PerfectO: An Online Toolkit for Improving Quality, Accessibility, and Classification of Domain-Based Ontologies

Gyrard, Amélie; Atemezing, Ghislain Auguste; Serrano, Martin M.

doi:10.1007/978-3-030-64619-6_7

Cited by 5 publications

(6 citation statements)

References 39 publications

(50 reference statements)

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“…The DAO ontology was evaluated following the semantic web best practices recognized by the International Semantic Web Conference Resource Track guidelines [68], which provide the following criteria: (1) impact, (2) reusability, (3) design and technical quality, and (4) availability. We have also followed the PerfectO methodology [40], which synthesizes a set of additional best practices and eases their achievements [69]. We have discussed the results of applying the following criteria to our DAO: the ontology is available on the web as a graph visualization using web-based visualization of ontologies (WebVOWL) [72,73].…”

Section: Discussionmentioning

confidence: 99%

Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study

Lokala¹,

Lamy²,

Daniulaityte³

et al. 2022

JMIR Public Health Surveill

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Background Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the use of these novel data sources for epidemiological surveillance of substance use behaviors and trends. Objective The key aims were to describe the development and application of the drug abuse ontology (DAO) as a framework for analyzing web-based and social media data to inform public health and substance use research in the following areas: determining user knowledge, attitudes, and behaviors related to nonmedical use of buprenorphine and illicitly manufactured opioids through the analysis of web forum data Prescription Drug Abuse Online Surveillance; analyzing patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the United States through analysis of Twitter and web forum data (eDrugTrends); assessing trends in the availability of novel synthetic opioids through the analysis of cryptomarket data (eDarkTrends); and analyzing COVID-19 pandemic trends in social media data related to 13 states in the United States as per Mental Health America reports. Methods The domain and scope of the DAO were defined using competency questions from popular ontology methodology (101 ontology development). The 101 method includes determining the domain and scope of ontology, reusing existing knowledge, enumerating important terms in ontology, defining the classes, their properties and creating instances of the classes. The quality of the ontology was evaluated using a set of tools and best practices recognized by the semantic web community and the artificial intelligence community that engage in natural language processing. Results The current version of the DAO comprises 315 classes, 31 relationships, and 814 instances among the classes. The ontology is flexible and can easily accommodate new concepts. The integration of the ontology with machine learning algorithms dramatically decreased the false alarm rate by adding external knowledge to the machine learning process. The ontology is recurrently updated to capture evolving concepts in different contexts and applied to analyze data related to social media and dark web marketplaces. Conclusions The DAO provides a powerful framework and a useful resource that can be expanded and adapted to a wide range of substance use and mental health domains to help advance big data analytics of web-based data for substance use epidemiology research.

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Section: Discussionmentioning

confidence: 99%

Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study

Lokala¹,

Lamy²,

Daniulaityte³

et al. 2022

JMIR Public Health Surveill

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“…Also, a tool named Karma was developed to do the mapping automatically and it also had the capability of visualizing the links it made so that the authorities could check the accuracy of linking to LOD sources. PerfectO [128] is a Knowledge Directory Services tool that concentrates on the best techniques in an ontology. As an example of how PerfectO can be used beyond the semantic web domain, it is extended to the Internet of Things (IoT) domain, improving ontologies from the Linked Open vocabularies for IoT (LOV4IoT) ontology catalog.…”

Section: ) Linked (Open) Datamentioning

confidence: 99%

Cultural Heritage Information Retrieval: Past, Present, and Future Trends

Ranjgar,

Sadeghi-Niaraki,

Shakeri

et al. 2024

IEEE Access

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Knowledge organization and development of better information retrieval techniques were of great importance from a very early time period in human history. The need has grown high for such systems with the advent of digitization and the web era. Computer systems and web have offered easier retrieval of information in almost no time. However, as the amount of data increased, these systems were not able to work well in terms of accuracy and precision of retrieval. Semantic Web concept was introduced to overcome the issue by converting the web of documents to a web of data. Semantic Web technologies makes data machine-understandable so that information retrieval can be more precise and accurate. The Cultural Heritage (CH) community, with the goal of preserving and dissemination of the historical information to people and society, is one of the first domains to adopt Semantic Web recommendations and technologies, which can provide interoperability between various organizations by creating a shared understanding in the community. The data in the CH domain differs widely with types and formats. Also, a lot of organizations and experts from various fields interact through different processes within this community. Due to the mentioned needs, the CH community employed Semantic Web technologies step by step along its evolution process for better knowledge management and a uniform understanding among the community. In this study, we present a comprehensive conceptual framework that spans cultural heritage, information modeling, and information retrieval. Our model addresses early solutions in knowledge organization systems, highlighting the evolution from classification systems and controlled vocabularies to the significance of metadata schemas. We delve into the limitations of traditional knowledge organization systems and the necessity of formal ontologies, particularly in the cultural heritage domain. The comparative analysis of CRM vs. EDM, ontology-based metadata interoperability, and ontology technologies elucidate our contributions to the field. This paper outlines the process from the initial steps of adopting Semantic Web technologies in the CH domain to the latest developments in CH information retrieval. In this paper, we also reviewed intelligent applications and services developed in the CH domain after establishing semantic data models and Knowledge Organization Systems. Finally, challenges and possible future research directions are discussed. The findings revealed that GLAMs (Galleries, Libraries, Archives, and Museums) are excellent and comprehensive sources of CH information. The CH community has put in a lot of time and effort to develop data models and knowledge organization tools; now it's time to use this valuable resource to construct smart applications that are still in their early phases. This could benefit the CH industry even more.

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“…We employed our designed PerfecO methodology [91] that references a set of semantic web best practices and collection of tools providing web services to evaluate ontologies to enhance our emotional knowledge graph: (1) Syntactic Ontology Evaluation in Section 5.1, (2) ontology design evaluation in Section 5.2, (3) LOV4IoT-Emotion Ontology Catalog statistics in Section 5.3, and (4) the emotional knowledge graph evaluated with emotion scenarios in Section 5.4.…”

Section: Evaluation: Applying Semantic Web Best Practices To Enhance ...mentioning

confidence: 99%

“…Rules are implemented and employed within the emotion scenarios (see Table 6 for demonstrator's URLs such as sensor discovery, rule discovery, emotion ontology, etc.). 7, so researchers will enhance the reusability and better interoperability of their emotion knowledge [91]. Each rule is associated with bad practices and best practice examples to ease the task of beginners in their learning journey (Step-by-step tutorial to improve the ontology quality, dissemination, reuse, etc.…”

Section: Emotional Knowledge Graph Evaluated With Emotion Scenariosmentioning

confidence: 99%

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Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health

Gyrard

Boudaoud

2022

Applied Sciences

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Humans are feeling emotions every day, but they can still encounter difficulties understanding them. To better understand emotions, we integrated interdisciplinary knowledge about emotions from various domains such as neurosciences (e.g., neurobiology), physiology, and psychology (affective sciences, positive psychology, cognitive psychology, psychophysiology, neuropsychology, etc.). To organize the knowledge, we employ technologies such as Artificial Intelligence with Knowledge Graphs and Semantic Reasoning. Furthermore, Internet of Things (IoT) technologies can help to acquire physiological data knowledge. The goal of this paper is to aggregate the interdisciplinary knowledge and implement it within the Emotional Knowledge Graph (EmoKG). The Emotional Knowledge Graph is used within our naturopathy recommender system that suggests food to boost emotion (e.g., chocolate contains magnesium that is recommended when we feel depressed). The recommender system also answers a set of competency questions to easily retrieve emotional related-knowledge from EmoKG, such as what are the basic emotions and the more sophisticated ones, what are the neurotransmitters and hormones related to emotions, etc. To follow FAIR principles, EmoKG is mapped to existing knowledge bases found on the BioPortal biomedical ontology catalog such as SNOMEDCT, FMA, RXNORM, MedDRA, and also from emotion ontologies (when available online). We design the LOV4IoT-Emotion ontology catalog that encourages researchers from heterogeneous communities to apply FAIR principles by releasing online their (emotion) ontologies, datasets, rules, etc. The set of ontology codes shared online can be semi-automatically processed; if not available, the scientific publications describing the emotion ontologies are semi-automatically processed with Natural Language Processing (NLP) technologies. This research is also relevant for other use cases such as European projects (ACCRA for emotional robots to reduce the social isolation of aging people, StandICT for standardization, and AI4EU for Artificial Intelligence) and alliances for IoT such as AIOTI. The recommender system can be extended to address other advice such as aromatherapy and take into consideration medical devices to monitor patients’ vital signals related to emotions and mental health.

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PerfectO: An Online Toolkit for Improving Quality, Accessibility, and Classification of Domain-Based Ontologies

Cited by 5 publications

References 39 publications

Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study

Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study

Cultural Heritage Information Retrieval: Past, Present, and Future Trends

Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health

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