Using Triple Pattern Fragments to Enable Streaming of Top-k Shortest Paths via the Web

摘要: Searching for relationships between Linked Data resources is typically interpreted as a pathfinding problem: looking chains of intermediary nodes (hops) forming the connection or bridge these in single dataset across multiple datasets. In many cases centralizing all needed linked data certain (specialized) repository index to be able run algorithm not possible at least desired. To address this, we propose an approach top-k shortest pathfinding, which optimally translates query into sequences triple pattern fragment requests. Triple Pattern Fragments were recently introduced solution availability on Web and scalability client applications, preventing processing bottlenecks server. The results are streamed client, thus allowing clients do asynchronous paths. We explain how this behaves using training dataset, subset DBpedia with 10 million triples, show trade-offs SPARQL where gathered store machine. Furthermore investigate when increasing size up 110 triples.