EmbER: A Neuro-Symbolic Method for Entity Comparison and Ranking in Text-Rich Knowledge Graphs

Huan Chen, Gareth J. F. Jones, Rob Brennan

Abstract: We present EmbER, a new neuro-symbolic method for embedding-based entity comparison and ranking in text-rich knowledge graphs (KGs) that have frequent textual fields. Traditional symbolic and homogeneous embedding approaches struggle with distinguishing the semantic nuances of diverse attribute data types in text-rich KGs. EmbER addresses this problem by partitioning KG attributes into categories - numerical values, short terms, and long textual descriptions - guided by the KG schema, and applying type-specific embedding strategies to each data type. The resulting embeddings are then combined into a unified entity representation for similarity computation and relevance ranking of entities. To evaluate EmbER, we construct a curated subset of DBpedia entities, focusing on Irish populated places, e.g. towns and counties. We use SMATCH, a graph-based similarity metric commonly used to evaluate semantic structures, along with human evaluation, to build a ground truth of entity similarity and ranking. Results show that EmbER outperforms a symbolic query-driven method and general KG embedding models, and is competitive with strong textual baselines. These results demonstrate the effectiveness of schema-guided data type-specific embeddings for entity representation, which offer the potential to enhance applications such as semantic search, entity linking and recommendation systems.

External IDs:dblp:conf/kgswc/ChenJB25