Graph Embeddings
The problem of entity resolution is central in the field of Digital Humanities. It is also one of the major issues in the Golden Agents project, which aims at creating an infrastructure that enables researchers to search for patterns that span across decentralised knowledge graphs from cultural heritage institutes. To this end, we created a method to perform entity resolution on complex historical knowledge graphs. In previous work, we encoded and embedded the relevant (duplicate) entities in a vector space to derive similarities between them based on sharing a similar context in RDF graphs. In some cases, however, available domain knowledge or rational axioms can be applied to improve entity resolution performance. We show how domain knowledge and rational axioms relevant to the task at hand can be expressed as (probabilistic) rules, and how the information derived from rule application can be combined with quantitative information from the embedding.
Team
Jurian Baas
Developer
Mehdi Dastani
Leader WP3