Meaning-based information organization and retrieval

• US 6,453,315 B1
• Filed: 11/01/1999
• Issued: 09/17/2002
• Est. Priority Date: 09/22/1999
• Status: Expired due to Term

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Applied Semantics, Inc. (Alphabet Inc.)
• Inventors: Weissman, Adam J., Elbaz, Gilad Israel
• Primary Examiner(s): Homere, Jean R.
• Assistant Examiner(s): Wassum, Luke S
• Application Number:
  US09/431,760
• Time in Patent Office: 1,051 Days
• Field of Search: 707/1-5
• US Class Current: 1/1
• CPC Class Codes: G06F 16/3338 Query expansion
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

The present invention relies on the idea of a meaning-based search, allowing users to locate information that is close in meaning to the concepts they are searching. A semantic space is created by a lexicon of concepts and relations between concepts. A query is mapped to a first meaning differentiator, representing the location of the query in the semantic space. Similarly, each data element in the target data set being searched is mapped to a second meaning differentiator, representing the location of the data element in the semantic space. Searching is accomplished by determining a semantic distance between the first and second meaning differentiator, wherein this distance represents their closeness in meaning. Search results on the input query are presented where the target data elements that are closest in meaning, based on their determined semantic distance, are ranked higher.

Claims

1. A method comprising:

organizing concepts according to their meaning into a lexicon, said lexicon defining elements of a semantic space;

specifying relationships between concepts; and

determining a semantic distance from a first concept to a second concept, said semantic distance representing closeness in meaning between said first concept and said second concept, wherein said semantic distance is calculated by evaluating steps along a semantic path between said first concept and said second concept and applying a dynamic scaling factor to a perceived distance of each step along the semantic path according to types of relationships followed, directionality of the relationships and changes in direction along the semantic path, and number of competing relationships followed at each step.

2. A method according to claim 1 further comprising determining new relationships between concepts in said lexicon by determining said semantic distance between concepts, defining a radius of semantic distance about a given concept and inferring a relationship between said concepts, excluding concepts falling in distances beyond said radius.

3. A method according to claim 1 wherein said elements are related by a connection, said connections including a lateral bind, a kind of and a part of.

4. A method according to claim 3 wherein said connection has an associated strength representing the degree to which said elements are related.

5. A method according to claim 4 wherein said strength from a first element to a second element may be different from the strength from said second element to said first element.

6. A method of searching a data set comprising:

organizing concepts according to their meaning into a lexicon, said lexicon defining elements of a semantic space,

providing a first meaning differentiator in response to an input query, wherein said first meaning differentiator is a set of concepts from said lexicon that represent a first location of said query in the semantic space,

providing a second meaning differentiator for each element of a target data set, wherein said second meaning differentiator is a set of concepts from said lexicon that represent a second location of said target data element in e semantic space;

determining a semantic distance from the first meaning differentiator to the second meaning differentiator, wherein the semantic distance is calculated by evaluating steps along a semantic path between the first meaning differentiator and the second meaning differentiator and applying a dynamic scaling factor To a perceived distance of each step along the semantic path according to the types of relationships followed, directionality of the relationships and changes in direction along The semantic path, and number of competing relationships followed at each step, and

presenting results of a search conducted on the target data set for target data elements close in meaning to an input query, wherein the closeness in meaning is determined by the semantic distance between the first meaning differentiator for said input query and The second meaning differentiator for each target data element.

7. A method according to claim 6 wherein said search is conducted by ranking elements of said target data set according to conceptual relevance.

8. A method according to claim 6 wherein said concepts may be marked as at least one of a geographical location, offensive, unique instance, and timely, and where such markings can be used to filter elements from the target data set so that target data elements with said markings can be prevented from being presented as search results.

9. A method according to claim 6 further comprising:

enabling a user to select at least one meaning from the set of possible meanings for the input query to provide the correct interpretation of the input query for use as input to the search.

10. A method according to claim 6 wherein said target data set includes documents.

11. A method according to claim 10 wherein said documents include documents accessible via the world-wide web.

12. A method according to claim 10 wherein the meaning differentiators for the documents are determined in an interpretation phase by mapping each word in the document to probable desired meanings.

13. A method according to claim 12 wherein the interpretation phase uses the relationships between concepts defined by the lexicon to increase the likelihood of meanings of each word which have relationships to meanings of other words in the document.

14. A method according to claim 6 wherein the target data set includes subjects in a directory.

15. A method according to claim 6 wherein the input query may be a text string consisting of words.

16. A method according to claim 15 wherein the meaning differentiator is determined in an interpretation phase by mapping each word in an input string to probable desired meanings.

17. A method according to claim 16 wherein said interpretation phase uses the relationships between concepts defined by the lexicon to increase the likelihood of meanings of each word which have relationships to meanings of other words in the input.

18. A method according to claim 6 wherein the input query may be a set of predetermined concepts.

19. A method according to claim 6 wherein the target data element may be a set of predetermined concepts.

20. A method according to claim 6 wherein the concepts are given a commonness value;

and wherein the search is conducted to improve the ranking of elements of said target data set according to commonness of the concepts.

21. A method according to claim 6 wherein the meaning differentiator is an intersection or a union of concepts from the lexicon.

22. A method according to claim 6 wherein the target data elements are preindexed according to the concepts in their meaning differentiators to improve the speed of the search.

23. A method according to claim 6 wherein a user can initiate a secondary search for documents which are close in meaning to at least one of the search results.

24. An information handling system comprising:

means for organizing concepts according to their meaning into a lexicon that defines elements of a semantic space;

means for providing a first meaning differentiator in response to an input query, wherein the first meaning differentiator is a set of concepts from the lexicon representing a first location in the semantic space;

means for providing a second meaning differentiator for each element of a target data set, wherein the second meaning differentiator is a set of concepts from the lexicon representing a second location in the semantic space; and

means for determining a semantic distance from the first location in the semantic space to the second location in the semantic space, wherein the semantic distance represents closeness in meaning between the first location in the semantic space and the second location in the semantic space, wherein search results are presented for target data elements close in meaning to the input query and the closeness in meaning is determined by the semantic distance between the first meaning differentiator for said input query and the second meaning differentiator for each target data element.