Algorithm | ||
Require: A collection of returned documents from a search query. Ensure: A collection of semantic manifolds. | ||
Step 1 | Perform feature extractions using discriminative linear chain Conditional Random Field method to generate named entities. | |
Step 2 | Construct a manifold from the set of named entities generated from the document collection. | |
Step 3 | Classify the manifold into isomorphic (homogeneous) categories by using the Graph-based Tree-width Decomposition algorithm starting from a fixed dimension local manifold. Require: is the vertex set of named entities that each ti is associated with its named categories equipped with a weighted probability. Ensure: is the set of isomorphic semantic manifolds. where. | |
Step 3.1 | Let a semantic topic set:. Let G = (V, E) be the undirected connected graph generated from the returned documents. | |
Step 3.2 | Given a tree-width d, find a semantic manifold Mj generated from single named entities for each semantic category zi initially in which |Mj| = d and the semantic mapping with a probability, and quantity | |
Step 3.3 | Perform graph decompositions on G starting from Mj. |