Knowledge bases for explainable triple store benchmarking (CMPVY, SWDW)

This project provides two knowledge graphs that we created for the two triple store benchmarks: CMPVY(Carcinogenesis, Mutagenesis, Premier League and Vicodi), and SWDW(Swdf, Watdiv, Dbpedia, Wikidata). Here are some more details: 1. Preprocessing We preprocessed the DBpedia reference graph in SWDW by: Removing properties of the http://dbpedia.org/property/ namespace. Inferring the classes of all entities based on the class hierarchy. 2. Knowledge Base Structure In the first step of our benchmarking framework, we generate a knowledge graph comprising information from the dataset used during the benchmarking process. Our work include two types of data for each query: Reference knowledge graph(s)Each query has to be executed on a certain RDF graph. We adopt Lemming to add the following RDF graph features to our knowledge graph: Number of vertices and edges Number of colourless vertices Minimum, maximum, and average in-degree and out-degree Standard deviation of in-degree and out-degree Graph diameter Average clustering coefficient Number of node triangles and edge triangles SPARQL queryWe adopt LSQ to add the following SPARQL query features to our knowledge graph: Entities (dqb:hasEntity), properties (dqb:hasProperty) contained in the query, and the CBD of the entities Type of query The number of triple patterns The number of basic graph patterns The average degree of vertices The median degree of vertices involved in join operations The minimum, maximum, and median number of triple patterns in a basic graph pattern The presence of certain keywords such as FILTER, DISTINCT, and GROUP BY