A Pattern-Based Method for Re-Engineering Non-Ontological Resources into Ontologies

To speed up the ontology developement process, ontology developers are reusing all available ontological and non-ontological resources, such as classification schemes, thesauris, lexicons, and so forth, that have already reached some consensus. ..

SKOS Sources Transformations for Ontology Engineering: Agronomical Taxonomy Use Case

Sources like thesauri or taxonomies are already used as input in ontology development process. Some of them are also published on the LOD using the SKOS format. Reusing this type of sources to build an ontology is not an easy task. The ontology developer has to face different syntax and different modelling goals. We propose in this paper a new methodology to transform several non-ontological sources into a single ontology. We take into account: the redundancy of the knowledge extracted from sources in order to discover the consensual knowledge and Ontology Design Patterns (ODPs) to guide the transformation process. We have evaluated our methodology by creating an ontology on wheat taxonomy from three sources: Agrovoc thesaurus, TaxRef taxonomy, NCBI taxonomy

Essentials In Ontology Engineering: Methodologies, Languages, And Tools

In the beginning of the 90s, ontology development was similar to an art: ontology developers did not have clear guidelines on how to build ontologies but only some design criteria to be followed. Work on principles, methods and methodologies, together with supporting technologies and languages, made ontology development become an engineering discipline, the so-called Ontology Engineering. Ontology Engineering refers to the set of activities that concern the ontology development process and the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them. Thanks to the work done in the Ontology Engineering field, the development of ontologies within and between teams has increased and improved, as well as the possibility of reusing ontologies in other developments and in final applications. Currently, ontologies are widely used in (a) Knowledge Engineering, Artificial Intelligence and Computer Science, (b) applications related to knowledge management, natural language processing, e-commerce, intelligent information integration, information retrieval, database design and integration, bio-informatics, education, and (c) the Semantic Web, the Semantic Grid, and the Linked Data initiative. In this paper, we provide an overview of Ontology Engineering, mentioning the most outstanding and used methodologies, languages, and tools for building ontologies. In addition, we include some words on how all these elements can be used in the Linked Data initiative

Transforming Meteorological Data into Linked Data

We describe the AEMET meteorological dataset, which makes available some data sources from the Agencia Estatal de Meteorología (AEMET, Spanish Meteorological Office) as Linked Data. The data selected for publication are generated every ten minutes by approximately 250 automatic weather stations deployed across Spain and made available as CSV files in the AEMET FTP server. These files are retrieved from the server, processed with Python scripts, transformed to RDF according to an ontology network (which reuses the W3C SSN Ontology), published in a triple store and visualized using Map4RDF.This work has been supported by the Spanish project myBigData (TIN2010-17060)

Transforming meteorological data into linked data

This paper describes the process followed in order to make some of the public meterological data from the Agencia Estatal de Meteorología (AEMET, Spanish Meteorological Office) available as Linked Data. The method followed has been already used to publish geographical, statistical, and leisure data. The data selected for publication are generated every ten minutes by the 250 automatic stations that belong to AEMET and that are deployed across Spain. These data are available as spreadsheets in the AEMET data catalog, and contain more than twenty types of measurements per station. Spreadsheets are retrieved from the website, processed with Python scripts, transformed to RDF according to an ontology network about meteorology that reuses the W3C SSN Ontology, published in a triple store and visualized in maps with Map4rdf

Guidelines for multilingual linked data

In this article, we argue that there is a growing number of linked datasets in different natural languages, and that there is a need for guidelines and mechanisms to ensure the quality and organic growth of this emerging multilingual data network. However, we have little knowledge regarding the actual state of this data network, its current practices, and the open challenges that it poses. Questions regarding the distribution of natural languages, the links that are established across data in different languages, or how linguistic features are represented, remain mostly unanswered. Addressing these and other language-related issues can help to identify existing problems, propose new mechanisms and guidelines or adapt the ones in use for publishing linked data including language-related features, and, ultimately, provide metrics to evaluate quality aspects. In this article we review, discuss, and extend current guidelines for publishing linked data by focusing on those methods, techniques and tools that can help RDF publishers to cope with language barriers. Whenever possible, we will illustrate and discuss each of these guidelines, methods, and tools on the basis of practical examples that we have encountered in the publication of the datos.bne.es dataset

The support of constructs in thesaurus tools from a Semantic Web perspective: Framework to assess standard conformance

15 p.Thesauri are conceptual tools useful to achieve semantic interoperability and reusability, which are relevant goals in the Semantic Web. Thesaurus standards establish, among other issues, the constructs that can appear in a thesaurus. The ISO 25964 standard for thesauri, which supersedes ISO 2788, is the evolution of the ISO thesauri standard to a conceptual approach closer to the Semantic Web. However, it appeared when SKOS -the W3C Recommendation- was already consolidated as the standard for KOS (Knowledge Organization System) representation in the Semantic Web, including thesauri. The evolution from ISO 2788 to ISO 25964, and the relationships between constructs in ISO 2788/ISO 25964 and SKOS, are studied in this paper. From the analysis of this comparison, a methodological framework, that focuses on the construct support, is proposed to evaluate the conformance quality of thesaurus management tools. Target readers are professionals in charge of thesauri edition. A Semantic Web perspective is taken to characterize the effect that using SKOS to represent thesauri can have on the results of the assessment. A proof of concept for the model’s feasibility was performed on two tools and the analysis of the results of this experimental validation is presented. The conclusions highlight the model’s suitability for assessing conformance to the standards concerning support for thesaurus constructs

Semantic model for mining e-learning usage with ontology and meaningful learning characteristics

The use of e-learning in higher education institutions is a necessity in the learning process. E-learning accumulates vast amount of usage data which could produce a new knowledge and useful for educators. The demand to gain knowledge from e-learning usage data requires a correct mechanism to extract exact information. Current models for mining e-learning usage have focused on the activities usage but ignored the actions usage. In addition, the models lack the ability to incorporate learning pedagogy, leading to a semantic gap to annotate mining data towards education domain. The other issue raised is the absence of usage recommendation that refers to result of data mining task. This research proposes a semantic model for mining e-learning usage with ontology and meaningful learning characteristics. The model starts by preparing data including activity and action hits. The next step is to calculate meaningful hits which categorized into five namely active, cooperative, constructive, authentic, and intentional. The process continues to apply K-means clustering analysis to group usage data into three clusters. Lastly, the usage data is mapped into ontology and the ontology manager generates the meaningful usage cluster and usage recommendation. The model was experimented with three datasets of distinct courses and evaluated by mapping against the student learning outcomes of the courses. The results showed that there is a positive relationship between meaningful hits and learning outcomes, and there is a positive relationship between meaningful usage cluster and learning outcomes. It can be concluded that the proposed semantic model is valid with 95% of confidence level. This model is capable to mine and gain insight into e-learning usage data and to provide usage recommendation