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Building Web APIs on top of SPARQL endpoints is becoming a common practice to enable universal access to the integration favorable dataspace of Linked Data. However, the Linked Data community cannot expect users to learn SPARQL to query this dataspace, and Web APIs are the most common way of enabling programmatic access to data on the Web. However, the implementation of Web APIs around Linked Data is often a tedious and repetitive process. Recent work speeds up this Linked Data API construction by wrapping it around SPARQL queries, which carry out the API functionality under the hood. Inspired by this, in this paper we present grlc, a lightweight server that translates SPARQL queries curated in GitHub repositories to Linked Data APIs on the fly.

Ad-hoc querying is crucial to access information from Linked Data, yet publishing queryable RDF datasets on the Web is not a trivial exercise. The most compelling argument to support this claim is that the Web contains hundreds of thousands of data documents, while only 260 queryable SPARQL endpoints are provided. Even worse, the SPARQL endpoints we do have are often unstable, may not comply with the standards, and may differ in supported features. In other words, hosting data online is easy, but publishing Linked Data via a queryable API such as SPARQL appears to be too difficult. As a consequence, in practice, there is no single uniform way to query the LOD Cloud today. In this paper, we therefore combine a large-scale Linked Data publication project LOD Laundromat with a low-cost server-side interface Triple Pattern Fragments, in order to bridge the gap between the Web of downloadable data documents and the Web of live queryable data. The result is ai¾?repeatable, low-cost, open-source data publication process. To demonstrate its applicability, we made over 650,000 data documents available as datai¾?APIs, consisting of 30i¾?billion i¾?triples.

The current state-of-the-art Entity Linking (EL) systems are geared towards corpora that are as heterogeneous as the Web, and therefore perform sub-optimally on domain-specific corpora. A key open problem is how to construct effective EL systems for specific domains, as knowledge of the local context should in principle increase, rather than decrease, effectiveness. In this paper we propose the hybrid use of simple specialist linkers in combination with an existing generalist system to address this problem. Our main findings are the following. First, we construct a new reusable benchmark for EL on a corpus of domain-specific conversations. Second, we test the performance of a range of approaches under the same conditions, and show that specialist linkers obtain high precision in isolation, and high recall when combined with generalist linkers. Hence, we can effectively exploit local context and get the best of both worlds.

In this paper we present LocLinkVis (Locate-Link-Visualize); a system which supports exploratory information access to a document collection based on geo-referencing and visualization. It uses a gazetteer which contains representations of places ranging from countries to buildings, and that is used to recognize toponyms, disambiguate them into places, and to visualize the resulting spatial footprints.

The report presents the work carried out over seven months in 2015, during which our group has: - inventoried relevant semantic enrichment work in the Europeana Network in the past years. We have tried to do this in a way that we hope will facilitate identification and use of services relevant to the application that needs them. This includes general considerations on the diversity of the processes at hand and a focus on interoperability issues. - further developed criteria to select datasets for semantic enrichment and illustrate with a thorough analysis of selected examples - explored methodological issues for the evaluation of semantic enrichment services - performed a quantitative and qualitative evaluation of seven enrichment services on a same subset of the Europeana dataset, containing 17.300 records. This report, together with its two companion documents and a data space on the Europeana Assembla, presents all this. We have also included, as a conclusion, a number of lessons learned we think should be considered for the design and enhancement of enrichment services as well as for their evaluation.

International audience; Video has become a first class citizen on the Web with broad support in all common Web browsers. Where with struc- tured mark-up on webpages we have made the vision of the Web of Data a reality, in this paper, we propose a new vi- sion that we name the Web(VTT) of Data, alongside with concrete steps to realize this vision. It is based on the evolving standards WebVTT for adding timed text tracks to videos and JSON-LD, a JSON-based format to serial- ize Linked Data. Just like the Web of Data that is based on the relationships among structured data, the Web(VTT) of Data is based on relationships among videos based on WebVTT files, which we use as Web-native spatiotemporal Linked Data containers with JSON-LD payloads. In a first step, we provide necessary background information on the technologies we use. In a second step, we perform a large- scale analysis of the 148 terabyte size Common Crawl corpus in order to get a better understanding of the status quo of Web video deployment and address the challenge of integrat- ing the detected videos in the Common Crawl corpus into the Web(VTT) of Data. In a third step, we open-source an online video annotation creation and consumption tool, targeted at videos not contained in the Common Crawl cor- pus and for integrating future video creations, allowing for weaving the Web(VTT) of Data tighter, video by video.

This paper introduces YASGUI, a user-friendly SPARQL client. We compare YASGUI with other SPARQL clients, and show the added value and ease of integrating Web APIs, services, and new technologies such as HTML5. Finally, we discuss some of the challenges we encountered in using these technologies for a building robust and feature rich web application.

Using Linked Data based approaches, public transport companies are able to share their time tables and its updates in an affordable way while allowing user agents to perform multimodal route planning algorithms. Providing time table updates, usually published as data streams, means that data is being constantly modified and in the presence of large analytical queries, results might be affected due to the changing data. In this demo we introduce a mechanism to tackle this problem by guaranteeing that a user agent will always receive version based responses, therefore ensuring data consistency. Such mechanism also enables access to historical data that could be used for deep analysis of transport systems. However, how this data shall be archived, in order to keep this approach scalable and inexpensive is still a matter of study. In a demonstrator, we published and query data from the Belgium national train system (SNCB) and Madrid Regional Transport Consortium (CRTM). This paper represents the first step towards establishing an affordable framework to publish reliable transport data.

Stream Reasoning, and more particularly RDF Stream Processing (RSP), has focused on processing data streams in a timely manner, while expressive reasoning techniques, such as OWL2 DL, allow to fully model and interpret their domain knowledge. However, expressive reasoning techniques have thus far mostly focused on static data, as it tends to become slow with growing datasets. Expressive Stream Reasoning aims to combine these fields and evaluate expressive reasoning techniques in a timely manner over volatile data streams through various reasoning optimizations. Both expressive reasoning and RSP have benchmarks and frameworks for evaluating and comparing proposed solutions. However, no benchmarks or evaluation frameworks for Expressive Stream Reasoning are currently available. In this paper, we propose OWL2Streams, a resource framework for the evaluation of Expressive Stream Reasoning solutions. We identified challenges and opportunities for optimizations when dealing with expressive reasoning over the combination of streams and static data. OWL2Streams proposes three streaming scenarios, each tackling different challenges.

This paper describes MantisTable, an open source Semantic Table Interpretation tool, which automatically annotates tables using a Knowledge Graph. MantisTable provides a graphical interface allowing users to analyse the results of the semantic table interpretation process and validate the final annotations. The tool also provides a guided mode for viewing and editing annotations by users. Thanks to MantisTable features, it is possible to create semantic annotations and favour the publication and exchange of tabular data.

Nowadays, a website is used to disseminate information about an event (e.g., location, dates, time). In the academic world, it is common to develop a website for an event, such as workshops or conferences. Aligning with the “Web of data”, its dissemination should also happen by publishing the information of the event as a knowledge graph, e.g., via RDF that is available through a SPARQL endpoint or a Triple Patterns Fragment server. However, the RDF generation and website development is not always straightforward and can be time-consuming. In this demo, we present the Semantic Academic-event Dissemination (SAD) Generator for generating RDF and websites for academic events. The generator allows to (i) annotate CSV files that contain academic event data and use the annotations to generate a knowledge graph and (ii) generate a website with the information for the event querying the knowledge graph. We used our generator to generate the RDF and website of a real workshop, the KGB workshop. It can be easily reused by organizers of other academic events by simply providing the event’s information in CSV files.

In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, we (i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, (ii) describe an approach for integrating entities and relationships generated by these tools, and (iii) analyse an automatically generated Knowledge Graph including 10,425 entities and 25,655 relationships in the field of Semantic Web.

This paper introduces YASGUI, a user-friendly SPARQLclient. We compare YASGUI with other SPARQL clients, and show theadded value and ease of integrating Web APIs, services, and new tech-nologies such as HTML5. Finally, we discuss some of the challenges weencountered in using these technologies for a building robust and featurerich web application