Forthcoming and Online First Articles

International Journal of Metadata, Semantics and Ontologies

International Journal of Metadata, Semantics and Ontologies (IJMSO)

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International Journal of Metadata, Semantics and Ontologies (5 papers in press)

Regular Issues

  • Domain-specific schema discovery from general-purpose knowledge bases   Order a copy of this article
    by Everaldo Costa Neto, Johny Moreira, Luciano Barbosa, Ana Carolina Salgado 
    Abstract: General-purpose knowledge bases (KBs) have been used for various applications. An essential step for leveraging the content of KBs on domain-specific tasks is to discover their schema. In this paper, we propose ANCHOR, an end-to-end pipeline for schema discovery from general-purpose KB in an automated way. ANCHOR identifies a domain of interest based on category mapping from KB. Next, it learns representations of entities in this domain based on the entity-category mappings and uses these representations to identify the entities topics within this domain. Finally, ANCHOR generates a profile for each topic using a strategy based on attributes co-occurrence. We have evaluated ANCHOR on four domains. The results show that: (1) the learned entity representation effectively produces better entity clusters than some traditional and embedding-based baselines; (2) our solution produces a high-quality profile for the discovered topics.
    Keywords: schema discovery; knowledge base; topic identification; entity representation.
    DOI: 10.1504/IJMSO.2023.10060907
  • CIDaTa: an ontology-based framework for international data transfers and GDPR compliance   Order a copy of this article
    by Mohammad Mahmudul Hasan, Marcelo Corrales, George Kousiouris, Dimosthenis Anagnostopoulos 
    Abstract: Cross-border data transfers and their legal aspects have created a daunting landscape for application and service providers, in which rules and regulations need to be constantly monitored and addressed, especially in dynamic scenarios such as cloud brokerage or cloud/edge operations. Even if regulations such as the General Data Protection Regulation (GDPR) have started to mature and be understood by the IT industry, further complexity has been added by relatively recent court rulings (such as the Schrems II decision) that create new challenges for the IT domain. The latter is heavily oriented towards a fully automated operational environment thus the consideration of the legality of data transfer is necessary to comply with current regulations. The aim of this work is to semantically model several concepts surrounding international data transfers based on the current changes and formulate them around a newly defined ontology (CIDaTa). The work exploits 23 existing ontologies, as dictated by the Linked Data paradigm, and introduces 54 links between them.
    Keywords: international data transfers; Schrems II; data privacy; data protection; GDPR; ontology-based framework.
    DOI: 10.1504/IJMSO.2023.10060489
  • Assessing the effectiveness of image recognition tools in metadata identification through semantic and label-based analysis   Order a copy of this article
    by Akara Thammastitkul 
    Abstract: This study evaluates the performance of four image recognition tools (Amazon Rekognition, Clarifai, Imagga, and Google Cloud Vision API) for automatic image metadata. The experiment was conducted on various image categories, including human, animal, plant and flower, view and landscape, vegetable and fruit, food, vehicle, tourist landmark, art and culture, and old book cover and posters. Sematic and label-based analysis were used to evaluate the performance of each tool. Results indicate that each tool performed differently across categories, demonstrating the importance of selecting the appropriate tool for specific tasks. Clarifai was found to perform best for human, animal, and food image tagging, while Amazon Rekognition was best for vegetable-fruit and vehicle images. Imagga performed best for plant and flower, art and culture, and old book cover and posters image recognition, while Google Cloud Vision API performed best for view and landscape and tourist landmark recognition.
    Keywords: image recognition tool; automatic image metadata; evaluation; performance assessment; image category; semantic; label-based.
    DOI: 10.1504/IJMSO.2023.10061676
  • Structuring narrative information via metadata: a case of clinical narratives   Order a copy of this article
    by Udaya Varadarajan, Biswanath Dutta 
    Abstract: The purpose of the current work is to improve and evaluate the existing metadata for clinical narrative information. The metadata developed previously was from studies majorly based on literature for narrative information and from ontology-based models for narrative information. The elements corresponding to the narrative information were from the elements of narration, narrative theories and entities in the ontology-based models. The current research identifies a gap in the metadata schema for the narrative in general and medical narratives in specific. The work developed a metadata framework for medical narrative information. Following the development of this framework, an evaluation by experts was conducted through the Delphi method.
    Keywords: narrative information metadata; narrative ontology; clinical narration; medical narrative metadata.
    DOI: 10.1504/IJMSO.2023.10061854
  • OntoAthena: an ontology for intelligent services in distance education   Order a copy of this article
    by Lidia M. Silva, Jorge Luis Victoria Barbosa, Sandro José Rigo, Andresa Vargas Larentis, Emiliano Monteiro 
    Abstract: This article proposes OntoAthena, an ontology to represent knowledge in the domain of intelligent services for e-learning. The purpose of the proposed ontology is to support educational practices and provide intelligent services, helping managers, teachers and students in the teaching and learning process. OntoAthena was specified through Ontology Development 101 and implemented in Prot
    Keywords: ontologies; smart services; distance learning.
    DOI: 10.1504/IJMSO.2024.10061855