Knowledge Graph Generation from Web Text

The overall goal of the project is Knowledge Graph creation through the automated reading of large scale web text corpora. This means extracting information from text and organizing it in one structured resource, which makes it more accessible for computational use. The main use case of such a resource is question answering. The novelty of our approach on the following factors: (1) our method will allow for construction of such a graph using source text in multiple languages and will also make it possible to query it in multiple languages. (2) Moreover, in contrast to existing Knowledge Graphs, the interface between natural language queries and our graph will be direct and unproblematic because the representation language of the graph is the same as semantic representation of natural language utterances. Traditionally knowledge representation semantic parsing languages were developed separately which means that the task of mapping natural language queries onto knowledge databases is challenging. (3) Finally, our graph will make use of automatically mined information about entailments between events in order to supplement information extracted from text with the facts that were not mentioned but that are implied.
The question the project asks is whether the above listed aspects will provide benefits to downstream applications. The most obvious of those is question answering, which we are using as our evaluation task. The project involves designing a suitable representation language, developing a Knowledge Graph generation algorithm, integrating it with entailment resources, testing on available question answering datasets and potentially developing a dataset specifically for questions requiring access and integration of information from multiple documents.