In this paper, we present Ontolearn---a framework for learning OWL class expressions over large knowledge graphs. Ontolearn contains efficient implementations of recent state-of-the-art symbolic and neuro-symbolic class expression learners including EvoLearner and DRILL. A learned OWL class expression can be used to classify instances in the knowledge graph. Furthermore, Ontolearn integrates a verbalization module based on an LLM to translate complex OWL class expressions into natural language sentences. By mapping OWL class expressions into respective SPARQL queries, Ontolearn can be easily used to operate over a remote triplestore. The source code of Ontolearn is available at https://github.com/dice-group/Ontolearn.

Abstract Time-efficient solutions for querying RDF knowledge graphs depend on indexing structures with low response times to answer SPARQL queries rapidly. Hypertries—an indexing structure we recently developed for tensor-based triple stores—have achieved significant runtime improvements over several mainstream storage solutions for RDF knowledge graphs. However, the space footprint of this novel data structure is still often larger than that of many mainstream solutions. In this work, we detail means to reduce the memory footprint of hypertries and thereby further speed up query processing in hypertrie-based RDF storage solutions. Our approach relies on three strategies: (1) the elimination of duplicate nodes via hashing, (2) the compression of non-branching paths, and (3) the storage of single-entry leaf nodes in their parent nodes. We evaluate these strategies by comparing them with baseline hypertries as well as popular triple stores such as Virtuoso, Fuseki, GraphDB, Blazegraph and gStore. We rely on four datasets/benchmark generators in our evaluation: SWDF, DBpedia, WatDiv, and WikiData. Our results suggest that our modifications significantly reduce the memory footprint of hypertries by up to 70% while leading to a relative improvement of up to 39% with respect to average Queries per Second and up to 740% with respect to Query Mixes per Hour.

Data partitioning is an effective way to manage large datasets. While a broad range of RDF graph partitioning techniques has been proposed in previous works, little attention has been given to workload-aware RDF graph partitioning. In this paper, we propose two techniques that make use of the querying workload to detect the portions of RDF graphs that are often queried concurrently. Our techniques leverage predicate co-occurrences in SPARQL queries. By detecting highly co-occurring predicates, our techniques can keep data pertaining to these predicates in the same data partition. We evaluate the proposed partitioning techniques using various real-data and query benchmarks generated by the FEASIBLE SPARQL benchmark generation framework. Our evaluation results show the superiority of the proposed techniques in comparison to previous techniques in terms of better query runtime performances.