Ido Dagan: Open Knowledge Graphs: Consolidating and Exploring Textual Information

IDO DAGAN TITLE: Open Knowledge Graphs: Consolidating and Exploring Textual Information ABSTRACT: How can we capture effectively the information expressed in multiple texts? How can we allow people, as well as computer applications, to easily explore it? The current semantic NLP pipeline typically ends at the single sentence level, putting the burden on applications to consolidate related information that is spread across different texts. Further, semantic representations are often based on non-trivial pre-specified schemata, which require expert annotation and hence complicate the creation of large scale corpora for effective training. In this talk, I will outline a proposal for a novel open representation of the information exressed jointly by multiple texts, which we term Open Knowledge Graphs (OKG). First, we follow the spirit of "open" semantic approaches, such as Open Information Extraction (OIE) and more concretely the recent Question-Answer SRL (QA-SRL) paradigm, which represent semantic structure solely via natural language expressions.