One of the major obstacles that currently prevents the Semantic Web from exploiting its full potential is that the data it provides access to is sometimes not available or outdated. The reason is rooted deep within its architecture that relies on data providers to keep the data available, queryable, and up-to-date at all times – an expectation that many data providers in reality cannot live up to for an extended (or infinite) period of time. Hence, decentralized architectures have recently been proposed that use replication to keep the data available in case the data provider fails. Although this increases availability, it does not help keeping the data up-to-date or allow users to query and access previous versions of a dataset. In this paper, we therefore propose ColChain (COLlaborative knowledge CHAINs), a novel decentralized architecture based on blockchains that not only lowers the burden for the data providers but at the same time also allows users to propose updates to faulty or outdated data, trace updates back to their origin, and query older versions of the data. Our extensive experiments show that ColChain reaches these goals while achieving query processing performance comparable to the state of the art.

SPARQL query services that balance processing between clients and servers become more and more essential to handle the increasing load for open and decentralized knowledge graphs on the Web. To this end, Linked Data Fragments (LDF) have introduced a foundational framework that has sparked research exploring a spectrum of potential Web querying interfaces in between server-side query processing via SPARQL endpoints and client-side query processing of data dumps. Current proposals in between typically suffer from imbalanced load on either the client or the server. In this paper, to the best of our knowledge, we present the first work that combines both client-side and server-side query optimization techniques in a truly dynamic fashion: we introduce WiseKG, a system that employs a cost model that dynamically delegates the load between servers and clients by combining client-side processing of shipped partitions with efficient server-side processing of star-shaped sub-queries, based on current server workload and client capabilities. Our experiments show that WiseKG significantly outperforms state-of-the-art solutions in terms of average total query execution time per client, while at the same time decreasing network traffic and increasing server-side availability.

Although the Semantic Web in principle provides access to a vast Web of interlinked data, the full potential remains mostly unexploited. One of the main reasons for this is the fact that the architecture of the current Web of Data relies on a set of servers providing access to the data. These servers represent bottlenecks and single points of failure that result in instability and unavailability of data at certain points in time. In this paper, we therefore propose a decentralized architecture ( Piqnic ) for sharing and querying semantic data. By combining both client and server functionality at each participating node, and introducing replication, Piqnic avoids bottlenecks and keeps datasets available and queryable although the original source might not be available. Our experimental results, using a standard benchmark of real datasets, show that Piqnic can serve as an architecture for sharing and querying semantic data, even in the presence of node failures.

Vast amounts of world knowledge is now accessible through Knowledge Graphs (KGs) in RDF format and can be queried using SPARQL. Yet, finding paths between nodes in such graphs is not part of the official SPARQL 1.1 standard; only the simpler functionality of checking reachability is supported, i.e., assessing whether two nodes are connected based on certain conditions formalized as property paths but without providing information on how they are actually connected. To close this gap of functionality, we present J edi , a system that extends a popular SPARQL engine, Jena, with the ability to compute paths connecting entities in a KG. J edi shows the k most relevant results to the user where relevance is assessed as a trade-off between path length and diversification of the intermediate nodes in the path. Furthermore, our solution is not limited to a single property path pattern but supports queries containing multiple property path patterns. While J edi supports arbitrary KGs, for demonstration purposes some predefined KGs, such as YAGO and DBLP, will be used.