Information Consolidation

Motivation

Although existing search engines are effective in identifying relevant documents among bilions on non-relevant ones, they remain weak at isolating the facts of users' interest within these documents, let alone organizing and presenting this knowledge intuitively and concisely. Searchers have to laborously skim through all retrieved documents and collect the statements that are relevant to their information needs.

For example, a public decision maker in the domain of education wants to learn positive and negative experiences with a particular policy across countries, its impact on various populations, etc. So far, such information must have been consolidated by field experts, which is costly and time-consuming.

Goals

This project targets the big next step in information access technology by

Automatically identifying relevant statements
Consolidating the information and inferring relations between the statements
Enabling users to explore the consolidated information

Methods

The progress of the project will be led by an iterative methodology that encompasses the following:

Corpus – large data set of partially annotated data in the domain of educational topics acquired using focused crawling and web-based annotation tools
Linguistic annotation on various levels (syntax, semanantic roles, word senses, named entities, co-reference resolution, truth values, and other domain-specific ones) using state-of-the-art automatic annotation methods
Extracting atomic statements – by adapting and extending open information extraction techniques
Reflecting relationships between statements – by applying textual entailment and semantic similarity methods
Knowledge exploration – effective and efficient user interfaces for interactively displaying statements relevant to user queries