SYSTEM AND METHOD FOR USE OF SEMANTIC UNDERSTANDING IN STORAGE, SEARCHING AND PROVIDING OF DATA OR OTHER CONTENT INFORMATION

• US 20110093479A1
• Filed: 10/15/2010
• Published: 04/21/2011
• Est. Priority Date: 10/19/2009
• Status: Active Grant

• Current Assignee: Gil Fuchs
• Original Assignee: Vexigo Ltd. (SharpLink Gaming Ltd.)
• Inventors: Fuchs, Gil
• Granted Patent: US 8,880,537 B2
• Time in Patent Office: Days
• US Class Current: 707/755
• CPC Class Codes: G06F 16/13 File access structures, e.g. distributed indices arrangements of input from, or output to, record carriers G06F3/06
• CPC Class Codes: G06F 16/24 Querying
• CPC Class Codes: G06F 16/36 Creation of semantic tools, e.g. ontology or thesauri
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/268 Morphological analysis
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 30/0241 Advertisements

Abstract

A system and method for using semantic understanding in storing and searching data and other information. A linearized tuple-based version of a conceptual graph can be created from a user input. A plurality of conceptual graphs, or portions thereof, can be compared to determine matches. An associative database can be created and/or searched using a hierarchy of conceptual graphs in tuple format, so that the data storage and searching of such database is optimized. The associative database can be used to integrate data from multiple different sources; form part of an Internet or other search engine; or used in other implementations. Also disclosed herein is a system and method for use of semantic understanding in searching and providing of content is described herein. In accordance with an embodiment, the system comprises a Syntactic Parser (SP) or statistical word tokenizer for data retrieval and parsing; a Syntax To Semantics (STS) transformational algebra-based semantic rule set, and an Associative Database (ADB) of linearized tuple conceptual graphs (TCG), utilizing a conceptual graph formalism. Data can be represented within the ADB, enabling both fast data retrieval in the form of semantic objects and a broad ranging taxonomy of content.

Claims

1. A computer-based method of expressing an input text to have a semantic meaning, comprising the steps of:

receiving an input text expressed as a plurality of sentences, each of which sentences includes a plurality of words therein;

parsing the input text using the link grammar methodology and lexicon, to determine a syntax within the input text, including linkages between the words;

using a set of semantic rules to transform the syntax to a semantic rendering or meaning, wherein each rule maps a particular linkage type or words in the input sentence to a semantic relationship component, or to a concept node participating in a semantic relationship component, respectively, and wherein the set of rules can be executed in combination or in succession to transform the input text to a plurality of semantic relationships expressed as a conceptual graph and corresponding to the input text.

2. A computer-based method of creating a linearized tuple based rendering of a conceptual graph (TCG) for use in expressing an input text as having a semantic rendering or meaning, comprising the steps of:

receiving a plurality of semantic relationships expressed as a conceptual graph and corresponding to the input text, and

storing the plurality of tuples as a tuple conceptual graph (TCG) together with a unique name or other TCG identifier.

3. A computer-based method of comparing a plurality of tuple conceptual graph (TCG), comprising the steps of:

receiving a first tuple conceptual graph (TCG), together with a first name or other TCG identifier and a first set of linearized tuples;

receiving a second TCG, together with a second name or other TCG identifier and a second set of linearized tuples;

ordering the first and second set of linearized tuples according to a sort criteria, and removing redundant or duplicate tuples;

comparing the first name or other TCG identifier and the second name or other TCG identifier to determine a match, and if so then identifying the first TCG as an equal match of the second TCG;

if the first name or other TCG identifier and the second name or other TCG identifier do not match, then successively comparing the tuples in the first TCG with the tuples in the second TCG to determine a full or partial match results between the tuples; and

reporting the results of the full or partial match.

4. A computer-based method of storing and retrieving text within a database, the text having a semantic rendering or meaning in a TCG, comprising the steps of:

providing a database content and a tuple conceptual graph (TCG) hierarchy, relation hierarchy, and node hierarchy;

receiving a TCG rendering of an input text expressed as a plurality of sentences or as a query, each of which includes a plurality words therein;

accessing the database of previously stored plurality of tuple conceptual graph (TCG) corresponding to other texts; and

matching, including comparing the tuple conceptual graph (TCG) and the tuples within it corresponding to the input text with the database content according to the TCG hierarchy, relation hierarchy, and node hierarchy, to determine an appropriate location for subsets of information within the input text within the database, and one of

storing the tuple conceptual graph (TCG) for the input text within the database, and/or

providing full or partially matching tuple conceptual graph (TCG) information from the database semantically matching the input text.

5. A computer-based method of discovering, storing and retrieving text within a database according to a semantic hierarchy, comprising the steps of:

providing a database content and a tuple conceptual graph (TCG) hierarchy, relation hierarchy, and node hierarchy;

receiving an input text which includes a plurality of phrases and words therein;

using a set of semantic rules to transform the syntax to a semantic rendering or meaning, including creating a plurality of tuples corresponding to the input text;

comparing the tuples in to the input text with the database content according to the TCG hierarchy, relation hierarchy, and node hierarchy, to determine an appropriate location for subsets of information within the input text within the database;

updating the database content with the subsets of information within the input text; and

performing TCG joins where appropriate, based either on partial tuple overlap or over any concept node argument to tuple information which comes from different sources, to combine content within the database into new TCG reflecting new semantic information which is not fully or directly present in any individual textual source or previously stored in the database.

6. A system for use of semantic understanding in searching and providing of content, comprising:

a syntactic parser or word tokenizer for data retrieval and parsing;

a syntax to semantics transformational algebra-based semantic rule set;

an associative database of linearized tuple conceptual graphs (TCG);

an interface for allowing a user to input requests for new data; and

wherein the system semantically interprets an original data, such as an original Web page content, and uses techniques such as a link grammar, rules, and algebra-based transformations to automatically populate the associative database with semantic links, and

wherein when requests for new data is received, the system uses the information therein to prepare a response to the request which includes semantically related content, such as related advertising, and which is then provided as, or in addition to, a response to the request.

7. The system of claim 6, wherein the content includes advertising categories.

8. The system of claim 6, wherein for an original data such as a Web page the system creates semantic objects contains related terms and phrases articulating a specific subject, and enabling automatic categorization of the Web page.

9. The system of claim 6, wherein semantic approach is used to analyze a potential advertisement and the Web page or site it links to, in order to identify relevant matches.

10. A method for use of semantic understanding in searching and providing of content, comprising:

providing access to a syntactic parser or word tokenizer for data retrieval and parsing;

providing access to a syntax to semantics transformational algebra-based semantic rule set;

providing access to an associative database of linearized tuple conceptual graphs (TCG); and

semantically interpreting an original data, such as an original Web page content, and using techniques such as a link grammar, rules, and algebra-based transformations to populate the associate database, and wherein when a request for new data is received, using the information therein to prepare a response to the request which includes semantically related content, and which is then provided as a response to the request.

11. The method of claim 10, wherein the content includes advertising categories.

12. The method of claim 10, wherein for an original data such as a Web page the system creates semantic objects contains related terms and phrases articulating a specific subject, and enabling automatic categorization of the Web page.

13. The method of claim 10, wherein semantic approach is used to analyze a potential advertisement and the Web page or site it links to, in order to identify relevant matches.