Evaluation Dataset and Methodology for Extracting Application-Specific Taxonomies from the Wikipedia Knowledge Graph

Georgeta Bordea (1) , Stefano Faralli , Fleur Mougin (2) , Paul Buitelaar , Gayo Diallo

1 BPH - Bordeaux population health
2 Université de Bordeaux

In this work, we address the task of extracting application-specific taxonomies from the category hierarchy of Wikipedia. Previous work on pruning the Wikipedia knowledge graph relied on silver standard taxonomies which can only be automatically extracted for a small subset of domains rooted in relatively focused nodes, placed at an intermediate level in the knowledge graphs. In this work, we propose an iterative methodology to extract an application-specific gold standard dataset from a knowledge graph and an evaluation framework to comparatively assess the quality of noisy automatically extracted taxonomies. We employ an existing state-of-the-art algorithm in an iterative manner and we propose several sampling strategies to reduce the amount of manual work needed for evaluation. A first gold standard dataset is released to the research community for this task along with a companion evaluation framework. This dataset addresses a real-world application from the medical domain, namely the extraction of food-drug and herb-drug interactions.

Mots clés: Taxonomy Extraction; Knowledge Graphs; Knowledge Graph Pruning

Domaines: Intelligence artificielle [cs.AI]

Georgeta Bordea, Stefano Faralli, Fleur Mougin, Paul Buitelaar, Gayo Diallo. Evaluation Dataset and Methodology for Extracting Application-Specific Taxonomies from the Wikipedia Knowledge Graph. LREC'2020, May 2020, Marseille, France. ⟨hal-02614678⟩