data, and also deals with uncertain and missing information. STMaps has been tested to browse silk-related objects, discovering some interesting relationships between different objects, showing the versatility and power of the different visualization tools proposed in this paper. To the best of our knowledge, this is also the first ontology-based visualization tool applied to silk-related heritage.

GENETIC POLYMOPRHISMS MAY DIFFERENTIATE BETWEEN COMPLICATED PEDIATRIC SMALL BOWEL VERSUS COLONIC CROHN’S DISEASE

Abstract Objectives About 9% of pediatric Crohn’s disease (CD) patients experience complications within 3 years from diagnosis in spite of anti-Tumor Necrosis Factor-alpha (anti-TNF) biologic treatment. Within 5 years, such complications may require surgical interventions in up to 20% of cases. Due to the relatively rare nature of these complicated pediatric CD cases within large CD cohorts, traditional genome wide association studies (GWAS) rarely capture this patient population. Our aim was to identify critical genetic associations of anti-TNF refractory complicated pediatric CD subtypes. Methods The study was approved by BCM IRB. Included patients had developed CD between the ages of 6 and 18. The Paris classification was used to place patients in the following categories: 1. Anti-TNF refractory small bowel CD (RSB-CD): L4b; L4b/B2; or L4b/B2B3 who had or were imminent to have small bowel resection. 2.. Anti-TNF refractory colonic CD (RC-CD): L2 requiring ileal diversion; or L2/B2; or L2/B2B3 who had or were considered to receive colectomy or partial colonic resection. Whole-exome sequencing (WES) was performed from peripheral blood derived DNA. PLINK (https://www.cog-genomics.org/plink2/) was used to identify enriched single nucleotide polymorphisms (SNPs). The level of significance for exome wide differentiation was relaxed to p<0.01. The potential deleteriousness of single nucleotide variants was determined by CADD (https://cadd.gs.washington.edu/). Gene Ontology enRIchment anaLysis and visuaLizAtion tool (GORILLA) was used to compare gene lists associated with colonic enriched SNPs versus ileal enriched SNPs (http://geneontology.org/). Results In eight RSB-CD and 11 RC-CD cases, age at presentation, gender, ethnicity, and need for surgery (75% and 55%, respectively) did not significantly differ between the groups. There were 269 genes associated with 277 SNPs that had significantly (p<0.01) different allele variation between colonic vs ileal CD. Of these, 170 genes associated with 116 distinct SNPs had a CADD score of >10. Out of the top five SNP candidate associated genes, four (4/5 = 80%: EFNA3, STEAP1B, DSG1, and CYP4F2) have been already linked with Crohn’s disease and/or colitis. GORILLA based investigation indicated that colonic CD associated genes were enriched (p<0.001) in biologic function relevant for lipid metabolic process (GO:0006629), small molecule metabolic process (GO:0044281), and organic substance metabolic process (GO:0071704) compared to ileal CD associated genes. Conclusions We have uncovered candidate genetic risk loci, which may differentially influence pediatric CD towards developing primarily small intestinal or colonic complications. The discovered loci hold significant potential as novel genetic modifiers, which may set the basis for risk assessment, prevention and personalized treatment against complicated pediatric CD.

Forming Cognitive Maps of Ontologies Using Interactive Visualizations

Ontology datasets, which encode the expert-defined complex objects mapping the entities, relations, and structures of a domain ontology, are increasingly being integrated into the performance of challenging knowledge-based tasks. Yet, it is hard to use ontology datasets within our tasks without first understanding the ontology which it describes. Using visual representation and interaction design, interactive visualization tools can help us learn and develop our understanding of unfamiliar ontologies. After a review of existing tools which visualize ontology datasets, we find that current design practices struggle to support learning tasks when attempting to build understanding of the ontological spaces within ontology datasets. During encounters with unfamiliar spaces, our cognitive processes align with the theoretical framework of cognitive map formation. Furthermore, designing encounters to promote cognitive map formation can improve our performance during learning tasks. In this paper, we examine related work on cognitive load, cognitive map formation, and the use of interactive visualizations during learning tasks. From these findings, we formalize a set of high-level design criteria for visualizing ontology datasets to promote cognitive map formation during learning tasks. We then perform a review of existing tools which visualize ontology datasets and assess their interface design towards their alignment with the cognitive map framework. We then present PRONTOVISE (PRogressive ONTOlogy VISualization Explorer), an interactive visualization tool which applies the high-level criteria within its design. We perform a task-based usage scenario to illustrate the design of PRONTOVISE. We conclude with a discussion of the implications of PRONTOVISE and its use of the criteria towards the design of interactive visualization tools which help us develop understanding of the ontological space within ontology datasets.

The Role of a Facilitator in Co-Design Applications for Exploratory Analysis in Domains of High Complexity: The Case of MAHiCGO

Background: Robust and useful tools for exploratory analysis in biosciences are still lagging behind the size and complexity of the biological datasets produced since the completion of the human genome in 2000. A possible reason is that developers are unlikely to understand domain and case-specific requirements of existing research questions. Methods: We formed a design team comprising a visualization expert, a Human Computer Interaction (HCI) researcher, bioinformatics domain experts, and the Principal Investigator (PI) as a `facilitator' filling the communication gap between them. We implemented co-design methodology. Results: We identified the need for an interactive visual analytic tool for exploratory analysis of biological data. We describe the process of developing MAHiCGO, a novel tool for the simultaneous visualization of MA Gene Expression data, Hi-C data and Gene Ontology (GO) information in an interactive manner for the exploratory analysis of biological data. Conclusion: The key finding of this research to include a facilitator role in the co-design is useful in the evolving fields of design research and of bioinformatics, merging computational sciences with biosciences. The findings support in understanding new functional roles in the field of design, in particular design of computer applications in highly complex domains such as bioinformatics, and for highly complex tasks such as exploratory tasks. The findings also stress on the key role of visualization to expand user cognitive capabilities, and of co-design for constant engagement of domain experts in the creation process.

Non-Empirical Metrics for Ontology Visualizations Evaluation and Comparing

There are numerous ontology visualization systems, however, the choice of a visualization system is non-trivial, as there is no method for evaluation and comparing them, except for empirical experiments, that are subjective and costly. In this research, we aim to develop non- empirical metrics for ontology visualizations evaluation and comparing. First, we propose several half-formal metrics that require expert evaluation. These metrics are completeness, semanticity, and conservativeness. We apply the proposed metrics to evaluate and compare VOWL and Logic Graphs visualization systems. And second, we develop a com- pletely computable measure for the complexity of ontology visualizations, based on graph theory and information theory. In particular, ontology visualizations are considered as hypergraphs and the information mea- sure is derived from the Hartley function. The usage of the proposed information measure is exemplified by the evaluation of visualizations of the sample of axioms from the DoCO ontology in Logic Graphs and Graphol. These results can be practically applied for choosing ontology visualization systems in general and regarding a particular ontology.