Map-On: A web-based editor for visual ontology mapping

Sicilia, Álvaro; Nemirovski, German; Nölle, Andreas

doi:10.3233/sw-160246

Cited by 24 publications

(11 citation statements)

References 13 publications

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“…Each data source has been mapped to the ontology using the R2RML standard mapping language. The mappings have been created with Map-On tool [14]. Transformation of the data sources into RDF.…”

Section: Integration Of Building Data With Semantic Web Technologiesmentioning

confidence: 99%

Enhancing energy performance certificates with energy related data to support decision making for building retrofitting

et al. 2018

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The increasing availability of large-scale repositories of energy performance certificates offers the opportunity to interlink them with other data sources (cadastre, geographical data, weather data, building regulations, catalogues of refurbishment measures) and to derive innovative services that use the integrated data in conjunction with various tools (energy performance simulation, environmental impact). In the ENERSI project, two applications have been developed to make it easier for building owners and planners to take informed decisions to improve building energy performance in their properties and in their municipalities. These applications, named ENERHAT and ENERPAT, are based on the integration of building data from multiple sources and domains (energy performance certificates, cadastre, geographic information, and census), building refurbishment policies and assessment tools. Data integration has been performed using Semantic Web technologies. A user-friendly interface enables end-users of these on-line applications to obtain information about the current building status of properties, the measures which could be undertaken to improve them, the energy savings achieved and their respective costs.

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Section: Integration Of Building Data With Semantic Web Technologiesmentioning

confidence: 99%

Enhancing energy performance certificates with energy related data to support decision making for building retrofitting

et al. 2018

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“…Authors of [10] introduced a collaborative ontology mapping environment Map-On. The Map-On tool supports creating of mappings between a database schema and existing domain ontology.…”

Section: Related Workmentioning

confidence: 99%

Alignment: A Hybrid, Interactive and Collaborative Ontology and Entity Matching Service

et al. 2018

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Ontology matching is an essential problem in the world of Semantic Web and other distributed, open world applications. Heterogeneity occurs as a result of diversity in tools, knowledge, habits, language, interests and usually the level of detail. Automated applications have been developed, implementing diverse aligning techniques and similarity measures, with outstanding performance. However, there are use cases where automated linking fails and there must be involvement of the human factor in order to create, or not create, a link. In this paper we present Alignment, a collaborative, system aided, interactive ontology matching platform. Alignment offers a user-friendly environment for matching two ontologies with the aid of configurable similarity algorithms.

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“…This prevents the users from specifying the desired annotations for the data sources. In the past, graphical editors were proposed, e.g., the RMLEditor [4] and Map-On [7], to enhance the workflow of defining rules. However, next to users who prefer a graphical approach, there are users who desire a text-based approach.…”

Section: Introductionmentioning

confidence: 99%

Declarative Rules for Linked Data Generation at Your Fingertips!

Heyvaert

Meester

Dimou

et al. 2018

Lecture Notes in Computer Science

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Linked Data is often generated based on a set of declarative rules using languages such as R2RML and RML. These languages are built with machine-processability in mind. It is thus not always straightforward for users to define or understand rules written in these languages, preventing them from applying the desired annotations to the data sources. In the past, graphical tools were proposed. However, next to users who prefer a graphical approach, there are users who desire to understand and define rules via a text-based approach. For the latter, we introduce an enhancement to their workflow. Instead of requiring users to manually write machine-processable rules, we propose writing human-friendly rules, and generate machine-processable rules based on those human-friendly rules. At the basis is YARRRML: a human-readable text-based representation for declarative generation rules. We propose a novel browser-based integrated development environment (IDE) called Matey, showcasing the enhanced workflow. In this work, we describe our demo. Users can experience first hand how to generate triples from data in different formats by using YARRRML's representation of the rules. The actual machine-processable rules remain completely hidden when editing. Matey shows that writing human-friendly rules enhances the workflow for a broader range of users. As a result, more desired annotations will be added to the data sources which leads to more desired Linked Data.

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Map-On: A web-based editor for visual ontology mapping

Cited by 24 publications

References 13 publications

Enhancing energy performance certificates with energy related data to support decision making for building retrofitting

Enhancing energy performance certificates with energy related data to support decision making for building retrofitting

Alignment: A Hybrid, Interactive and Collaborative Ontology and Entity Matching Service

Declarative Rules for Linked Data Generation at Your Fingertips!

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