Anastasia Dimou

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.

Assessing and Refining Mappingsto RDF to Improve Dataset Quality

rdf dataset quality assessment is currently performed primarily after data is published. However, there is neither a systematic way to incorporate its results into the dataset nor the assessment into the publishing workflow. Adjustments are manually ---but rarely--- applied. Nevertheless, the root of the violations which often derive from the mappings that specify how the rdf dataset will be generated, is not identified. We suggest an incremental, iterative and uniform validation workflow for rdf datasets stemming originally from semi-structured datai¾?e.g., csv, xml, json. In this work, we focus on assessing and improving their mappings. We incorporate ii¾?a test-driven approach for assessing the mappings instead of the rdf dataset itself, as mappings reflect how the dataset will be formed when generated;i¾?and iii¾?perform semi-automatic mapping refinementsbased on the results of the quality assessment. The proposed workflow is applied to diverse cases, e.g., large, crowdsourced datasets such as dbpedia, or newly generated, such as iLastic. Our evaluation indicates the efficiency of our workflow, as it significantly improves the overall quality of an rdf dataset in the observed cases.

Semantic Publishing Challenge – Assessing the Quality of Scientific Output by Information Extraction and Interlinking

The Semantic Publishing Challenge series aims at investigating novel approaches for improving scholarly publishing using Linked Data technology. In 2014 we had bootstrapped this effort with a focus on extracting information from non-semantic publications - computer science workshop proceedings volumes and their papers - to assess their quality. The objective of this second edition was to improve information extraction but also to interlink the 2014 dataset with related ones in the LOD Cloud, thus paving the way for sophisticated end-user services.

Visualizing the information of a Linked Open Data enabled Research Information System

Abstract The Open Access movement and the research management can take a new turn if the research information is published as Linked Open Data. With Linked Open Data, the management of the research information within institutions and across institutions can be facilitated, the quality of the available data can be improved and their availability to the public is assured. However, it can be difficult for non-expert users to take advantage of the interlinked information offered by Linked Open Data as they lack of in- depth knowledge. In this paper, we present a use case of publishing research metadata as Linked Open Data and creating interactive visualizations to support users in analyzing the Flemish research landscape.

Extraction and Semantic Annotation of Workshop Proceedings in HTML Using RML

Despite the significant number of existing tools, incorporating data into the Linked Open Data cloud remains complicated; hence discouraging data owners to publish their data as Linked Data. Unlocking the semantics of published data, even if they are not provided by the data owners, can contribute to surpass the barriers posed by the low availability of Linked Data and come closer to the realisation of the envisaged Semantic Web. rml, a generic mapping language based on an extension over Open image in new window , the Open image in new window standard for mapping relational databases into rdf, offers a uniform way of defining the mapping rules for data in heterogeneous formats. In this paper, we present how we adjusted our prototype rml Processor, taking advantage of rml’s scalability, to extract and map data of workshop proceedings published in html to the rdf data model for the Semantic Publishing Challenge needs.

Declarative Data Transformations for Linked Data Generation: The Case of DBpedia

Mapping languages allow us to define how Linked Data is generated from raw data, but only if the raw data values can be used as is to form the desired Linked Data. Since complex data transformations remain out of scope for mapping languages, these steps are often implemented as custom solutions, or with systems separate from the mapping process. The former data transformations remain case-specific, often coupled with the mapping, whereas the latter are not reusable across systems. In this paper, we propose an approach where data transformations (i) are defined declaratively and (ii) are aligned with the mapping languages. We employ an alignment of data transformations described using the Function Ontology ( Open image in new window ) and mapping of data to Linked Data described using the rdf Mapping Language (rml). We validate that our approach can map and transform dbpedia in a declaratively defined and aligned way. Our approach is not case-specific: data transformations are independent of their implementation and thus interoperable, while the functions are decoupled and reusable. This allows developers to improve the generation framework, whilst contributors can focus on the actual Linked Data, as there are no more dependencies, neither between the transformations and the generation framework nor their implementations.

An Ontology to Semantically Declare and Describe Functions

Applications built on top of the Semantic Web are emerging as a novel solution in different areas, such as decision making and route planning. However, to connect results of these solutions – i.e., the semantically annotated data – with real-world applications, this semantic data needs to be connected to actionable events. A lot of work has been done (both semantically as non-semantically) to describe and define Web services, but there is still a gap on a more abstract level, i.e., describing interfaces independent of the technology used. In this paper, we present a data model, specification, and ontology to semantically declare and describe functions independently of the used technology. This way, we can declare and use actionable events in semantic applications, without restricting ourselves to programming language-dependent implementations. The ontology allows for extensions, and is proposed as a possible solution for semantic applications in various domains.

Machine-interpretable dataset and service descriptions for heterogeneous data access and retrieval

The rdf data model allows the description of domain-level knowledge that is understandable by both humans and machines. rdf data can be derived from different source formats and diverse access points, ranging from databases or files in csv format to data retrieved from Web apis in json, Web Services in xml or any other speciality formats. To this end, machine-interpretable mapping languages, such as rml, were introduced to uniformly define how data in multiple heterogeneous sources is mapped to the rdf data model, independently of their original format. However, the way in which this data is accessed and retrieved still remains hard-coded, as corresponding descriptions are often not available or not taken into account. In this paper, we introduce an approach that takes advantage of widely-accepted vocabularies, originally used to advertise services or datasets, such as Hydra or dcat, to define how to access Web-based or other data sources. Consequently, the generation of rdf representations is facilitated and further automated, while the machine-interpretable descriptions of the connectivity to the original data remain independent and interoperable, offering a granular solution for accessing and mapping data.

Bringing mathematics to the web of data: the case of the mathematics subject classification

The Mathematics Subject Classification (MSC), maintained by the American Mathematical Societys Mathematical Reviews (MR) and FIZ Karlsruhes Zentralblatt fur Mathematik (Zbl), is a scheme for classifying publications in mathematics. While it is widely used, its traditional, idiosyncratic conceptualization and representation did not encourage wide reuse on the Web, and it made the scheme hard to maintain. We have reimplemented its current version MSC2010 as a Linked Open Dataset using SKOS, and our focus is concentrated on turning it into the new MSC authority. This paper explains the motivation and details of our design considerations and how we realized them in the implementation, presents use cases, and future applications.

Towards Multi-level Provenance Reconstruction of Information Diffusion on Social Media

In order to assess the trustworthiness of information on social media, a consumer needs to understand where this information comes from, and which processes were involved in its creation. The entities, agents and activities involved in the creation of a piece of information are referred to as its provenance, which was standardized by W3C PROV. However, current social media APIs cannot always capture the full lineage of every message, leaving the consumer with incomplete or missing provenance, which is crucial for judging the trust it carries. Therefore in this paper, we propose an approach to reconstruct the provenance of messages on social media on multiple levels. To obtain a fine-grained level of provenance, we use an approach from prior work to reconstruct information cascades with high certainty, and map them to PROV using the PROV-SAID extension for social media. To obtain a coarse-grained level of provenance, we adapt our similarity-based, fuzzy provenance reconstruction approach -- previously applied on news. We illustrate the power of the combination by providing the reconstructed provenance of a limited social media dataset gathered during the 2012 Olympics, for which we were able to reconstruct a significant amount of previously unidentified connections.