Search engine with increased performance and specificity

• US 20070143273A1
• Filed: 12/08/2006
• Published: 06/21/2007
• Est. Priority Date: 12/08/2005
• Status: Abandoned Application

• Current Assignee: Intelligent Search Technologies
• Original Assignee: Intelligent Search Technologies
• Inventors: Siadaty, Mir, Knaus, William
• Application Number:
  US11/635,815
• Publication Number:
  US 20070143273A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/3338 Query expansion
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

The present invention discloses a system and methods for retrieval of most relevant information from a given digital data repository. This is done in the first step by verifying two conditions of relevance, presence of query words plus presence of at least one type of relationship between the words in the data record. Additionally a numeric relevance score is computed for each relevant record, such that they can be sorted descendingly according to this relevance metric. The most relevant results will be shown first, while irrelevant records are eliminated. This reduces the volume of the results substantially. The information retrieval system according to this invention includes: a data pre-processing component where multiple steps of processing is performed, a second new data repository where the modified data is stored, a user interface with the capability of real-time translation of user'"'"'s query, a search engine, and computing hardware in a distributed architecture.

Claims

1. A search engine for searching and retrieving information from a data repository comprising:

a pre-processing component that modifies the records of the data repository wherein;

i. the concepts of the language are identified in each record;

ii. term ambiguity and term synonymy are resolved;

iii. compound or complex semantic units are processed and simplified;

iv. presence and type of relationships between terms are detected; and

v. anaphoric terms and sortal anaphoric noun phrases are resolved, and the actual entities they refer to are identified;

a new, second data repository where the data is stored in the pre-processed, modified representation, containing all the concept IDs and the relation types;

a user interface wherein the user enters a query, and where the user query is translated to concept IDs of the language;

a data engine wherein concept IDs of the user query are matched against the concept IDs of the data records of said second data repository, and where the matching records are returned according to a relevance metric calculated for each data record; and

a multitude of computing hardware wherein said pre-processing component, second data repository, said user interface, and said data engine operate simultaneously and in parallel in response to a single user query.

2. The search engine of claim 1, wherein said information is retrieved by verifying two conditions for relevance of each data record to a given user query:

1) presence of user'"'"'s query words in said record; and

2) presence of a relationship or a specific type of relation between the query words in said record.

3. The search engine of claim 1, wherein said data repository contains one or more textual data fields, in a plurality of languages.

4. The search engine of claim 1, wherein said concepts of the language are identified in the textual fields, using a plurality of methods including:

1) morphological rules;

2) parts-of-speech tagging engines;

3) grammar rules;

4) combined rule-based and dictionary-based methods;

5) support-vector machines;

6) hidden Markov model; and

7) classifiers such as naï

ve Bayes and decision-trees.

5. The identification of concepts in claim 4, wherein a plurality of existing and emerging standardized vocabularies are used simultaneously in data processing, including the vocabulary standards of the 137 sources from the Unified Medical Language System (UMLS).

6. The search engine of claim 1, wherein compound semantic units (sentences) are simplified, using a plurality of part-of-speech tagging processes.

7. The search engine of claim 1, wherein presence and type of relationships between terms are detected, using methods of:

1) grammar-based parsing;

2) template matching methods; and

3) correlation methods, including, but not limited to, the hidden Markov model and statistical concurrence.

8. The relationships of claim 7 are detected, with a plurality of tools, including Perl Regular Expressions, Porter stemming algorithm, and NegEx package for detection of negative statements.

9. The correlation methods of claim 7, wherein sentence-level concurrence is a preferred statistical surrogate for detecting a relationship than larger portions of text.

10. The types of relationships of claim 8, including the hierarchies of Semantic Network of UMLS, composed of two types in level 1 of the hierarchy (‘

isa’ and

‘

associated_with’

), five types in level 2, thirty-four in level 3, and thirteen in level 4.

11. The search engine of claim 1, wherein said anaphoric terms and said sortal anaphoric noun phrases are resolved and identified, with a plurality of anaphora resolution methods.

12. The search engine of claim 1, wherein said relevance metric (numeric score) is computed using multiple relevance operators simultaneously, wherein all of the operators are incorporated by default, and all are used to define a numeric gradient of relevance in response to the submission of query terms by the user, without the user explicity requesting one or more of the operators.

13. The relevance operators of claim 12, including the following:

1) presence of query words;

2) presence of relationship between query words;

3) type of relationship;

4) type of semantic unit;

5) number and grouping of adjacent semantic units used for ascertainment of query word concurrences;

6) proximity of query words, measured by count of words separating them;

7) order of appearance of query words;

8) frequency of each query word occurring in the semantic unit;

9) Boolean operators; and

10) credence of the source of each record, quantified by measures including the ISI Impact Factor, sale rank, and count of refereed URL links.

14. The relevance metric of claim 12, wherein the retrieval process attains precision and recall approaching 100% and provides valid and reproducible comparisons for evaluating the completeness, accuracy, and usefulness of a result set for the given query provided by various systems.

15. The search engine of claim 1, wherein the computing hardware comprises one or more clusters of computer servers, wherein the databases and the applications are divided into tractable pieces, where each component is housed in a separate server, such that their cumulative effect reconstructs a single copy of said search engine.

16. The search engine of claim 1, further comprising implementation either as an internet-based application program or as a local computer-based application program.

17. The application program of claim 16, further comprising:

1) a first stage extraction from said data repository wherein said data records are scanned for relevance, and transmitted to the user'"'"'s computer;

2) a second stage extraction wherein the relevant articles are scanned by the local application.

18. The application program of claim 17, wherein either said first stage or said second stage can be performed individually or together.