Miel Vander Sande

Opportunistic Linked Data Querying Through Approximate Membership Metadata

Between urii¾?dereferencing and the sparql protocol lies ai¾?largely unexplored axis of possible interfaces to Linked Data, each with its own combination of trade-offs. One of these interfaces is Triple Pattern Fragments, which allows clients to execute sparql queries against low-cost servers, at the cost of higher bandwidth. Increasing a clients efficiency means lowering the number of requests, which can among others be achieved through additional metadata in responses. We noted that typical sparql query evaluations against Triple Pattern Fragments require ai¾?significant portion of membership subqueries, which check the presence of ai¾?specific triple, rather than ai¾?variable pattern. This paper studies the impact of providing approximate membership functions, i.e., Bloom filters and Golomb-coded sets, as extra metadata. In addition to reducing http requests, such functions allow to achieve full result recall earlier when temporarily allowing lower precision. Half of the tested queries from ai¾?WatDiv benchmark test set could be executed with up to ai¾?third fewer http requests with only marginally higher server cost. Query times, however, did not improve, likely due to slower metadata generation and transfer. This indicates that approximate membership functions can partly improve the client-side query process with minimal impact on the server and its interface.

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.

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.

A digital-first authoring environment for enriched e-books using EPUB 3

The overall majority of books are currently being made with primarily a printed outcome in mind. To make a digital version of these books, most manuscripts need to be re-processed, which usually results in customary built e-books. This need for a customized authoring workflow for every electronic version of a book makes it impossible to build e-books in a cost-effective way. In this paper, we propose a novel workflow that incorporates both print and digital book authoring. By charting the currently most widespread workflow Flemish publishers use to author print books and e-books, we are able to identify the most pressing problems. These are the print-first approach, the vendor lock-in situation of the e-reader market, and the high cost of updating and/or maintaining the content of an (e-)book. To overcome the aforementioned problems, we devise a new workflow that follows a digital-first approach using Open Web standards, separating content, structure, and layout. We evaluate the proposed workflow by building a proof-of-concept authoring environment. Using this new workflow, both digital and print books can be built without significant additional costs. The proof of concept is evaluated using an experts group of Flemish publishers, and received general positive reception, with concerns on how to incorporate the proposed workflow into production environments. By not limiting the proof of concept to a fixed data model, it could handle content from more content providers, facilitating further research into the possibilities and future requirements of the EPUB 3 specification.

Quantifying the Interoperability of Open Government Datasets

A proposed technique quantifies the semantic interoperability of open government datasets with three metrics calculated using a set of statements that indicate for each pair of identifiers in the system whether or not they represent the same concept.

Hypermedia-Based Discovery for Source Selection Using Low-Cost Linked Data Interfaces

Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed—even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness.

OSTRICH : versioned random-access triple store

The Linked Open Data cloud is evergrowing and many datasets are frequently being updated. In order to fully exploit the potential of the information that is available in and over historical dataset versions, such as discovering evolution of taxonomies or diseases in biomedical datasets, we need to be able to store and query the different versions of Linked Datasets efficiently. In this demonstration, we introduce OSTRICH, which is an efficient triple store with supported for versioned query evaluation. We demonstrate the capabilities of OSTRICH using a Web-based graphical user interface in which a store can be opened or created. Using this interface, the user is able to query in, between, and over different versions, ingest new versions, and retrieve summarizing statistics.

Toward sustainable publishing and querying of distributed Linked Data archives

Purpose The purpose of this paper is to detail a low-cost, low-maintenance publishing strategy aimed at unlocking the value of Linked Data collections held by libraries, archives and museums (LAMs). Design/methodology/approach The shortcomings of commonly used Linked Data publishing approaches are identified, and the current lack of substantial collections of Linked Data exposed by LAMs is considered. To improve on the discussed status quo, a novel approach for publishing Linked Data is proposed and demonstrated by means of an archive of DBpedia versions, which is queried in combination with other Linked Data sources. Findings The authors show that the approach makes publishing Linked Data archives easy and affordable, and supports distributed querying without causing untenable load on the Linked Data sources. Research limitations/implications The proposed approach significantly lowers the barrier for publishing, maintaining, and making Linked Data collections queryable. As such, it offers the potential to substantially grow the distributed network of queryable Linked Data sources. Because the approach supports querying without causing unacceptable load on the sources, the queryable interfaces are expected to be more reliable, allowing them to become integral building blocks of robust applications that leverage distributed Linked Data sources. Originality/value The novel publishing strategy significantly lowers the technical and financial barriers that LAMs face when attempting to publish Linked Data collections. The proposed approach yields Linked Data sources that can reliably be queried, paving the way for applications that leverage distributed Linked Data sources through federated querying.

Supporting Sustainable Publishing and Consuming of Live Linked Time Series Streams

The road to publishing public streaming data on the Web is paved with trade-offs that determine its viability. The cost of unrestricted query answering on top of data streams, may not be affordable for all data publishers. Therefore, public streams need to be funded in a sustainable fashion to remain online. In this paper we present an overview of possible query answering features for live time series in the form of multidimensional interfaces. For example, from a live parking availability data stream, pre-calculated time constrained statistical indicators or geographically classified data can be provided to clients on demand. Furthermore, we demonstrate the initial developments of a Linked Time Series server that supports such features through an extensible modular architecture. Benchmarking the costs associated to each of these features allows to weigh the trade-offs inherent to publishing live time series and establishes the foundations to create a decentralized and sustainable ecosystem for live data streams on the Web.

Updating SPARQL results in real-time with client-side fragment patching

A lot of Linked Data on the Web is dynamic. Despite the existing query solutions and implementations, crucial unresolved issues remain. This poster presents a novel approach to update sparql results client-side by exchanging fragment patches. We aim at a sustainable solution which balances the load and reduces bandwidth. Therefore, our approach benefits from reusing unchanged data and minimizing data transfer size. By only working with patches, the load on the server is minimal. Also, the bandwidth usage is low, since only relevant changes are transferred to the client.