1. Using Link Grammar and WordNet on Fact Extraction for the Travel Domain

2. Problem Statement Extract facts from English sentences Reasoning can be done on extracted facts How to extract facts? Using the connectivity feature of Link Grammar to associate relations between extracted facts Using WordNet to associate semantic meanings to extracted facts

3. WordNet What is WordNet? Developed by the Cognitive Science Laboratory at Princeton University Synonym sets of nouns, verbs and adjectives Various semantic relations connect the synonym sets car is_a motor vehicle breathe entails inhale present and absent are antonyms

4. An example of is_a hierarchy airplane, aeroplane, plane -- => heavier-than-air craft => aircraft => craft => vehicle => conveyance, transport => artifact, artefact => object, physical object => entity => whole, whole thing, unit => object, physical object => entity

5. Fact Extraction Prerequisites: knowledge base for travel objects plane is_a transportation ticket is_a document knowledge base for geographic locations Input: a simple English sentence related to the travel domain Output: facts extracted from Link Grammar output and association with WordNet

6. Example John took a plane from Paris to Baghdad. actor(e1,john) parameter(e1,from,paris) parameter(e1,to,baghdad) place(paris) place(baghdad) event(e1,took) object(e1,plane)

7. Associate WordNet Sense For object(e1,plane), the word “plane” exists in the travel object knowledge base. plane is_a transportation Stem the word transportation => transport. For event(e1,took), do WordNet search on “took” and parse the search result to match the word transport. There is a match in the parsing, so associate event(e1,took) to sense 11 as event(e1,took,11).

8. Associate WordNet Sense There is a match in the parsing, so associate event(e1,took) to sense 11 as event(e1,took,11).

9. Extracted Facts John took a plane from Paris to Baghdad. actor(e1,john) parameter(e1,from,paris) parameter(e1,to,baghdad) place(paris) place(baghdad) object(e1,plane) event(e1,took,11)

10. Future Work Currently only utilizes the noun and verb parts of WordNet. Expand current work by utilizing the adjective part of WordNet and the semantic relations. Work on more examples to generalize the algorithm.