Granted Patent B1

US 7,389,225 · App. 09/691,629 · Granted Jun 17, 2008

Abstract

Claims (94)

1. A computer-implemented method for constructing a single vector representing a semantic abstract in a topological vector space for a semantic content of a document on a computer system, the method comprising:

storing a semantic content for the document in computer memory accessible by the computer system;

identifying a directed set of concepts as a dictionary, the directed set including a maximal element at least one concept, and at least one chain from the maximal element to every concept;

selecting a subset of the chains to form a basis for the dictionary;

identifying lexemes/lexeme phrases in the semantic content;

measuring how concretely each lexemes/lexeme phrase is represented in each chain in the basis and the dictionary;

constructing state vectors in the topological vector space for the semantic content using the measures of how concretely each lexemes/lexeme phrase is represented in each chain in the dictionary and the basis;

superpositioning the state vectors to construct the single vector; and

comparing the single vector with a second semantic abstract for a second document to determined whether the second document is semantically close to the document.

2. A method according to claim 1 , wherein superpositioning the state vectors includes adding the state vectors using vector arithmetic.

3. A method according to claim 1 , wherein superpositioning the state vectors includes weighting the state vectors.

4. A method according to claim 1 further comprising normalizing the single vector.

5. A method according to claim 1 , wherein:

storing a semantic content includes:

storing the document in computer memory accessible by the computer system; and

extracting words from at least a portion of the document;

constructing state vectors includes constructing a state vector in the topological vector space for each word using the dictionary and the basis; and

the method further comprises filtering the state vectors.

6. A computer-readable medium containing a program to construct a single vector representing a semantic abstract in a topological vector space for a semantic content of a document on a computer system, the program comprising:

storing a semantic content for the document in computer memory accessible by the computer system;

identifing a directed set of concepts as a dictionary, the directed set including a maximal element at least one concept, and at least one chain from the maximal element to every concept;

selecting a subset of the chains to form a basis for the dictionary;

identifing lexemes/lexeme phrases in the semantic content;

measuring how concretely each lexemes/lexeme phrase is represented in each chain in the basis and the dictionary;

constructing state vectors in the topological vector space for the semantic content using the measures of how concretely each lexemes/lexeme phrase is represented in each chain in the dictionary and the basis;

superpositioning the state vectors to construct the single vector; and

storing the single vector as the semantic abstract for the document.

7. A computer-readable medium according to claim 6 , wherein superpositioning the state vectors includes adding the state vectors using vector arithmetic.

8. A computer-readable medium according to claim 6 , wherein superpositioning the state vectors includes weighing the state vectors.

9. A computer-readable medium according to claim 6 , the program further implementing normalizing the single vector.

10. A computer-readable medium according to claim 6 , wherein:

storing the semantic software includes:

storing the document in computer memory accessible by the computer system; and

extracting words from at least a portion of the document;

constructing state vectors includes constructing a state vector in the topological vector space for each word using the dictionary and the basis; and

the program further implements filtering the state vectors.

11. An apparatus on a computer system to construct a single vector representing a semantic abstract in a topological vector space for a semantic content of a document on a computer system, the apparatus comprising:

a semantic content stored in a memory of the computer system;

a lexeme identifier adapted to identify lexemes/lexeme phrases in the semantic content;

a state vector constructor for constructing state vectors in the topological vector space for each lexeme/lexeme phrase identified by the lexeme identifier, the state vectors measuring how concretely each lexeme/lexeme phrase identified by the lexeme identifier is represented in each chain in a basis and a dictionary, the dictionary including a directed set of concepts including a maximal element and at least one chain from the maximal element to every concept in the directed set, the basis including a subset of chains in the directed set; and

a superpositioning unit adapted to superposition the state vectors into a single vector as the semantic abstract.

12. An apparatus according to claim 11 , wherein:

the state vector includes an associated threshold distance; and

the apparatus further comprises:

search means for searching the topological vector space for a second document with a second semantic abstract within the threshold distance associated with the first semantic abstract for the first document; and

retrieval means to retrieve the second document.

13. An apparatus according to claim 11 , wherein the superpositioning unit includes a vector arithmetic unit adapted to add the state vectors.

14. An apparatus according to claim 11 further comprising a normalization unit adapted to normalize the single vector.

15. An apparatus according to claim 11 , wherein:

the apparatus further comprises:

a lexeme extractor adapted to extract lexemes/lexeme phrases from the semantic content; and

filtering means for filtering the state vectors; and

the state vector constructor is adapted to constructing a state vector in the topological vector space for each lexeme/lexeme phrase using the dictionary and the basis.

16. A computer-implemented method for constructing minimal vectors representing a semantic abstract in a topological vector space for a semantic content of a document on a computer system, the method comprising:

storing a semantic content for the document in computer memory accessible by the computer system;

identifying a directed set of concepts as a dictionary, the directed set including a maximal element at least one concept, and at least one chain from the maximal element to every concept;

selecting a subset of the chains to form a basis for the dictionary;

identifying lexemes/lexeme phrases in the semantic content;

measuring how concretely each lexemes/lexeme phrase is represented in each chain in the basis and the dictionary;

constructing state vectors in the topological vector space for the semantic content using the measures of how concretely each lexemes/lexeme phrase is represented in each chain in the dictionary and the basis;

locating clumps of state vectors in the topological vector space;

superpositioning the state vectors within each clump to form a single vector representing the clump;

collecting the single vectors representing each clump to form the minimal vectors; and

storing the minimal vectors as the semantic abstract for the document.

17. A computer-readable medium containing a program to construct minimal vectors representing a semantic abstract in a topological vector space for a semantic content of a document on a computer system, the program executable by a computer and implementing:

storing a semantic content for the document in computer memory accessible by the computer system;

identifing a directed set of concepts as a dictionary, the directed set including a maximal element at least one concept, and at least one chain from the maximal element to every concept;

selecting a subset of the chains to form a basis for the dictionary;

identifing lexemes/lexeme phrases in the semantic content;

measuring how concretely each lexemes/lexeme phrase is represented in each chain in the basis and the dictionary;

constructing state vectors in the topological vector space for the semantic content using the measures of how concretely each lexemes/lexeme phrase is represented in each chain in the dictionary and the basis;

locating clumps of state vectors in the topological vector space;

superpositioning the state vectors within each clump to form a single vector representing the clump;

collecting the single vectors representing each clump to form the minimal vectors; and

storing the minimal vectors as the semantic abstract for the document.

18. An apparatus on a computer system to construct minimal vectors representing a semantic abstract in a topological vector space for a semantic content of a document on a computer system, the apparatus comprising:

a semantic content stored in a memory of the computer system;

a state vector constructor for constructing state vectors in the topological vector space for each lexeme/lexeme phrase in the semantic content the state vectors measuring how concretely each lexeme/lexeme phrase is represented in each chain in a basis and a dictionary, the dictionary including a directed set of concepts including a maximal element and at least one chain from the maximal element to every concept in the directed set, the basis including a subset of chains in the directed set;

a clump locator unit adapted to locate clumps of state vectors in the topological vector space;

a superpositioning unit adapted to superposition the state vectors within each clump into a single vector representing the clump; and

a collection unit adapted to collect the single vectors representing the clump into the minimal vectors of the semantic abstract.

19. A method according to claim 1 , further comprising storing the single vector as the semantic abstract for the document.

20. A method according to claim 1 , further comprising storing the minimal vectors as the semantic abstract for the document.

21. An apparatus, comprising:

means for storing a semantic content for a document in computer memory accessible by a computer system;

means for identifying a directed set of concepts as a dictionary, the directed set including a maximal element at least one concept, and at least one chain from the maximal element to every concept;

means for selecting a subset of the chains to form a basis for the dictionary;

means for identifying lexemes/lexeme phrases in the semantic content;

means for measuring how concretely each lexemes/lexeme phrase is represented in each chain in the basis and the dictionary;

means for constructing state vectors in the topological vector space for the semantic content using the measures of how concretely each lexemes/lexeme phrase is represented in each chain in the dictionary and the basis;

means for locating clumps of state vectors in the topological vector space;

means for superpositioning the state vectors within each clump to form a single vector representing the clump;

means for collecting the single vectors representing each clump to form the minimal vectors; and

means for storing the minimal vectors as the semantic abstract for the document.

Assignments (7)