Arnaud Soulet

In J. Comput. Cult. Herit.

Abstract As more and more knowledge graphs (KG) are published on the Web, there is a need for tools that show their content. This implies showing the schema-level patterns instantiated in the graph, but also the terms used to qualify its entities. In this article, we present a new profiling tool that we call TTprofiler. It shows the predicates that relate types in the KG, and also the terms present in this KG, because of their paramount importance in most KGs, especially in the Cultural Heritage (CH) domain.

Trie-based output itemset sampling

By Lamine Diop, Cheikh Talibouya Diop, Arnaud Giacometti, Dominique Li, Arnaud Soulet

2022-12-12

In 2022 IEEE international conference on big data (big data)

Abstract Pattern sampling algorithms produce interesting patterns with a probability proportional to a given utility measure. Utility changes need quick re-preprocessing when sampling patterns from large databases. In this context, existing sampling techniques require storing all data in memory, which is costly. To tackle these issues, this work enriches D. Knuth’s trie structure, avoiding 1) the need to access the database to sample since patterns are drawn directly from the enriched trie and 2) the necessity to reprocess the whole dataset when the utility changes.

TTProfiler: Types and terms profile building for online cultural heritage knowledge graphs

Trie-based output itemset sampling

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