Semantic technology, once a niche academic endeavour, has evolved to the point of becoming an important asset for big organizations and enterprises. Yet, it remains unclear how to relate existing knowledge graphs to the vast amounts of text and structured data available online. In this talk, I will argue that this challenge calls for a move towards more cognitive approaches, including 1) better knowledge integration methods, 2) an increased reliance on linguistic knowledge, and 3) neural modeling. The talk will showcase several of our contributions towards this goal. These include our work on large multilingual knowledge graphs such as the Universal Wordnet, on integrated semantic resources such as FrameBase, and on large-scale common-sense resources such as WebChild.
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