Querying Archives of Dynamic Linked Open Data

The Linked Data paradigm promotes the use of the RDF data model to publish structured data on the Web and to create data-to-data links between different data sources. As a result, a continuously growing interconnected Web of data, consisting of typed hyperlinks between interconnected resources and documents has emerged over the past years and attracted the attention of several research areas, such as indexing and querying, reasoning, visualization of RDF data. However, structured interlinked datasets in this Web of data are not static but continuously evolving, which suggests the investigation of approaches to observe and preserve Linked data across time. This project tackles the problem of archiving and querying semantic Web data. Although traditional techniques for crawling and archiving collections of Web documents together with preservation policies of such collections, could assure the availability and traceability of datasets over time, current approaches are seriously compromised by scalability limits compared to Web scale and growth: as opposed to the scale- free nature of the Web itself, (a) archiving infrastructures are typically centralized; in addition, (b) these infrastructures include only very basic search capabilities, whereas upon archives for structured data on the Web also structured and time-traversing queries are an emerging demand. As for (a), we propose a decentralized, federated architecture for compressed and queryable archiving of Semantic Web data. To that end, we will develop a novel representation technique on the basis of succinct data structures, leading to compressed Linked archives for Linked data that can be distributed in a modular fashion. As for (b) we will investigate and extend suitable query languages for archived data capturing the required expressiveness for archives of evolving interlinked information, allowing to query not only data backwards but also queries about how data developed over time. Our architecture shall support querying for evolution patterns, time-traversing queries and ontological reasoning within and across such archives. Finally, we will validate all our steps on real Data, by archiving governmental Open Data. We will crawl and archive a large corpus of evolving interlinked governmental data and we evaluate our theoretical results on this corpus, assuring the feasibility and sustainability of the approach in a real-world domain of societal interest. We aim at advancing on several research fields (e.g. compact representations and indexing of evolving data at large scale, efficient query models and services enabling queries across time). The proposed work complements existing expertise in the host institution in data extraction and pattern recognition, semantic crawling, live queries, and foundations of query languages and reasoning. We have also established several cooperation agreements supporting project goals and strengthening international collaborations in the host institution.

No additional funding sources recorded.