Renaud Delbru

Invited paper: Sig.ma: Live views on the Web of Data

We present Sig.ma, both a service and an end user application to access the Web of Data as an integrated information space. Sig.ma uses an holistic approach in which large scale semantic Web indexing, logic reasoning, data aggregation heuristics, ad-hoc ontology consolidation, external services and responsive user interaction all play together to create rich entity descriptions. These consolidated entity descriptions then form the base for embeddable data mashups, machine oriented services as well as data browsing services. Finally, we discuss Sig.mas peculiar characteristics and report on lessons learned and ideas it inspires.

Produce and Consume Linked Data with Drupal

Currently a large number of Web sites are driven by Content Management Systems (CMS) which manage textual and multimedia content but also - inherently - carry valuable information about a sites structure and content model. Exposing this structured information to the Web of Data has so far required considerable expertise in RDF and OWL modelling and additional programming effort. In this paper we tackle one of the most popular CMS: Drupal. We enable site administrators to export their site content model and data to the Web of Data without requiring extensive knowledge on Semantic Web technologies. Our modules create RDFa annotations and --- optionally --- a SPARQL endpoint for any Drupal site out of the box. Likewise, we add the means to map the site data to existing ontologies on the Web with a search interface to find commonly used ontology terms. We also allow a Drupal site administrator to include existing RDF data from remote SPARQL endpoints on the Web in the site. When brought together, these features allow networked RDF Drupal sites that reuse and enrich Linked Data. We finally discuss the adoption of our modules and report on a use case in the biomedical field and the current status of its deployment.

Searching web data: An entity retrieval and high-performance indexing model

More and more (semi) structured information is becoming available on the web in the form of documents embedding metadata (e.g., RDF, RDFa, Microformats and others). There are already hundreds of millions of such documents accessible and their number is growing rapidly. This calls for large scale systems providing effective means of searching and retrieving this semi-structured information with the ultimate goal of making it exploitable by humans and machines alike. This article examines the shift from the traditional web document model to a web data object (entity) model and studies the challenges faced in implementing a scalable and high performance system for searching semi-structured data objects over a large heterogeneous and decentralised infrastructure. Towards this goal, we define an entity retrieval model, develop novel methodologies for supporting this model and show how to achieve a high-performance entity retrieval system. We introduce an indexing methodology for semi-structured data which offers a good compromise between query expressiveness, query processing and index maintenance compared to other approaches. We address high-performance by optimisation of the index data structure using appropriate compression techniques. Finally, we demonstrate that the resulting system can index billions of data objects and provides keyword-based as well as more advanced search interfaces for retrieving relevant data objects in sub-second time. This work has been part of the Sindice search engine project at the Digital Enterprise Research Institute (DERI), NUI Galway. The Sindice system currently maintains more than 200 million pages downloaded from the web and is being used actively by many researchers within and outside of DERI.

ActiveRDF: object-oriented semantic web programming

Object-oriented programming is the current mainstream programming paradigm but existing RDF APIs are mostly triple-oriented. Traditional techniques for bridging a similar gap between relational databases and object-oriented programs cannot be applied directly given the different nature of Semantic Web data, for example in the semantics of class membership, inheritance relations, and object conformance to schemas. We present ActiveRDF, an object-oriented API for managing RDF data that offers full manipulation and querying of RDF data, does not rely on a schema and fully conforms to RDF(S) semantics. ActiveRDF can be used with different RDF data stores: adapters have been implemented to generic SPARQL endpoints, Sesame, Jena, Redland and YARS and new adapters can be added easily. In addition, integration with the popular Ruby on Rails framework enables rapid development of Semantic Web applications.

Sig.ma: live views on the web of data

We demonstrate Sig.ma, both a service and an end user application to access the Web of Data as an integrated information space. Sig.ma uses an holistic approach in which large scale semantic web indexing, logic reasoning, data aggregation heuristics, ad hoc ontology consolidation, external services and responsive user interaction all play together to create rich entity descriptions. These consolidated entity descriptions then form the base for embeddable data mashups, machine oriented services as well as data browsing services. Finally, we discuss Sig.mas peculiar characteristics and report on lessions learned and ideas it inspires.

Semantic sitemaps: efficient and flexible access to datasets on the semantic web

Increasing amounts of RDF data are available on the Web for consumption by Semantic Web browsers and indexing by Semantic Web search engines. Current Semantic Web publishing practices, however, do not directly support efficient discovery and high-performance retrieval by clients and search engines. We propose an extension to the Sitemaps protocol which provides a simple and effective solution: Data publishers create Semantic Sitemaps to announce and describe their data so that clients can choose the most appropriate access method. We show how this protocol enables an extended notion of authoritative information across different access methods.

Introducing RDF Graph Summary with Application to Assisted SPARQL Formulation

One of the reasons for the slow adoption of SPARQL is the complexity in query formulation due to data diversity. The principal barrier a user faces when trying to formulate a query is that he generally has no information about the underlying structure and vocabulary of the data. In this paper, we address this problem at the maximum scale we can think of: providing assistance in formulating SPARQL queries over the entire Sindice data collection - 15 billion triples and counting coming from more than 300K datasets. We present a method to help users in formulating complex SPARQL queries across multiple heterogeneous data sources. Even if the structure and vocabulary of the data sources are unknown to the user, the user is able to quickly and easily formulate his queries. Our method is based on a summary of the data graph and assists the user during an interactive query formulation by recommending possible structural query elements.

Hierarchical link analysis for ranking web data

On the Web of Data, entities are often interconnected in a way similar to web documents. Previous works have shown how PageRank can be adapted to achieve entity ranking. In this paper, we propose to exploit locality on the Web of Data by taking a layered approach, similar to hierarchical PageRank approaches. We provide justifications for a two-layer model of the Web of Data, and introduce DING (Dataset Ranking) a novel ranking methodology based on this two-layer model. DING uses links between datasets to compute dataset ranks and combines the resulting values with semantic-dependent entity ranking strategies. We quantify the effectiveness of the approach with other link-based algorithms on large datasets coming from the Sindice search engine. The evaluation which includes a user study indicates that the resulting rank is better than the other approaches. Also, the resulting algorithm is shown to have desirable computational properties such as parallelisation.

A node indexing scheme for web entity retrieval

Now motivated also by the partial support of major search engines, hundreds of millions of documents are being published on the web embedding semi-structured data in RDF, RDFa and Microformats. This scenario calls for novel information search systems which provide effective means of retrieving relevant semi-structured information. In this paper, we present an “entity retrieval system” designed to provide entity search capabilities over datasets as large as the entire Web of Data. Our system supports full-text search, semi-structural queries and top-k query results while exhibiting a concise index and efficient incremental updates. We advocate the use of a node indexing scheme and show that it offers a good compromise between query expressiveness, query processing time and update complexity in comparison to three other indexing techniques. We then demonstrate how such system can effectively answer queries over 10 billion triples on a single commodity machine.

RDFS and OWL reasoning for linked data

Linked Data promises that a large portion of Web Data will be usable as one big interlinked RDF database against which structured queries can be answered. In this lecture we will show how reasoning --- using RDF Schema (RDFS) and the Web Ontology Language (OWL) --- can help to obtain more complete answers for such queries over Linked Data. We first look at the extent to which RDFS and OWL features are being adopted on the Web. We then introduce two high-level architectures for query answering over Linked Data and outline how these can be enriched by (lightweight) RDFS and OWL reasoning, enumerating the main challenges faced and discussing reasoning methods that make practical and theoretical trade-offs to address these challenges. In the end, we also ask whether or not RDFS and OWL are enough and discuss numeric reasoning methods that are beyond the scope of these standards but that are often important when integrating Linked Data from several, heterogeneous sources.