ONTOLOGY-BASED DATA MAPPING TO SUPPORT PLANNING IN HISTORICAL URBAN CENTRES

Because of the need for new sustainable future alternatives, the re-inhabitation of rural areas, hinterlands, small historical urban centres and villages has become a unique real opportunity. Therefore, it is necessary to define and adopt new sustainable urban planning and building permits to follow this path. These processes involve both various actors and disciplines and a variety of spatial and semantic data. For this reason, the present research aims at providing a methodology to build the necessary spatial documentation of historical centres and villages by adopting an ontology-based workflow. Existing ontologies and conceptualisations have been considered together with classes and rules from city historical core regulations. A case study has been selected considering its available spatial datasets and national data models. The bottom-up approach here adopted aims at validating and enriching a reference ontology previously developed in the domain of historical centre by adding new concepts and relations from selected regulation plans and other existing ontologies and data models. Finally, the obtained ontology is also populated with instances of concepts and relations.

Ontologias bibliográficas e Web Semântica: limitações e propostas de investigação

The literature on bibliographic data and ontologies on the Semantic Webidentifies problems, not in terms of data instances or their publicationin isolated sets, but regarding the ontologies that describe the underlying concepts, impacting on the quality of semantic interoperability and in sharing ontologies between systems.This paper elaborates on the adequacy of conceptual models and the limitations of FRBR -Functional Requirements for Bibliographic Records (IFLA, 1998, 2018)1to the Semantic Web; the absence of a common conceptual framework; the insufficiency of semantic mechanisms; the low and deficient reuse of external vocabularies; and the inadequacy of mapping methodologies being applied.A research project is presented proposing a solution to the semantic problems in sharing ontologies, through the creation of a conceptual reference model and a reference ontology as a high level mechanism for semantic relations and data validation using SHACL -Shapes Constraint Language (KNUBLAUCH e KONTOKOSTAS, 2017).

Reference ontology and database annotation of the COVID-19 Open Research Dataset (CORD-19)

The COVID-19 Open Research Dataset (CORD-19) was released in March 2020 to allow the machine learning and wider research community to develop techniques to answer scientific questions on COVID-19. The dataset consists of a large collection of scientific literature, including over 100,000 full text papers. Annotating training data to normalise variability in biological entities can improve the performance of downstream analysis and interpretation. To facilitate and enhance the use of the CORD-19 data in these applications, in late March 2020 we performed a comprehensive annotation process using named entity recognition tool, TERMite, along with a number of large reference ontologies and vocabularies including domains of genes, proteins, drugs and virus strains. The additional annotation has identified and tagged over 45 million entities within the corpus made up of 62,746 unique biomedical entities. The latest updated version of the annotated data, as well as older versions, is made openly available under GPL-2.0 License for the community to use at: https://github.com/SciBiteLabs/CORD19

Coordinating Coronavirus Research: The COVID-19 Infectious Disease Ontology

The COVID-19 pandemic prompted immense investigation of the SARS-CoV-2 virus. Rapidly, accurately, and easily interpreting generated data is of fundamental concern. Ontologies – structured, controlled, vocabularies – support interoperability, and prevent the development of data silos which undermine interoperability. The Open Biological and Biomedical Ontology Foundry serves to ensure ontologies remain interoperable through adherence by its members to core ontology design principles. For example, the Infectious Disease Ontology (IDO) Core includes terminological content common to investigations of all infectious diseases. Ontologies covering more specific infectious diseases, in turn, extend from IDO Core, such as the Coronavirus Infectious Disease Ontology (CIDO). The growing list of virus specific IDO extensions has motivated construction of a reference ontology covering content common to viral infectious disease investigations: the Virus Infectious Disease Ontology (VIDO). Additionally, the present pandemic has motivated construction of a more specific extension of CIDO, covering terminological content specific to the pandemic: the COVID-19 Infectious Disease Ontology (IDO-COVID-19). We report here the development of VIDO and IDO-COVID-19. More specifically, we examine newly minted terms for each ontology, showcase reuse of terms from existing OBO ontologies, motivate choice-points for ontological decisions based on research from relevant life sciences, apply ontology terms to explicate viral pathogenesis, and discuss the annotating power of virus ontologies for use in machine learning projects.

Semantic Search Enhanced with Rating Scores

This paper presents SemSime, a method based on semantic similarity for searching over a set of digital resources previously annotated by means of concepts from a weighted reference ontology. SemSime is an enhancement of SemSim and, with respect to the latter, it uses a frequency approach for weighting the ontology, and refines both the user request and the digital resources with the addition of rating scores. Such scores are High, Medium, and Low, and in the user request indicate the preferences assigned by the user to each of the concepts representing the searching criteria, whereas in the annotation of the digital resources they represent the levels of quality associated with each concept in describing the resources. The SemSime has been evaluated and the results of the experiment show that it performs better than SemSim and an evolution of it, referred to as S e m S i m R V .

Enhancing User Profile Accuracy for Personalized Web Systems

Personalized Web Applications aim to improve the user's browsing experience by offering customized products and services based on his preferences and needs. A key feature of a successful personalization system is building profiles that accurately express the real interests and needs of each user. In this work, we focus on creating accurate, complete and dynamic profiles by capturing and tracking the users’ browsing activities. Moreover, we implement techniques to increase the accuracy of the retrieved user profiles by collecting more browsing data, identifying the most important concepts and removing irrelevant ones, and the number of levels from the concept hierarchy in the reference ontology that we should use to efficiently represent the users’ reel interests and needs. The result is a complete, dynamic, and accurate user profile that can be used to give users better-personalized browsing experience.