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Towards a Vecsigrafo: Portable Semantics in Knowledge-based Text Analytics

The proliferation of knowledge graphs and recent advances in Artificial Intelligence have raised great expectations related to the combination of symbolic and distributional semantics in cognitive tasks. This is particularly the case of knowledge-based approaches to natural language processing as near-human symbolic understanding and explanation rely on expressive structured knowledge representations that tend to be labor-intensive, brittle and biased. This paper reports research addressing such limitations by capturing as embeddings the semantics of both words and concepts in large document corpora. We show how the emerging knowledge representation – our Vecsigrafo – can drive semantic portability capabilities that are not easily achieved by either word embeddings or knowledge graphs on their own, supporting curation, overcoming modeling gaps, enabling interlinking and multilingualism. In doing so, we also share our experiences and lessons learned and propose new methods that provide insight on the quality of such embeddings.