SemNet

SemNet is a semantic network of concepts generated by parsing natural language. The goal is to simulate a human intelligence model by interpretting text and forming logical connections between ideas.

Setup

The experiment requires the Stanford CoreNLP library jar and the model jar. A configuration file config.cfg needs to be created in the project's root of the following format:

[stanford]
jar-path: path/to/stanford-corenlp.jar
model-path: path/to/stanford-english-model.jar

[graphs]
output-dir: path/of/output/graph/dir

With the respective paths replaced with the locations of the downloaded jars and desired output directories.

Explanation

Currently, the mapper operates by using Stanford's corenlp library to graph the dependencies within each sentence (identify subject, direct object, adjectival modifiers, etc.), then using the structures within the graph, extract and/or infer logical rules based on the text. Given a set of sentences about one topic, a reasonable logical model can be generated for that topic.

Concepts follow a hierarchy of specificity. Some concepts are more specific than others, and the relationship between abstract and more concrete concepts is captured. For example, Concept <plant> [tall] is a child concept of Concept <plant> because they both refer to plants, except that the first one only refers to tall plants, which is a subset of all plants.

Example

parse("trees are tall plants")

=> Concept <tree>
     be:
       Concept <plant> [tall]
       
parse("plants have green leaves")

=> Concept <tree>
     be:
       Concept <plant> [tall]
         have:
           Concept <leav> [green]
           
   Concept <plant>
     have:
       Concept <leav> [green]
       
   Concept <leav>
   
   Concept <green>