An Approach to Publish Spatial Data on the Web: The GeoLinked Data Case

In this paper we report on an ongoing process aimed at publishing hydrographical data on the Web with a Spanish GeoLinked Data Use Case. Moreover, we discuss the process we followed, and propose methodological guidelines for all the activities involved within the process

Rivière or Fleuve? Modelling Multilinguality in the Hydrographical

The need for interoperability among geospatial resources in different natural languages evidences the difficulties to cope with domain representations highly dependent of the culture in which they have been conceived. In this paper we characterize the problem of representing cultural discrepancies in ontologies. We argue that such differences can be accounted for at the ontology terminological layer by means of external elaborated models of linguistic information associated to ontologies. With the aim of showing how external models can cater for cultural discrepancies, we compare two versions of an ontology of the hydrographical domain: hydrOntology. The first version makes use of the labeling system supported by RDF(S) and OWL to include multilingual linguistic information in the ontology. The second version relies on the Linguistic Information Repository model (LIR) to associate structured multilingual information to ontology concepts. In this paper we propose an extension to the LIR to better capture linguistic and cultural specificities within and across language

Using a hybrid approach for the development of an ontology in the hydrographical domain

This work presents a hybrid approach for domain ontology development, which merges top-down and bottom-up techniques. In the top-down approach the concepts in the ontology are derived from an analysis and study of relevant information sources about the domain (e.g., hydrographic features). In the bottom-up approach the concepts in the ontology are the result of applying formal methods on a analysis of the data instances on the repositories (e.g., repositories containing hydrographical features)

Is it possible to enrich ontologies with a specialized domain linguistic resource?

Enriching ontologies with linguistic resources is considered an important target in natural language applications. These linguistic resources should contain not only linguistic but knowledge information. However the linguistic resources available, such as WordNet, are built around lexical relations such as synonymy, antonym, hyponymy, etc. and they do not provide enough information for ontology building. On the other hand, ontologies building requires deeper and more accurate knowledge than general vocabulary contains and, consequently, demands specialized domain resources. This paper presents a linguistic resource developed for Spanish, that has been built followingsome Meaning-Text Theory principles, in order to contain as much possible knowledge related to several specialized domains

Geographical Linked Data: a Spanish Use Case

We present the process that has been followed for the development of an application that makes use of several heterogeneous Spanish public datasets that are related to administrative, hydrographic, and statistical domains. Our application aims at analysing existing relations between the Spanish coastal area and dierent statistical variables such as unemployment, population, dwelling, industry, and building trade. Moreover, we provide an important innovation with respect to other similar processes followed in other initiatives by dealing with the geometrical information of features

Knowledge formalization for vector data matching using belief theory

Nowadays geographic vector data is produced both by public and private institutions using well defined specifications or crowdsourcing via Web 2.0 mapping portals. As a result, multiple representations of the same real world objects exist, without any links between these different representations. This becomes an issue when integration, updates, or multi-level analysis needs to be performed, as well as for data quality assessment. In this paper a multi-criteria data matching approach allowing the automatic definition of links between identical features is proposed. The originality of the approach is that the process is guided by an explicit representation and fusion of knowledge from various sources. Moreover the imperfection (imprecision, uncertainty, and incompleteness) is explicitly modeled in the process. Belief theory is used to represent and fuse knowledge from different sources, to model imperfection, and make a decision. Experiments are reported on real data coming from different producers, having different scales and either representing relief (isolated points) or road networks (linear data)

An analysis of existing production frameworks for statistical and geographic information: Synergies, gaps and integration

The production of official statistical and geospatial data is often in the hands of highly specialized public agencies that have traditionally followed their own paths and established their own production frameworks. In this article, we present the main frameworks of these two areas and focus on the possibility and need to achieve a better integration between them through the interoperability of systems, processes, and data. The statistical area is well led and has well-defined frameworks. The geospatial area does not have clear leadership and the large number of standards establish a framework that is not always obvious. On the other hand, the lack of a general and common legal framework is also highlighted. Additionally, three examples are offered: the first is the application of the spatial data quality model to the case of statistical data, the second of the application of the statistical process model to the geospatial case, and the third is the use of linked geospatial and statistical data. These examples demonstrate the possibility of transferring experiences/advances from one area to another. In this way, we emphasize the conceptual proximity of these two areas, highlighting synergies, gaps, and potential integration. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

ACLRO: An Ontology for the Best Practice in ACLR Rehabilitation

Indiana University-Purdue University Indianapolis (IUPUI)With the rise of big data and the demands for leveraging artificial intelligence (AI), healthcare requires more knowledge sharing that offers machine-readable semantic formalization. Even though some applications allow shared data interoperability, they still lack formal machine-readable semantics in ICD9/10 and LOINC. With ontology, the further ability to represent the shared conceptualizations is possible, similar to SNOMED-CT. Nevertheless, SNOMED-CT mainly focuses on electronic health record (EHR) documenting and evidence-based practice. Moreover, due to its independence on data quality, the ontology enhances advanced AI technologies, such as machine learning (ML), by providing a reusable knowledge framework. Developing a machine-readable and sharable semantic knowledge model incorporating external evidence and individual practice’s values will create a new revolution for best practice medicine. The purpose of this research is to implement a sharable ontology for the best practice in healthcare, with anterior cruciate ligament reconstruction (ACLR) as a case study. The ontology represents knowledge derived from both evidence-based practice (EBP) and practice-based evidence (PBE). First, the study presents how the domain-specific knowledge model is built using a combination of Toronto Virtual Enterprise (TOVE) and a bottom-up approach. Then, I propose a top-down approach using Open Biological and Biomedical Ontology (OBO) Foundry ontologies that adheres to the Basic Formal Ontology (BFO)’s framework. In this step, the EBP, PBE, and statistic ontologies are developed independently. Next, the study integrates these individual ontologies into the final ACLR Ontology (ACLRO) as a more meaningful model that endorses the reusability and the ease of the model-expansion process since the classes can grow independently from one another. Finally, the study employs a use case and DL queries for model validation. The study's innovation is to present the ontology implementation for best-practice medicine and demonstrate how it can be applied to a real-world setup with semantic information. The ACLRO simultaneously emphasizes knowledge representation in health-intervention, statistics, research design, and external research evidence, while constructing the classes of data-driven and patient-focus processes that allow knowledge sharing explicit of technology. Additionally, the model synthesizes multiple related ontologies, which leads to the successful application of best-practice medicine