Entity Type Prediction in Knowledge Graphs using Embeddings
This addresses data quality issues in knowledge graphs for applications in data mining and information retrieval, but appears incremental as it builds on existing embedding methods.
The paper tackles the problem of noisy, incomplete, and incorrect entity type information in open knowledge graphs by proposing a multi-label classification approach using KG embeddings, reporting comparisons with state-of-the-art methods on experiments with KGs.
Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized as the backbone of diverse applications in the field of data mining and information retrieval. Hence, the completeness and correctness of the Knowledge Graphs (KGs) are vital. Most of these KGs are mostly created either via an automated information extraction from Wikipedia snapshots or information accumulation provided by the users or using heuristics. However, it has been observed that the type information of these KGs is often noisy, incomplete, and incorrect. To deal with this problem a multi-label classification approach is proposed in this work for entity typing using KG embeddings. We compare our approach with the current state-of-the-art type prediction method and report on experiments with the KGs.