Pieter Heyvaert

RMLEditor: A Graph-Based Mapping Editor for Linked Data Mappings

Although several tools have been implemented to generate Linked Data from raw data, users still need to be aware of the underlying technologies and Linked Data principles to use them. Mapping languages enable to detach the mapping definitions from the implementation that executes them. However, no thorough research has been conducted on how to facilitate the editing of mappings. We propose the rmleditor, a visual graph-based user interface, which allows users to easily define the mappings that deliver the rdf representation of the corresponding raw data. Neither knowledge of the underlying mapping language nor the used technologies is required. The rmleditor aims to facilitate the editing of mappings, and thereby lowers the barriers to create Linked Data. The rmleditor is developed for use by data specialists who are partners of ii¾źa companies-driven pilot and iii¾źa community group. The current version of the rmleditor was validated: participants indicate that it is adequate for its purpose and the graph-based approach enables users to conceive the linked nature of the data.

Semantically Annotating CEUR-WS Workshop Proceedings with RML

In this paper, we present our solution for the first task of the second Semantic Publishing Challenge. The task requires extracting and semantically annotating information regarding ceur-ws workshops, their chairs and conference affiliations, as well as their papers and their authors, from a set of html-encoded workshop proceedings volumes. Our solution builds on last year’s submission, while we address a number of shortcomings, assess the generated dataset for its quality and publish the queries as sparql query templates. This is accomplished using the rdf Mapping Language (rml) to define the mappings, the rmlprocessor to execute them, the rdfunit to both validate the mapping documents and assess the generated dataset’s quality, and the datatank to publish the sparql query templates. This results in an overall improved quality of the generated dataset that is reflected in the query results.

Specification and Implementation of Mapping Rule Visualization and Editing: MapVOWL and the RMLEditor

Visual tools are implemented to help users in defining how to generate Linked Data from raw data. This is possible thanks to mapping languages which enable detaching mapping rules from the implementation that executes them. However, no thorough research has been conducted so far on how to visualize such mapping rules, especially if they become large and require considering multiple heterogeneous raw data sources and transformed data values. In the past, we proposed the RMLEditor, a visual graph-based user interface, which allows users to easily create mapping rules for generating Linked Data from raw data. In this paper, we build on top of our existing work: we (i) specify a visual notation for graph visualizations used to represent mapping rules, (ii) introduce an approach for manipulating rules when large visualizations emerge, and (iii) propose an approach to uniformly visualize data fraction of raw data sources combined with an interactive interface for uniform data fraction transformations. We perform two additional comparative user studies. The first one compares the use of the visual notation to present mapping rules to the use of a mapping language directly, which reveals that the visual notation is preferred. The second one compares the use of the graph-based RMLEditor for creating mapping rules to the form-based RMLx Visual Editor, which reveals that graph-based visualizations are preferred to create mapping rules through the use of our proposed visual notation and uniform representation of heterogeneous data sources and data values.

Ontology-Based Data Access Mapping Generation Using Data, Schema, Query, and Mapping Knowledge

Ontology-Based Data Access systems provide access to non-rdf data using ontologies. These systems require mappings between the non-rdf data and ontologies to facilitate this access. Manually defining such mappings can become a costly process when dealing with large and complex data sources, and/or multiple data sources at the same time. This resulted in different mapping generation tools. While a number of these tools use knowledge from the original data, existing Linked Data, schemas, and/or mappings, they still fall short when dealing with complex challenges and the user effort can be high. In this paper, we propose an approach, together with an evaluation, that discovers and uses extended knowledge from existing (Linked) Data, schemas, query workload, and mappings, and combines it with knowledge provided by the mapping process to generate a new mapping. Our approach aims to improve the mapping quality, while decreasing the task complexity, and subsequently the user effort.

Data Analysis of Hierarchical Data for RDF Term Identification

Generating Linked Data based on existing data sources requires the modeling of their information structure. This modeling needs the identification of potential entities, their attributes and the relationships between them and among entities. For databases this identification is not required, because a data schema is always available. However, for other data formats, such as hierarchical data, this is not always the case. Therefore, analysis of the data is required to support rdf term and data type identification. We introduce a tool that performs such an analysis on hierarchical data. It implements the algorithms, Daro and S-Daro, proposed in this paper. Based on our evaluation, we conclude that S-Daro offers a more scalable solution regarding run time, with respect to the dataset size, and provides more complete results.

Graph-Based Editing of Linked Data Mappings Using the RMLEditor

Linked Data is in many cases generated from (semi-) structured data. This generation is supported by several tools, a number of which use a mapping language to facilitate the Linked Data generation. However, knowledge of this language and other used technologies is required to use the tools, limiting their adoption by non-Semantic Web experts. We demonstrate the rmleditor: a graphical user interface that utilizes graphs to easily visualize the mappings that deliver the rdf representation of the original data. The required amount of knowledge of the underlying mapping language and the used technologies is kept to a minimum. The rmleditor lowers the barriers to create Linked Data by aiming to also facilitate the editing of mappings by non-experts.

Linked Data-enabled Gamification in EPUB 3 for Educational Digital Textbooks

Interest in eLearning environments is increasing, as well as in digital textbooks and gamification. The advantages of gamification in the context of education have been proven. However, gamified educational material, such as gamified digital textbooks and systems, are scarce. As an answer to the need for such material, the framework GEL (Gamification for EPUB using Linked Data) has been developed. GEL allows to incorporate gamification concepts in a digital textbook, using EPUB 3 and Linked Data. As part of GEL, we created the ontology Gamification Ontology (GO), representing the different gamification concepts, and a JavaScript library. Using GO allows to discover other gamified books, to share gamification concepts between applications and to separate the processing and representation of the gamification concepts.

Declarative Rules for Linked Data Generation at Your Fingertips

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.

Knowledge Representation as Linked Data: Tutorial

The process of extracting, structuring, and organizing knowledge requires processing large and originally heterogeneous data sources. Offering existing data as Linked Data increases its shareability, extensibility, and reusability. However, using Linking Data as a means to represent knowledge can be easier said than done. In this tutorial, we elaborate on how to semantically annotate data, and generate and publish Linked Data. We introduce [R2]RML languages to generate Linked Data. We also show how to easily publish Linked Data on the Web as Triple Pattern Fragments. As a result, participants, independently of their knowledge background, can model, annotate and publish Linked Data on their own.

Modeling, Generating, and Publishing Knowledge as Linked Data

The process of extracting, structuring, and organizing knowledge from one or multiple data sources and preparing it for the Semantic Web requires a dedicated class of systems. They enable processing large and originally heterogeneous data sources and capturing new knowledge. Offering existing data as Linked Data increases its shareability, extensibility, and reusability. However, using Linking Data as a means to represent knowledge can be easier said than done. In this tutorial, we elaborate on the importance of semantically annotating data and how existing technologies facilitate their mapping to Linked Data. We introduce [R2]RML languages to generate Linked Data derived from different heterogeneous data formats –e.g., DBs, XML, or JSON– and from different interfaces –e.g., files or Web apis. Those who are not Semantic Web experts can annotate their data with the RMLEditor, whose user interface hides all underlying Semantic Web technologies to data owners. Last, we show how to easily publish Linked Data on the Web as Triple Pattern Fragments. As a result, participants, independently of their knowledge background, can model, annotate and publish data on their own.