Creating a Semantic Graph from Wikipedia by Ryan Tanner, Trinity University.
With the continued need to organize and automate the use of data, solutions are needed to transform unstructred text into structred information. By treating dependency grammar functions as programming language functions, this process produces \property maps” which connect entities (people, places, events) with snippets of information. These maps are used to construct a semantic graph. By inputting Wikipedia, a large graph of information is produced representing a section of history. The resulting graph allows a user to quickly browse a topic and view the interconnections between entities across history.
Of particular interest is Ryan’s approach to the problem:
Most approaches to this problem rely on extracting as much information as possible from a given input. My approach comes at the problem from the opposite direction and tries to extract a little bit of information very quickly but over an extremely large input set. My hypothesis is that by doing so a large collection of texts can be quickly processed while still yielding useful output.
A refreshing change from semantic orthodoxy that has a happy result.
Printing the thesis now for a close read.
(Source: Jack Park)