This paper describes one of the specific functionalities of the data.bnf.fr library discovery service: the use of semantic Web technologies to create Web pages around "named entities" from the authority files.
Thanks to the initiative of Linked Open Data, the RDF datasets that are published on the Web are more and more numerous. One active research field currently concerns the problem of finding links between entities. We focus in this paper on ontology-based data linking approaches which use linking rules based on the available schemas (or ontologies). This kind of systems assume to have beforehand a set of mappings between ontology elements. However, this set of mappings could be incomplete. We propose in this paper a data linking approach called N2R-Part. It is based on the computation of similarity scores by exploiting at the same time properties for which a mapping exists and those for which there is no mapping. We illustrate throughout an example how the exploitation of the unmapped properties improves the data linking results.
We deal in this paper with the problem of creating an interactive and visual map for a large collection of Open datasets. We first describe how to define a representation space for such data, using text mining techniques to create features. Then, with a similarity measure between Open datasets, we use the K-nearest neighbors method for building a proximity graph between datasets. We use a force-directed layout method to visualize the graph (Tulip Software). We present the results with a collection of 300,000 datasets from the French Open data web site, in which the display of the graph is limited to 150,000 datasets. We study the discovered clusters and we show how they can be used to browse this large collection.
Platforms for publication and collaborative management of data, such as Data.gov or Google Fusion Tables, are a new trend on the web. They manage very large corpora of datasets, but often lack an integrated schema, ontology, or even just common publication standards. This results in inconsistent names for attributes of the same meaning, which constrains the discovery of relationships between datasets as well as their reusability. Existing data integration techniques focus on reuse-time, i.e., they are applied when a user wants to combine a specific set of datasets or integrate them with an existing database. In contrast, this paper investigates a novel method of data integration at publish-time, where the publisher is provided with suggestions on how to integrate the new dataset with the corpus as a whole, without resorting to a manually created mediated schema or ontology for the platform. We propose data-driven algorithms that propose alternative attribute names for a newly published dataset based on attribute- and instance statistics maintained on the corpus. We evaluate the proposed algorithms using real-world corpora based on the Open Data Platform opendata.socrata.com and relational data extracted from Wikipedia. We report on the system's response time, and on the results of an extensive crowdsourcing-based evaluation of the quality of the generated attribute names alternatives.
KD2R allows the automatic discovery of composite key constraints in RDF data sources that conform to a given ontology. We consider data sources for which the Unique Name Assumption is fulfilled. KD2R allows this discovery without having to scan all the data. Indeed, the proposed system looks for maximal non keys and derives minimal keys from this set of non keys. KD2R has been tested on several datasets available on the web of data and it has obtained promising results when the discovered keys are used to link data. In the demo, we will demonstrate the functionality of our tool and we will show on several datasets that the keys can be used in a datalinking tool.
The need to better integrate and link various isolated data sources on the web has been widely recognized and is tackled by the Linked Open Data (LOD) initiative. One of the problems to address is the issue of publishing and subsequently exploiting the data as LOD, due to reasons of data size and performance of the respective queries and to the publication complexity. This work addresses the size and performance issues by adapting the cloud as a hosting platform for LOD publication services so as to exploit its scalability and elasticity capabilities. The publication complexity issue is addressed by proposing a Linked Open Data-as-a-Service approach offering an integrated service based API for (semi)automatic publication of relational data as LOD and subsequent querying and updating capabilities.
Working with open data sources can yield high value information but raises major problems in terms of metadata extraction, data source integration and visualization. In this paper we describe a demonstration of WebSmatch, a flexible environment for Web data integration, based on a real, end-to-end data integration scenario over public data from Data Publica. The demonstration focuses on poorly structured input data sources (XLS files).
In this paper we present a case study on publishing statistical data as Linked Open Data. Statistical or fact-based data are maintained by statistical agencies and organizations, harvested via surveys or aggregated from other sources and mainly concern to observations of socioeconomic indicators. In this case study, we present the publishing as LOD of the preliminary results of Greece's resident population census, conducted in 2011. We have employed the Data Cube vocabulary and the Google Refine tool for modelling and publishing the census results.
Points of interest (POIs) in a city are specific locations that present some significance to people; examples include restaurants, museums, hotels, theatres and landmarks, just to name a few. Due to their role in our social and economic life, POIs have been increasingly gaining the attention of location-based applications, such as online maps and social networking sites. While it is relatively easy to find on the Web basic information about a POI, such as its geographic location, telephone number and opening hours, it is more challenging to have a deeper knowledge as to what other people say about it. What if a person wants to know all the restaurants in Paris that serve good seafood and provide a kind service? Typically, the answer to this question has to be looked for on websites that let people leave comments and opinions on POIs, a time-consuming manual task that few are willing to do. This search would be better supported by search engines if information mined from opinions were available in a structured form, such as RDF. In this position paper, we describe a general approach to enrich an existing RDF repository about POIs with data obtained from social networking sites.
In this paper, we describe the development of the first ontology module for observation of pest attacks in crop production. We applied the NeOn methodology and more particularly the ontology engineering method based on Ontology Design Pattern.
Without Linked Data, transport data is limited to applications exclusively around transport. In this paper, we present a workflow for publishing and linking transport data on the Web. So we will be able to develop transport applications and to add other features which will be created from other datasets. This will be possible because transport data will be linked to these datasets. We apply this workflow to two datasets: NEPTUNE, a French standard describing a transport line, and Passim, a directory containing relevant information on transport services, in every French city.
Integrating open data sources can yield high value information but raises major problems in terms of metadata extraction, data source integration and visualization of integrated data. In this paper, we describe WebSmatch, a flexible environment for Web data integration, based on a real, end-to-end data integration scenario over public data from Data Publica. WebSmatch supports the full process of importing, refining and integrating data sources and uses third party tools for high quality visualization. We use a typical scenario of public data integration which involves problems not solved by currents tools: poorly structured input data sources (XLS files) and rich visualization of integrated data.
This paper describes a system to support the visual exploration of Open Data. During his/her interactive experience with the graphics, the user can easily store the current complete state of the visualization application (called a viewpoint). Next, he/she can compose sequences of these viewpoints (called scenarios) that can easily be reloaded. This feature allows to keep traces of a former exploration process, which can be useful in single user (to support investigation carried out in multiple sessions) as well as in collaborative setting (to share points of interest identified in the data set).
With today's public data sets containing billions of data items, more and more companies are looking to integrate external data with their traditional enterprise data to improve business intelligence analysis. These distributed data sources however exhibit heterogeneous data formats and terminologies and may contain noisy data. In this paper, we present RUBIX, a novel framework that enables business users to semi-automatically perform data integration on potentially noisy tabular data. This framework offers an extension to Google Refine with novel schema matching algorithms leveraging Freebase rich types. First experiments show that using Linked Data to map cell values with instances and column headers with types improves significantly the quality of the matching results and therefore should lead to more informed decisions.
This paper presents our Linked Open Data (LOD) infrastructures for genomic and experimental data related to microRNA biomolecules. Legacy data from two well-known microRNA databases with experimental data and observations, as well as change and version information about microRNA entities, are fused and exported as LOD. Our LOD server assists biologists to explore biological entities and their evolution, and provides a SPARQL endpoint for applications and services to query historical miRNA data and track changes, their causes and effects.
The Linked Data Paradigm is one of the most promising technologies for publishing, sharing, and connecting data on the Web, and offers a new way for data integration and interoperability. However, the proliferation of distributed, inter-connected sources of information and services on the Web poses significant new challenges for managing consistently a huge number of large datasets and their interdependencies. In this paper we focus on the key problem of preserving evolving structured interlinked data. We argue that a number of issues that hinder applications and users are related to the temporal aspect that is intrinsic in linked data. We present a number of real use cases to motivate our approach, we discuss the problems that occur, and propose a direction for a solution.
Open data platforms such as data.gov or opendata.socrata. com provide a huge amount of valuable information. Their free-for-all nature, the lack of publishing standards and the multitude of domains and authors represented on these platforms lead to new integration and standardization problems. At the same time, crowd-based data integration techniques are emerging as new way of dealing with these problems. However, these methods still require input in form of specific questions or tasks that can be passed to the crowd. This paper discusses integration problems on Open Data Platforms, and proposes a method for identifying and ranking integration hypotheses in this context. We will evaluate our findings by conducting a comprehensive evaluation using on one of the largest Open Data platforms.
OpenData movement around the globe is demanding more access to information which lies locked in public or private servers. As recently reported by a McKinsey publication, this data has significant economic value, yet its release has potential to blatantly conflict with people privacy. Recent UK government inquires have shown concern from various parties about publication of anonymized databases, as there is concrete possibility of user identification by means of linkage attacks. Differential privacy stands out as a model that provides strong formal guarantees about the anonymity of the participants in a sanitized database. Only recent results demonstrated its applicability on real-life datasets, though. This paper covers such breakthrough discoveries, by reviewing applications of differential privacy for non-interactive publication of anonymized real-life datasets. Theory, utility and a data-aware comparison are discussed on a variety of principles and concrete applications.
