Christophe Guéret

Assessing linked data mappings using network measures

Linked Data is at its core about the setting of links between resources. Links provide enriched semantics, pointers to extra information and enable the merging of data sets. However, as the amount of Linked Data has grown, there has been the need to automate the creation of links and such automated approaches can create low-quality links or unsuitable network structures. In particular, it is difficult to know whether the links introduced improve or diminish the quality of Linked Data. In this paper, we present LINK-QA, an extensible framework that allows for the assessment of Linked Data mappings using network metrics. We test five metrics using this framework on a set of known good and bad links generated by a common mapping system, and show the behaviour of those metrics.

Finding the achilles heel of the web of data: using network analysis for link-recommendation

The Web of Data is increasingly becoming an important infrastructure for such diverse sectors as entertainment, government, ecommerce and science. As a result, the robustness of this Web of Data is now crucial. Prior studies show that the Web of Data is strongly dependent on a small number of central hubs, making it highly vulnerable to single points of failure. In this paper, we present concepts and algorithms to analyse and repair the brittleness of the Web of Data. We apply these on a substantial subset of it, the 2010 Billion Triple Challenge dataset. We first distinguish the physical structure of the Web of Data from its semantic structure. For both of these structures, we then calculate their robustness, taking betweenness centrality as a robustnessmeasure. To the best of our knowledge, this is the first time that such robustness-indicators have been calculated for the Web of Data. Finally, we determine which links should be added to the Web of Data in order to improve its robustness most effectively. We are able to determine such links by interpreting the question as a very large optimisation problem and deploying an evolutionary algorithm to solve this problem. We believe that with this work, we offer an effective method to analyse and improve the most important structure that the Semantic Web community has constructed to date.

RadioMarché: distributed voice- and web-interfaced market information systems under rural conditions

Despite its tremendous success, the World Wide Web is still inaccessible to 4.5 billion people - mainly in developing countries - who lack a proper internet infrastructure, a reliable power supply, and often the ability to read and write. Hence, alternative or complementary technologies are needed to make the Web accessible to all, given the limiting conditions. These technologies must serve a large audience, who then may start contributing to the Web by creating content and services. In this paper we propose RadioMarche, a voice- and web-based market information system aimed at stimulating agricultural trade in Sahel countries. To overcome interfacing and infrastructural issues, RadioMarche has a mobile-voice interface and is easy to deploy. Furthermore, we will show how data from regionally distributed instances of RadioMarche, can be aggregated and exposed using Linked Data approaches, so that new opportunities for product and service innovation in agriculture and other domains can be unleashed.

Evolutionary and Swarm Computing for the Semantic Web

The Semantic Web has become a dynamic and enormous network of typed links between data sets stored on different machines. These data sets are machine readable and unambiguously interpretable, thanks to their underlying standard representation languages. The expressiveness and flexibility of the publication model of Linked Data has led to its widespread adoption and an ever increasing publication of semantically rich data on the Web. This success however has started to create serious problems as the scale and complexity of information outgrows the current methods in use, which are mostly based on database technology, expressive knowledge representation formalism and high-performance computing. We argue that methods from computational intelligence can play an important role in solving these problems. In this paper we introduce and systemically discuss the typical application problems on the Semantic Web and argue that the existing approaches to address their underlying reasoning tasks consistently fail because of the increasing size, dynamicity and complexity of the data. For each of these primitive reasoning tasks we will discuss possible problem solving methods grounded in Evolutionary and Swarm computing, with short descriptions of existing approaches. Finally, we will discuss two case studies in which we successfully applied soft computing methods to two of the main reasoning tasks; an evolutionary approach to querying, and a swarm algorithm for entailment.

CEDAR: The Dutch Historical Censuses as Linked Open Data

In this document we describe the CEDAR dataset, a five-star Linked Open Data representation of the Dutch historical censuses, conducted in the Netherlands once every 10 years from 1795 to 1971. We produce a linked dataset from a digitized sample of 2,288 tables. The dataset contains more than 6.8 million statistical observations about the demography, labour and housing of the Dutch society in the 18th, 19th and 20th centuries. The dataset is modeled using the RDF Data Cube vocabulary for multidimensional data, uses Open Annotation to express rules of data harmonization, and keeps track of the provenance of every single data point and its transformations using PROV. We link these observations to well known standard classification systems in social history, such as the Historical International Standard Classification of Occupations (HISCO) and the Amsterdamse Code (AC), which in turn link to DBpedia and GeoNames. The two main contributions of the dataset are the improvement of data integration and access for historical research, and the emergence of new historical data hubs, like classifications of historical religions and historical house types, in the Linked Open Data cloud.

SemanticXO : connecting the XO with the World's largest information network

The XO, powered by a dedicated graphical environment Sugar, is a low cost, robust and connected laptop suitable for usage in developing countries. The Web of Linked Data is the largest decentralized information network ever crafted so far, containing factual information about millions of “things”. The community project SemanticXO is about connecting the two, providing Sugar with dedicated tools to harness the power of the Web of Linked Data. In this paper, we introduce the project, its current status and the goals.

Longitudinal Queries over Linked Census Data

This paper discusses the use of semantic technologies to increase quality, machine-processability, format translatability and cross-querying of complex tabular datasets. Our interest is to enable longitudinal studies of social processes in the past, and we use the historical Dutch censuses as case-study. Census data is notoriously difficult to compare, aggregate and query in a uniform fashion. We describe an approach to achieve this, discussing results, trade-offs and open problems.

Let's "Downscale" Linked Data

Open data policies and linked data publication are powerful tools for increasing transparency, participatory governance, and accountability. The linked data community proudly emphasizes the economic and societal impact such technology shows. But a closer look proves that the design and deployment of these technologies leave out most of the worlds population. The good news is that it will take small but fundamental changes to bridge this gap. Research agendas should be upated to design systems for small infrastructure, provide multimodal interfaces to data, and account better for locally relevant, contextualized data. Now is the time to act, because most linked data technologies are still in development.

Nature-Inspired Dissemination of Information in P2P Networks

After having first been used as a means to publish content, the Web is now widely used as a social tool for sharing information. It is an easy task to subscribe to a social network, join one of the Web-based communities according to some personal interests and start to share content with all the people who do the same. It is easy once you solve two basic problems: select the network to join (go to hi5, facebook, myspace,…? join all of them?) and find/pick up the right communities (i.e., find a strict label to match non-strict centers of interest). An error of appreciation would result in getting too much of useless/non-relevant information. This chapter provides a study on the dissemination of information within groups of people and aim at answering one question: can we find an effortless way of sharing information on the Web? Ideally, such a solution would require neither the definition of a profile nor the selection of communities to join. Publishing information should also not be the result of an active decision but be performed in an automatic way. A nature-inspired framework is introduced as an answer to this question. This framework features artificial ants taking care of the dissemination of information items within the network. Centers of interest of the users are reflected by artificial pheromones laid down on connections between peers. Another part of the framework uses those pheromone trails to detect shared interests and creates communities.

Knowledge Maps of the UDC: Uses and Use Cases

Insight into the depth and breadth of knowledge for use in and across disciplines is of vital importance. Our knowledge maps are visualizations based on empirical evidence about both collection characteristics and knowledge clusters such as disciplines. We report in this paper on collaborative efforts over several years, combining the resources of the Knowledge Space Lab and the Research and Innovation Group at DANS. In particular, we were interested in the narrative of how knowledge and knowledge systems change over time. Knowledge organization systems are evolving complex systems. Their analysis, both concerning inner structure, evolution over time, and their implementation in information spaces is important to better understand how knowledge is produced and can be navigated through.