SEMANTIC ENRICHMENT BY EXPLOITING TOP-K PROCESSING

• US 20130268261A1
• Filed: 06/03/2011
• Published: 10/10/2013
• Est. Priority Date: 06/03/2010
• Status: Abandoned Application

• Current Assignee: InterDigital Madison Patent Holdings (InterDigital, Inc.)
• Original Assignee: Thomson Licensing (Vantiva SA)
• Inventors: Kim, Jong Wook, Kashyap, Ashwin S., Li, Dekai, Sridhar, Avinash, Mathur, Saurabh, Patel, Bankim A., Bhamidipati, Sandilya
• Application Number:
  US13/701,347
• Publication Number:
  US 20130268261A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/289 Phrasal analysis, e.g. finite state techniques or chunking
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

Proper representation of the meaning of texts is crucial to enhancing many data mining and information retrieval tasks, including clustering, computing semantic relatedness between texts, and searching. Representing of texts in the concept-space derived from Wikipedia has received growing attention recently, due to its comprehensiveness and expertise. This concept-based representation is capable of extracting semantic relatedness between texts that cannot be deduced with the bag of words model. A key obstacle, however, for using Wikipedia as a semantic interpreter is that the sheer size of the concepts derived from Wikipedia makes it hard to efficiently map texts into concept-space. An efficient algorithm is proved which is able to represent the meaning of a text by using the concepts that best match it. In particular, this approach first computes the approximate top- concepts that are most relevant to the given text. These concepts are then leverage to represent the meaning of the given text.

Claims

1. A method for performing semantic interpretation for keywords, the method comprising:

obtaining one or more keywords for semantic interpretation;

computing top-k concepts in a knowledge database for the one or more keywords; and

mapping the one or keywords into a concept space using the top-k concepts.

2. The method of claim 1, wherein the step of computing top-k concepts comprises the steps of:

estimating the bounds on the number of input lines; and

computing an expected score for a fully or partially unseen object.

3. The method of claim 1, wherein the step of obtaining one or more keywords for semantic interpretation comprises extracting keywords from close captioning data included with content.

4. The method of claim 1, further comprising processing concepts resulting from the mapping of the one or more keywords into the concept space.

5. The method of claim 4, wherein the processing comprises ranking the concepts

6. The method of claim 4, wherein the processing comprises creating a user profile based on the resulting concepts.

7. The method of claim 4, wherein the processing comprises creating a segmenting content based on the resulting concepts.

8. The method of claim 4, wherein the processing comprises filtering based on the resulting concepts.

9. The method of claim 4, wherein the processing comprises searching based on the resulting concepts.

10. A system for performing semantic interpretation for keywords, the system comprising:

keyword collection;

concept collection; and

concept processing.

11. The system of claim 10, wherein keyword collection comprises:

a close caption extractor; and

a sentence segmenter;

12. The system of claim 10, wherein concept collection comprises:

a semantic interpreter; and

a concept accumulator.

13. The system of claim 10, wherein concept processing comprises:

ranking; and

a user profile

14. A computer program product comprising a computer useable medium having a computer readable program, wherein the computer readable program when executed on a computer causes the computer to perform method steps including:

obtaining one or more keywords for semantic interpretation;

computing top-k concepts in a knowledge database for the one or more keywords; and

mapping the one or keywords into a concept space using the top-k concepts.