Title: Knowledge Graphs for Natural Language Processing
Speaker: José Manuel Gómez-Pérez
Knowledge graphs provide structured representations of entities,
concepts and relations that are useful in natural language
processing for tasks like word-sense disambiguation, natural
language inference, entailment or classification. In this class we
will show how knowledge graphs can contribute to train more
expressive models that are able to learn the meaning of words by
linking them to explicitly represented concepts in the graph. We
will illustrate how this can be achieved by jointly learning word
and concept representations from both the knowledge graph and a
text corpus as embeddings in a common vector space. In addition to
increased accuracies for tasks in natural language processing we
will see how this approach also entails benefits for knowledge
graph curation and evolution, like suggesting new concepts in a
knowledge graph, merging previously existing concepts, and
supporting the alignment of existing knowledge graphs.
The slides for the presentation are available here.
The author has generously arranged for a discount coupon for his book titled A Practical Guide to Hybrid Natural Language Processing.
Dr. José Manuel Gómez-Pérez leads the Cogito Research Lab at Expert
System, where he focuses on the combination of neural and
knowledge-based approaches to enable reading comprehension in
machines. His work lies at the intersection of several areas of
artificial intelligence, including natural language processing,
knowledge graphs and deep learning. He also consults for organizations
like the European Space Agency. A former Marie Curie fellow, José
Manuel holds a Ph.D. in Computer Science and Artificial Intelligence
from Universidad Politécnica de Madrid. He regularly publishes in top
scientific conferences and journals and his views have appeared in
magazines like Nature and Scientific American or national newspapers
like El País.