Understanding Quantities in Web Tables and Text

In the process of understanding quantity mentions in tables and text, we are faced with the following challenges; First, there is no comprehensive knowledge base for anchoring quantity mentions. Second, tables are created ad-hoc without a standard schema and with ambiguous header names; also table cells usually contain abbreviations. Third, quantities can be written in multiple forms and units of measures–for example “48 km/h” is equivalent to “30 mph”. Fourth, the text usually refers to the quantities in tables using aggregation, approximation, and different scales.

In this thesis, we target these challenges through the following contributions:

• We present the Quantity Knowledge Base (QKB), a knowledge base for representing Quantity mentions. We construct the QKB by importing information from Freebase, Wikipedia, and other online sources.

• We propose Equity: a system for automatically canonicalizing header names and cell values onto concepts, classes, entities, and uniquely represented quantities registered in a knowledge base. We devise a probabilistic graphical model that captures coherence dependencies between cells in tables and candidate items in the space of concepts, entities, and quantities. Then, we cast the inference problem into an efficient algorithm based on random walks over weighted graphs. baselines.

• We introduce the quantity alignment problem: computing bidirectional links between textual mentions of quantities and the corresponding table cells. We propose BriQ: a system for computing such alignments. BriQ copes with the specific challenges of approximate quantities, aggregated quantities, and calculated quantities.

• We design ExQuisiTe: a web application that identifies mentions of quantities in text and tables, aligns quantity mentions in the text with related quantity mentions in tables, and generates salient suggestions for extractive text summarization systems.