Knowledge graph embeddings that generate vector space representations of knowledge graph triples, have gained considerable popularity in past years. Relying on the presumed semantic capabilities of the learned embeddings, they have been leveraged for various other tasks such as entity typing, rule mining and conceptual clustering. However, a critical analysis and evaluation of these embeddings in terms of semantic representation of the underlying entities and relations reveals their limitations. The semantic representation in the knowledge graph embeddings is not universal but restricted to a small subset of the entities based on dataset characteristics. This can be improved by way of incorporating ontological reasoning during the training of the embeddings. The ReasonKGE approach identifies incorrect predictions produced by a given embedding model dynamically via symbolic reasoning and feeds them as negative samples for retraining the model. This method demonstrates the gains in accuracy and semantic consistency of facts produced by embedding models, thus improving their overall semantic capabilities. The fact predictions from embeddings and the semantic features encoded in such models play an important role in multi-modal knowledge completion tasks. Recent works on finding semantic interpretations in embedding techniques hold promise and could pave the way for explainable multi-modal predictions from knowledge graph embeddings.
Bio:
Dr Nitisha Jain is a Postdoctoral candidate in Computer Science in the Department of Informatics at King’s College London. She pursued her PhD on Knowledge Graphs from the Hasso Plattner Institute (University of Potsdam). Prior to this, she worked at the Max Planck Institute of Informatics (Saarland University) and was a Research Scientist at IBM AI, India research lab. Previously, she completed Masters in Research from Indian Institute of Science (IISc) in 2015 with a focus on Cloud Computing.
Her primary research interests are related to Knowledge Graphs, Semantic Web and Machine Learning and recent publications can be found at nitishajain.github.io. She is actively involved in the EU Horizon MuseIT project (www.muse-it.eu).
