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US20100241631A1 - Methods for indexing and retrieving information - Google Patents

Methods for indexing and retrieving information Download PDF

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US20100241631A1
US20100241631A1 US12661613 US66161310A US20100241631A1 US 20100241631 A1 US20100241631 A1 US 20100241631A1 US 12661613 US12661613 US 12661613 US 66161310 A US66161310 A US 66161310A US 20100241631 A1 US20100241631 A1 US 20100241631A1
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word
table
index
query
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Frank John Williams
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Frank John Williams
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20Handling natural language data
    • G06F17/27Automatic analysis, e.g. parsing

Abstract

A preferred method for providing an indexing methodology, an index table and method for retrieving information are disclosed. In a preferred method, an association between a plurality of word elements from a first data corpus such as a query is identified. Then, a preferred index table comprising additional information for identifying the associations between the several word elements of other data corpuses such as a data source is also disclosed, which in conjunction lead to retrieval of irrelevance-free information.

Description

    RELATED APPLICATIONS
  • [0001]
    This application claims the benefit of U.S. provisional patent application Ser. No. 61/210,396 filed 2009 Mar. 18 by the present inventor.
  • BACKGROUND
  • [0002]
    1. Field of Invention
  • [0003]
    The present invention relates generally to a method for retrieving information. More particularly, a novel method(s) for retrieving information implementing indexing information identifying an association between word elements.
  • [0004]
    2. Description of Related Art
  • [0005]
    The Revolution of the computer and the digital age are accountable for a series of inventions, communications and the transfer of knowledge including the storage of large amounts of valuable data upon which humanity sustains its progress. Many new scientific disciplines like Computational Linguistics and Natural Language Processing are born to study and understand some of the communication mediums such as natural languages. Regarded Intranets and Internet are built to distribute the valuable communication and knowledge to serve the specific information needs of millions of people every day. In particular, search engines are in charge of retrieving and delivering millions of documents to fulfill the specific needs of millions of people. However, current search technologies fail to effectively retrieve the information in a specific manner requiring its users to spend time and effort reading through large collections of text to find their particular or specific information needs. For example, a user looking to buy “red boots” may simply enter in the search engine the words “red boots.” The search engine then retrieves every document comprising the words “red” and “boots” producing data such as “red hat and yellow boots” which by having nothing to do with “red boots” it fails to serve or fulfills the specific wants of its user. As a result, users are forced to use valuable time and concentration in the efforts of focusing to sort and select through large quantities of relevant and irrelevant data which ultimately contributes to user confusion, frustration, discourage and loss of concentration.
  • [0006]
    In view of the present shortcomings, the present invention distinguishes over the prior art by providing heretofore a more compelling and effective method for retrieving specific information to allow search engines and other application the ability to remove irrelevant data from their results for better serving the needs of their users while providing additional unknown, unsolved and unrecognized advantages as described in the following summary.
  • SUMMARY OF THE INVENTION
  • [0007]
    The present invention teaches certain benefits in use and construction which give rise to the objectives and advantages described below. The methods and systems embodied by the present invention overcome the limitations and shortcomings encountered when retrieving information. The method(s) permits, through the use of a more compelling form of indexing methodology, a more accurate and precise form of massive information retrieval, which by implementing of associations between word elements, is capable of eliminating all the irrational and nonsensical data from user results.
  • OBJECTS AND ADVANTAGES
  • [0008]
    A primary objective inherent in the above described methods of use is to provide several methods and systems to index and identify the desired associations between words, thus allowing the method and systems to effectively reduce or remove the retrieval of irrelevant data not taught by the prior arts and further advantages and objectives not taught by the prior art. Accordingly, several objects and advantages of the invention are:
  • [0009]
    Another objective is to save user time by providing only conceptually matching data.
  • [0010]
    A further objective is to decrease the amount of effort implemented by users discriminating or sorting between relevant and irrelevant data.
  • [0011]
    A further objective is to improve the quality and quantity of results.
  • [0012]
    A further objective is to permit machines and application the ability of handling natural language more efficiently.
  • [0013]
    A further objective is to improve the ability of portable devices to manipulate natural language.
  • [0014]
    Another further objective is to permit the unification of the world's knowledge regardless of language and/or grammar.
  • [0015]
    Another further objective is to permit the retrieval of non-irrelevant data from large collections of information storage.
  • [0016]
    Other features and advantages of the described methods of use will become apparent from the following more detailed description, taken in conjunction with the accompanying drawings which illustrate, by way of example, the principles of the presently described apparatus and method of its use.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • [0017]
    The accompanying drawings illustrate examples of at least one of the best mode embodiments of the present method and methods of use. In such drawings:
  • [0018]
    FIG. 1 illustrates several exemplary non-limiting diagrams of some steps of the inventive method identifying and/or numbering the relationships between the elements of several exemplary data corpuses;
  • [0019]
    FIG. 2A is a non-limiting exemplary diagram of some steps of the inventive method displaying an index table, here introduced as Associative Index Table, which exploits the new idea of indexing the association between the different word elements;
  • [0020]
    FIG. 2B is another non-limiting exemplary diagram of some steps of the inventive method displaying another type of Associative Index Table, which in contrast to the index table from FIG. 2A, this type of table also uses the concept of sorting alphabetically the word elements from each association;
  • [0021]
    FIG. 3A is a non-limiting exemplary block diagram of some significant steps the inventive method handling a query and an index table for identifying information that matches the query and therefore needs to be retrieved;
  • [0022]
    FIG. 3B is another non-limiting exemplary block diagram of some significant steps the inventive method handling a query with several associations and an index table for identifying information that matches the associations in the query for retrieving matching information;
  • [0023]
    FIG. 3C is another non-limiting exemplary block diagram of a variation of some of the most significant steps the inventive method discussed in FIG. 3B manipulating group identifiers;
  • [0024]
    FIG. 3D is another non-limiting exemplary block diagram of a variation of some of the most significant steps the inventive method discussed in FIG. 3B manipulating other word elements such as eeggis;
  • [0025]
    FIG. 3E is another non-limiting exemplary block diagram of a variation of some of the most significant steps the inventive method, this time implementing an associative index table similar to that exemplified in FIG. 2B which uses alphabetically sorted indices;
  • [0026]
    FIG. 3F is yet another non-limiting exemplary block diagram of some significant steps the inventive method handling a query with several associations and an associative index table for identifying information that matches the associations of the said query for retrieving matching information;
  • [0027]
    FIG. 3G is yet another non-limiting exemplary block diagram of a variation of some of the most significant steps the inventive method discussed in FIG. 3B this time manipulating other word elements such as group identifiers instead of words;
  • [0028]
    FIG. 3H is yet another non-limiting exemplary block diagram of a variation of some of the most significant steps the inventive method discussed in FIG. 3B this time manipulating other word elements such as eeggis instead of words;
  • [0029]
    FIG. 3I is yet another non-limiting exemplary diagram of a variation of some of the most significant steps the inventive method this time involving the word elements from associations in no particular order;
  • [0030]
    FIG. 3J is yet another non-limiting exemplary block diagram of a variation of some of the most significant steps the inventive method implementing a combination of index tables separated from the documents own set of relations;
  • [0031]
    FIG. 4A is a non-limiting block diagram of some of the steps of the inventive method discussed in FIG. 1 and FIG. 2A for producing or providing an indexing table for finding and/or comparing and/or retrieving matching information;
  • [0032]
    FIG. 4B is a non-limiting block diagram of some of the steps of the inventive method discussed in FIG. 1 and FIG. 2B for producing or providing an indexing table for finding and/or comparing and/or retrieving matching information;
  • [0033]
    FIG. 5A is a non-limiting block diagram of some of the main steps of the inventive method displayed in FIGS. 3A, 3B, 3C and 3D for retrieving information;
  • [0034]
    FIG. 5B is yet another non-limiting block diagram of some of the main steps of the inventive method displayed in FIGS. 3E, 3F, 3G and 3H for retrieving information;
  • [0035]
    FIG. 6 is a non-limiting block diagram of the some steps of the inventive method exploring a more general view of the steps mentioned in FIG. 5A and FIG. 5B.
  • DETAILED DESCRIPTION
  • [0036]
    The above described drawing figures illustrate the described methods and use in at least one of its preferred, best mode embodiment, which are further defined in detail in the following description. Those having ordinary skill in the art may be able to make alterations and modifications from what is described herein without departing from its spirit and scope. Therefore, it must be understood that what is illustrated is set forth only for the purposes of example and that it should not be taken as a limitation in the scope of the present system and method of use.
  • [0037]
    FIG. 1 illustrates several exemplary non-limiting diagrams of some steps of the inventive method identifying and/or numbering the relationships between the elements of several exemplary data corpuses that were found or attained by an Associative Analysis Protocol such as CIRN. Noteworthy, CIRN discovers and forms associations between the different word elements of a given and/or analyzed data corpus, such as associating the nouns with their verbs, etc. The First Data Corpus 1010 (FIG. 1) or sentence “red boots and black hats” is displayed with its corresponding First Table of Relations 1015 (FIG. 1) which contains the relationships found, formed or desired between the words or the elements of the said first sentence. For example, in the First Table of Relations, the top row displays the word “red” (under column Word 1) next to the word “boots” (under column Word2) along with their association number or “1” (under column Association Number or “Assoc. No.” for short). In the bottom row, the word “black” (under column Word1), the word “hats” (under column Word2) and their association number “2” (under column Assoc. No.) are all displayed together. In such fashion, each of the two associations (“red—boots” and “black—hats”) that were formed or found in the first sentence is uniquely identified, differentiated and/or numbered. The Second Data Corpus 1020 (FIG. 1) or “Mary ran quickly” illustrates its corresponding table of relationships or Second Table of Relations 1025 (FIG. 1). In this table, the top row associates the word “Mary,” the word “ran” and their association number “15.” In similar fashion, the bottom row associates the word “ran,” the word “quickly” and their association number “16.” As a result, each of the relations formed/found in the second sentence is uniquely numbered or identified. The Third Data Corpus 1030 (FIG. 1) or sentence “silly kitty jumps” illustrates its corresponding table of relationships or Third Table of Relations 1035 (FIG. 1). In this third table, the top row associates the word “silly,” the word “kitty” with their association number “R17;” wherein R17 is the information responsible for identifying the association or relationship between “silly” and “kitty.” In similar fashion, the bottom row display the word “kitty,” the word “jumps” and their association number “M81.” As a result, each of the relationships formed/found in the third sentence is uniquely numbered or identified. Please note, in this particular example, the information identifying each of the associations is not in series but rather in random order or appearance. The Fourth Data Corpus 1040 (FIG. 1) is a sentence made of group identifiers or “adj333 nou112 ver777” which in English spells the sentence “silly kitty jumps” along with its corresponding table of relationships or Fourth Table of Relations 1045 (FIG. 1). In this table, the top row associates the group identifiers adj333 (silly), nou112 (kitty), with “6;” wherein “6” is the information identifying their unique association. The bottom row associates another set of group identifiers or nou112 (kitty), ver777 (jumps) with number “12” which is the information identifying their unique association. As a result, each of the relationships found, formed or desired from the second sentence is uniquely numbered, identified and/or differentiated. The Fifth Data Corpus 1050 (FIG. 1) is another sentence which this time is made of eeggis or “adj33.1 nou11.4 ver77.1” which in English spells the sentence “silly kitty jumps.” The Fifth Table of Relations 1055 (FIG. 1) illustrates the associations that were found, formed or desired between the eeggis of the said fifth sentence. In this table, the top row associates the eeggis adj33.1 (silly), nou11.4 (kitty), with their association number “50.” The bottom row displays another association between another group of eeggis or nou11.4 (kitty), ver77.1 (jumps) with number “18” which happens to be the information identifying their unique association. As a result, each eeggi association has its unique identification number within the Fifth Data Corpus. Please note, in this example or table of relations, although the associations happened next to each other, the information (numbers) identifying the said associations are not continuous or in series but are rather in random order.
  • [0038]
    FIG. 2A is a non-limiting exemplary diagram of some steps of the inventive method displaying an index table, here introduced as Associative Index Table, which exploits the new idea of indexing the associations between the different word elements of a given data corpus (at least two word elements are associated in the index table). This novel type of table is not only indexing word elements, but is also indexing the associations the word elements experience in the different data corpuses. The set of Data Corpuses 2010 (FIG. 2A) comprises three exemplary documents or sentences such as the first sentence or “[1] red boots and black hats,” the second sentence or “[2] black boots and red hats” and the third sentence or “[3] black hats and red boots.” Beneath it, is the Associative Index Table 2050 (FIG. 2A) displaying several rows (1-8) and columns (Word1, Word2 and Page No.). In such fashion, the Associative Index Table in every row associates two word elements in addition of providing the information identifying their corresponding Data Corpus or Page Number (Page No.). For example, in the Associative Index Table 2050 (FIG. 2A) the seventh row illustrates or discloses that the word “red” experiences an association with the word “boots” and said association is present or can be found in pages 1 and 3 ([1] and [3]). Another example, the eighth or last row, informs that the word “red” experiences an association with the word “hats” and that both words are associated in page number 2 or [2].
  • [0039]
    FIG. 2B is another non-limiting exemplary diagram of some steps of the inventive method displaying another type of Associative Index Table, which in contrast to the index table from FIG. 2A, this type of table also uses the concept of sorting alphabetically the word elements from each association. The set of Data Corpuses 2010 (FIG. 2B) comprises three exemplary documents, pages or sentences such as the first sentence or “[1] red boots and black hats,” the second sentence or “[2] black boots and red hats” and the third sentence or “[3] black hats and red boots.” Beneath it, is the Associative Index Table 2051 (FIG. 2B) which this time displays or has four rows (1-4) and columns (Word1, Word2 and Page No.). Particular to this type of associative indexing table, the word elements of each association or row are arranged alphabetically starting with the first word element, or word element with the first alphabetical order, and continuing with the second word element or element with the last alphabetical order. In such fashion, every row of the Associative Index Table displays the word elements of every known or identified association along with their location or Page Number (Page No.). This kind of Associative Index Table, in comparison to the associative index table of FIG. 2A, reduces or removes the need to repeat indices or words in the table, thus reducing its overall size. For example, the Associative Index Table 2051 (FIG. 2B) informs that in its third row, the word “boots” which experiences an association with the word “red” (or vice versa) can be found in pages 1 and 3 ([1] and [3]). In such fashion, when a query or other requires the retrieval of the association of “red” with “boots,” the word element with the first alphabetical order, in this case “boots,” can be used to find said word elements and their association in the Associative Index Table. Another example, the fourth or last row, the word “hats” and the word “red” experience an association in page number 2 or [2]. Please note, like in this example that uses the method of alphabetically ordering the word elements from first to last in the indexing table, there are a myriad of parameters, arrangements and ordering preferences that can be utilized for creating, forming and/or arranging an associative index table without ever departing from the main idea, scope and spirit of the inventive table and implementing methods.
  • [0040]
    FIG. 3A is a non-limiting exemplary block diagram of some significant steps of the inventive method handling a query and an associative index table for identifying information that matches the query and the corresponding retrieval of data. The Query 3010 (FIG. 3A) comprises the phrase or sentence “red hats.” The Associative Procedure 3020 (FIG. 3A) such as CIRN (Conceptual Interrelating Network Protocol) identifies if a relationship is present or is possible between any of the words of the phrase or sentence “red hats.” Please note, there are a variety of methodologies or protocols such as different types of CIRN that are available for creating, forming, producing and/or identifying associations (desired or not) between the different word elements from several kinds of data corpuses, such as those data corpuses using only text, words, group identifiers, eeggis, sounds, etc. In this example, the Query Table of Relations 3030 (FIG. 3A) illustrates that a single relationship is attained between the word elements “red” and “hats” of the Query. Consequentially, any such document(s) wherein the word “red” and “hats” are associated will represent a match. Next, the Associative Index Table 2050 (FIG. 3A), similar to the index page discussed in FIG. 2A, provides the information needed to allocate or find those pages or data corpuses wherein “red” and “hats” are indeed related/associated as implied by their row. For example, in the Associative Index Table, the fifth row indicates that “hats” and “black” both experience an association in pages 1 and 3 (in data corpuses [1] and [3]). In similar fashion, the last or eighth row of the Associative Index Table indicates that the word “red” and the word “hats” are related or experience and association in data corpus [2] or page 2. The Match Table 3070 (FIG. 3A) is a summary of the Associative Index Table with all the pages or documents that matched the query. For example, the Match Table indicates that the eighth row from the Associative Index Table produces a match to the query, and that the same association between “red” and “hats” can be found in data corpus [2] or the second page. As a result, the second data corpus or second page is retrieved or displayed as indicated by the Results Display 3090 (FIG. 3A). Please note, the Match Table 3070 (FIG. 3A) is used to illustrated the matches found and to aid or help the teaching of the present inventive method.
  • [0041]
    FIG. 3B is another non-limiting exemplary block diagram of some significant steps of the inventive method handling a query with several associations and an associative index table for identifying information that matches the associations of the query for retrieving matching information. The Query 3010 (FIG. 3B) comprises the phrase or sentence “black hats and red boots.” The Associative Procedure 3020 (FIG. 3B) such as CIRN (Conceptual Interrelating Network Protocol) identifies if a relationship(s) is present or is possible between several groups of words from the phrase or sentence “black hats and red boots.” Please note, there are a variety of methodologies or protocols such as different types of CIRN that are available for creating, forming, producing and/or identifying associations (desired or not) between the different word elements from several kinds of data corpuses, such as those data corpuses using only text, words, group identifiers, eeggis, sounds, etc. In this particular example, the Query Table of Relations 3030 (FIG. 3B) illustrates two different sets of relationships attained from the word elements of the Query. For example, “black” associates with “hats” and “red” associates with “boots.” Consequentially, the retrieval operation will involve any document(s) wherein the word “black” is associated with “hats” and wherein the word “red” is associated with the word “boots.” Next, the Associative Index Table 2050 (FIG. 3B) provides information that is needed to allocate or find those pages (data corpuses) matching the query's elements and associations. For example, in the Associative Index Table, the fourth row indicates that “boots” and “red” are associated in pages 1 and 3. In similar fashion, the last or eighth row in the Index Table indicates that the word “red” and “hats” are associated in the data corpus [2] or page 2. The Query Table of Relations 3030 (FIG. 3B) specifically requires that two sets of associations need to be matched (“red—boots” and “black—hats”). Inspecting the Associative Index Table we can see that the first row has the first associative set of the query or “black” with “hats,” and that the seventh row has the second associative set or “red” with “boots.” As a result, the Match Table 3070 (FIG. 3B) illustrates both set of matches, clearly identifying their pages, which in this particular case are in both cases pages 1 and 3. Consequentially, the Results Display 3090 (FIG. 3B) displays the matching records or pages [1] and [3]. Noteworthy, the Match Table 3070 (FIG. 3B) operates as an aid to help visualized the matching data obtained on the Associative Index Table.
  • [0042]
    FIG. 3C is another non-limiting exemplary block diagram of a variation of some of the most significant steps the inventive method discussed in FIG. 3B this time manipulating other word elements such as group identifiers instead of words. In this example, the Query 3010 (FIG. 3C) comprises the group identifier sentence “aj88 no44+aj99 no33” which in English spells or means “black hats and red boots.” The Associative Procedure 3020 (FIG. 3C) such as CIRN (Conceptual Interrelating Network Protocol) identifies any relationships present or possible between several groups of group identifiers from the phrase or sentence in the Query. Please note, the CIRN protocols in this example are designed to handle the involving group identifiers. The Query Table of Relations 3030 (FIG. 3C) illustrates the resulting two relationships that were found or obtained from the Query. For example, “aj88” (black) relates to “no44” (hats) while “aj99” (red) relates “no33” (boots). In such fashion, any document(s) wherein “aj88” and “no44” relate and wherein “aj99” and “no33” relate too will represent a match of the query. The Associative Index Table 2050 (FIG. 3C) provides information needed to retrieve matching data. For example, in the Associative Index Table, the fifth row shows that “no44” and “aj88” are associated in pages [1] and [3]. Carefully inspecting the matches from the Query (“aj88-no44” and “aj99-no33”) in the Associative Index Tables we can see that the first and the seventh rows have the same sets of group identifiers experiencing the same relations as those found/formed in the Query. As a result, the Match Table 3070 (FIG. 3C) illustrates the matching data from the Associative Index Table; wherein both associations are simultaneously present in two different data corpuses or pages [1] and [3]. Consequentially, pages 1 and 3 are retrieved as indicated in the Results Display 3090 (FIG. 3C) displaying each page of group identifiers with their corresponding English parallel or translation.
  • [0043]
    FIG. 3D is another non-limiting exemplary block diagram of a variation of some of the most significant steps the inventive method discussed in FIG. 3B this time manipulating other word elements such as eeggis instead of words. In this example, the Query 3010 (FIG. 3D) comprises the eeggi sentence “aj8.1 no4.0+aj9.5 no3.2” which in English spells or means “black hats and red boots.” The Spectrum Modifier 3015 (FIG. 3D), depending on synonym selection, modifies the eeggis of the query, such as converting or reducing “aj8.1” to its root eeggi or spectrum “aj8.” which has no decimals. In such fashion, any synonym or eeggi in the aj8.xx region will be equally treated. The Associative Procedure 3020 (FIG. 3D) such as CIRN (Conceptual Interrelating Network Protocol) identifies any relationships present or possible between several groups of eeggis or eeggi regions from the phrase or sentence in the Query. Noteworthy, CIRN protocols in this example are designed to handle the involving word elements “eeggis.” In The Query Table of Relations 3030 (FIG. 3D) illustrates the resulting two relationships that were found or obtained from the Query. For example, “aj8.” (black and synonyms of black) relates to “no4.0” (hats and synonyms of hats) while “aj9.5” (red and synonyms of red such as crimson) relates “no3.2” (boots and synonyms of boots). In such fashion, any document(s) wherein “aj8.” and “no4.” relate and wherein “aj9.” and “no3.” relate too, will represent a match to the query. The Associative eeggi Index Table 2050 (FIG. 3D) provides information needed to retrieve matching data. For example, in the Associative Index Table, the fifth row shows that eeggis “no4.” and “aj8.1” are associated in pages [1] and [3]. Carefully inspecting the matches from the Query (“aj8.-no4.” and “aj9.-no3.”) in the Associative eeggi Index Tables we can see that the first and the seventh rows have the same sets of eeggis or eeggi spectrums experiencing the same relations/associations as those found or formed in the Query. As a result, the Match Table 3070 (FIG. 3D) illustrates the matching data from the Associative eeggi Index Table; wherein both associations are simultaneously present in two different data corpuses or pages [1] and [3]. Consequentially, pages 1 and 3 are retrieved as indicated in the Results Display 3090 (FIG. 3D) displaying each page of eeggis with their corresponding English parallel or translation. Please note, pages [1] and [3] in the results involve the word “crimson” instead of “red” as in the Query. This is because “crimson” (aj9.7) and “red” (aj9.5) both share the same eeggi spectrum or “aj9.”
  • [0044]
    FIG. 3E is another non-limiting exemplary block diagram of a variation of some of the most significant steps the inventive method, this time implementing an associative index table similar to that exemplified in FIG. 2B which uses alphabetically sorted indices. The Query 3010 (FIG. 3E) comprises the phrase or sentence “red hats.” The Associative Procedure 3020 (FIG. 3E) such as CIRN (Conceptual Interrelating Network Protocol) identifies if a relationship(s) is present or possible between the word elements or in this particular example, words, from the phrase or sentence “red hats.” Please note, there are a variety of methodologies or protocols such as different types of CIRN that are available for forming, producing and/or identifying associations (desired or not) between the different word elements from several kinds of data corpuses, such as those data corpuses using only text, words, group identifiers, eeggis, sounds, etc. As a result from said associative analysis, the Query Table of Relations 3030 (FIG. 3E) illustrates a relationship from the Query which happens to be between the word “red” and “hats.” Next, the Sorting Procedure 3035 (FIG. 3E) arranges or alphabetically sorts, from first to last, the words of the desired association, thus resulting in the Sorted Query 3040 (FIG. 3E). Please note, alphabetically speaking, “hats” is before “red,” thus the reason for the new arrangement. Consequentially, the retrieval operation will involve any document(s) wherein the word “hats” is associated with “red.” Next, the Associative Index Table 2051 (FIG. 3E) provides the information needed to allocate or find the matching pages (data corpuses) of the Sorted Query. For example, in the Associative Index Table, the fourth or last row indicates that “hats” and “red” are associated in page 2 or [2] and the third row indicates that “boots” and “red” are associated under data corpuses [1] and [3]. By carefully inspecting the Associative Index Table we can see that indeed the fourth or last row has the same word elements and sorted associations as the Sorted Query. As a result, the Match Table 3070 (FIG. 3E) aids to illustrate that it is in page 2 wherein the words “hats” and “red” can be found being associated. Consequentially, the Results Display 3090 (FIG. 3E) displays the record or page [2].
  • [0045]
    FIG. 3F is yet another non-limiting exemplary block diagram of some significant steps the inventive method handling a query with several associations and an associative index table for identifying information that matches the associations of the said query for retrieving matching information. The Query 3010 (FIG. 3F) comprises the phrase or sentence “black hats and red boots.” The Associative Procedure 3020 (FIG. 3F) such as CIRN (Conceptual Interrelating Network Protocol) identifies if a relationship(s) is present or possible between several groups of words from the phrase or sentence “black hats and red boots.” Please note, there are a variety of methodologies or protocols such as different types of CIRN that are available for forming, producing and/or identifying associations (desired or not) between the different word elements from several kinds of data corpuses, such as those data corpuses using only text, words, group identifiers, eeggis, sounds, etc. In this particular example, the Query Table of Relations 3030 (FIG. 3F) illustrates the two different sets of relationships from the Query that were found, thanks to CIRN, between the different words. For example, “black” associates with “hats” and “red” associates with “boots.” Next, Sorting Procedure 3035 (FIG. 3F) arranges the elements accordingly to its sorting criteria, or as in this example, alphabetically. As a result, the Sorted Query 3040 (FIG. 3F) illustrates the arranged sets of associations that are required for the retrieval of information. In such fashion, the retrieval operation will involve any document(s) wherein the word “black” is associated with “hats” and wherein the word “boots” is associated with the word “red.” Next, the Associative Index Table 2051 (FIG. 3F) provides information that is needed to allocate or find those pages (data corpuses) matching the query's word elements and corresponding associations. For example, in the Associative Index Table, the fourth or last row indicates that “hats” and “red” are associated in page number 2. Paying close attention to the Associative Index Table, we can see that the first row has a first set identical to that of the Sorted Query or “black” with “hats” in pages [1] and [3], and that the third row has the second set or association between “boots” and “red” also in pages [1] and [3]. As a result, the Match Table 3070 (FIG. 3F) illustrates both set of matches, clearly identifying their pages 1 and 3. Consequentially, both pages (1 and 3) have all the matching words and associations between words. As a result, the Results Display 3090 (FIG. 3B) displays the matching records or pages [1] and [3]. Noteworthy, the Match Table 3070 (FIG. 3F) operates as an aid to help visualized the matching data obtained from the Associative Index Table and corresponding page retrieval.
  • [0046]
    FIG. 3G is yet another non-limiting exemplary block diagram of a variation of some of the most significant steps the inventive method discussed in FIG. 3B this time manipulating other word elements such as group identifiers instead of words. In this example, the Query 3010 (FIG. 3G) comprises the group identifier sentence “aj88 no44+aj99 no33” which in English spells or means “black hats and red boots.” The Associative Procedure 3020 (FIG. 3G) such as CIRN (Conceptual Interrelating Network Protocol) identifies any relationships present or possible between several groups of group identifiers from the phrase or sentence in the Query. Please note that the CIRN protocols in this example are designed to handle the involving group identifiers. The Query Table of Relations 3030 (FIG. 3G) illustrates the resulting two relationships that were found or obtained from the Query. For example, “aj88” (black) relates to “no44” (hats) while “aj99” (red) relates “no33” (boots). Next, the Sorting Procedure 3035 (FIG. 3G), following its particular sorting protocol, arranges and/or prepares the relations of the Query Table of Relations to be properly identified in the Associative Index Table of group identifiers. Please note, since the sorting protocol in this example exploits the format of sorting the group identifiers in descending alphabetical order, the order of the group identifiers in each association still remains unchanged. In such fashion, any document(s) wherein “aj88” and “no44” relate and wherein “aj99” and “no33” relate do represent a match. The Associative Index Table 2051 (FIG. 3G) provides information needed to retrieve any matching data. For example, in the Associative Index Table, the fourth or last row shows that “aj99” and “no44” are associated in page number 2 or [2]. Therefore, upon careful inspection of the matches from the Sorted Query (“aj88-no44” and “aj99-no33”) in the Associative Index Tables we can see that the first and third rows have the same sets of group identifiers experiencing the same relations. As a result, the Match Table 3070 (FIG. 3G) illustrates the index information for retrieving matching data, which is this example, points to pages [1] and [3]. Consequentially, pages [1] and [5] are retrieved as indicated in the Results Display 3090 (FIG. 3G) displaying each page of group identifiers with their corresponding English parallel or translations.
  • [0047]
    FIG. 3H is yet another non-limiting exemplary block diagram of a variation of some of the most significant steps the inventive method discussed in FIG. 3B this time manipulating other word elements such as eeggis instead of words. In this example, the Query 3010 (FIG. 3H) comprises the eeggi sentence “aj8.1 no4.0+aj9.5 no3.2” which in English spells or means “black hats and red boots.” The Spectrum Modifier 3015 (FIG. 3H) modifies the eeggis of the sentence into their corresponding eeggi spectrums. In such fashion, an eeggi such as “aj9.5” (red) is converted to “aj9.” to also identify other synonyms such as “aj9.7” (crimson). The Associative Procedure 3020 (FIG. 3H) such as CIRN (Conceptual Interrelating Network Protocol) identifies any relationships present or possible between several groups of group identifiers from the phrase or sentence in the Query. Please note the CIRN protocols in this example are designed to handle the involving eeggis. The Query Table of Relations 3030 (FIG. 3H) illustrates the resulting two relationships that were found or obtained from the Query. For example, “aj8.” (black and synonyms of black) relates to “no4.” (hats and synonyms of hats) while “aj9.” (red and synonyms of red such as crimson) relates “no3.” (boots and synonyms of boots). Next, the Sorting Procedure 3035 (FIG. 3G), following its particular sorting protocol, arranges and/or prepares the relations of the Query Table of Relations to be properly identified in the Associative Index Table. Please note, since the sorting protocol in this example exploits the format of sorting the group identifiers in descending alphabetical order, the order of the eeggis in each association still remains unchanged. In such fashion, any document(s) wherein “aj8.” and “no4.” relate and wherein “aj9.” and “no3.” relate do represent a match. The Associative Index Table 2051 (FIG. 3H) specifically designed to identified numerically sorted eeggis, provides the information needed to retrieve any matching data. For example, in the Associative Index Table, the fourth or last row shows that “aj9.5” (red) and “no4.0” (boots) associated in page number 2 or [2]. Therefore, upon careful inspection of the matches from the Sorted Query (“aj8.-no4.” and “aj9.-no3.”) on the Associative Index Table we can see that the first and third rows have the same sets of eeggi spectrums experiencing the same relations/associations. As a result, the Match Table 3070 (FIG. 3H) illustrates the index information for retrieving matching data, which is this example, points to pages [1] and [3]. Consequentially, pages [1] and are retrieved as indicated in the Results Display 3090 (FIG. 3H) displaying each page of eeggis with their corresponding English parallel or translations. Please note, pages [1] and [3] in the results involve the word “crimson” instead of “red” as in the Query. This is because “crimson” (aj9.7) and “red” (aj9.5) both share the same eeggi spectrum or “aj9.”
  • [0048]
    FIG. 3I is yet another non-limiting exemplary diagram of a variation of some of the most significant steps the inventive method this time involving the word elements from associations in no particular order. In other words, matches in the index tables need to have all words elements of the associations regardless of order. In this example, the Query 3010 (FIG. 3I) comprises the sentence “black hats and red boots.” The Associative Procedure 3020 (FIG. 3I) such as CIRN (Conceptual Interrelating Network Protocol) identifies any relationships present or possible between several words from the phrase or sentence in the Query. The Query Table of Relations 3030 (FIG. 3I) illustrates the resulting two relationships that were found or obtained from the Query. For example, “black” relates/associates to “hats” while “red” relates/associates to “boots.” In such fashion, any document(s) wherein “black” and “hats” relate and wherein “red” and “boots” relate will represent a match of the query. The Associative Index Table 2050 (FIG. 3I) provides information needed to retrieve any matching data. For example, in the Associative Index Table, the second row shows that “black” and “boots” are associated in page [2]. Then, upon careful inspection of the words and associations from the Query in the Associative Index Tables we can see that the first and the third rows have the same sets of words experiencing the same relations among them. Please note that in this example, the order of the words in the query and order of words in the index table are of no importance. In such fashion, as long all words and association among said words from the query can be found under the same index record (index record involves same words) the documents identified by said index table are a match. Accordingly, the Match Table 3070 (FIG. 3I) illustrates the matching data from the Associative Index Table; wherein both associations are simultaneously present in two different data corpuses or pages [1] and [3]. Consequentially, pages 1 and 3 are retrieved as indicated in the Results Display 3090 (FIG. 3I).
  • [0049]
    FIG. 3J is yet another non-limiting exemplary block diagram of a variation of some of the most significant steps the inventive method implementing a combination of index tables separated from the documents own set of relations. In this example, the Query 3010 (FIG. 3J) comprises the English sentence “black boots and red hats.” The Associative Procedure 3020 (FIG. 3J) such as CIRN (Conceptual Interrelating Network Protocol) identifies any relationships present or possible between several groups words from the phrase or sentence of the Query. Please note the CIRN protocols in this example are designed to handle the involving eeggis. The Query Table of Relations 3030 (FIG. 3J) illustrates the resulting two relationships that were found or obtained from the Query. For example, “black” relates to “hats” while “red” relates to “boots.” Next, the Index Table 3050 (FIG. 3J) provides some of the information needed to find or retrieve any matching data. For example, in the Index Table, the fourth or last row shows that “boots” can be found in pages [1], [2] and [3], in such fashion, any queries of the word “boots” imply the retrieval of pages [1], [2] and [3]. Therefore, upon careful inspection of the word of the Query Table of Relations, we can see that pages [1], [2] and [3] have all the words needed. Next, the Source of Data 3070 (FIG. 3J) depicts every document or page in addition to the associations that each page has. For example, the First Page File 3071 (FIG. 3J) in the Source of Data, displays its content “red boots and black hats” along with the associations (if any) that any of its words experiences. In this First Page File, “red” is associated to “boots” while “black” is associated to “hats.” The Second Page File 3072 (FIG. 3J) displays its page content or “black boots and red hats” along with the associations the words of the page have; which in this example involves “black” having a relation with “boots” and “red” having a relation with “hats.” Finally, the Third Page File 3073 (FIG. 3J) displays its page content or “black hats and red boots” with their corresponding associations; wherein “black” associates with “hats” and “red” associates with “boots.” Consequentially, inspecting the associations and words of the Query (or Query table of Relations) with the words of the pages in the source of Data that experience the same words and associations, we can see that only page [2] (The Second Page File) offers identical words and associations among them. As a result, the Results Display 3090 (FIG. 3J) displays the only matching page [2] or “black boots and red hats.”
  • [0050]
    FIG. 4A is a non-limiting block diagram of some of the steps of the inventive method discussed in FIG. 1 and FIG. 2A for producing or providing an indexing table for finding and/or comparing and/or retrieving matching information. The First Step 4010 (FIG. 4A) involves the step of identifying a First Word Element (a word element is an information identifying at least one of a: word, concept, idea, meaning, image and grammatical element) in a Data Corpus. For example, in a data corpus such as a query with three word elements, one of the word elements is identified or selected. The Second Step 4020 (FIG. 4A) involves the step of identifying another or Second Word Element in the said Data Corpus. For example, from the query in the First Step which identified one element, in this second step another word element from the remaining two elements is identified or selected. The next or Third Step 4030 (FIG. 4A) involves the step of identifying and/or finding an association between said First Word Element and said Second Word Elements through the use of an associative protocol such as CIRN. For example, CIRN (Conceptual Inter-relating Network Protocols) identifies and/or forms associations between different types of word elements of a particular data corpus. In such fashion, a sentence such as “fat cats ran” when analyzed by CIRN will find or form associations between “fat” and “cats” and also find or form another association between “cats” and “ran.” The next of Fourth Step 4040 (FIG. 4A) involves the obvious step of associating the First Word Element, Second Word Element and information identifying the data corpus wherein the association between both elements occurs. For example, in the sentence or data corpus “fat cats ran” two associations are identified. As a result, each of the associations is formed including the information identifying their corresponding data corpus. The next or Fifth Step 4050 (FIG. 4A) involves the next obvious step of registering all the information necessary for effectively identifying the word elements, their association and their data corpus where they are found. For example, on indexing tables of the current art, every word in its index table has an information identifying its source document(s) or page(s) (where the word is found). In such fashion, search engines can quickly retrieve those documents comprising the word of the query. However, in this disclosed inventive index table, at least two words experiencing any particular association are used along with the information for identifying the documents or pages containing them (document comprising both words been associated).
  • [0051]
    FIG. 4B is a non-limiting block diagram of some of the steps of the inventive method discussed in FIG. 1 and FIG. 2B for producing or providing an indexing table for finding and/or comparing and/or retrieving matching information. The First Step 4010 (FIG. 4B) involves the step of identifying a First Word Element (a word element is an information identifying at least one of a: word, concept, idea, meaning, image and grammatical element) in a Data Corpus. For example, in a data corpus such as a query with four word elements, one of the word elements is identified or selected. The Second Step 4020 (FIG. 4B) involves the step of identifying another or Second Word Element in the said Data Corpus. For example, from the query in the First Step which identified one element, in this second step another word element from the remaining three elements is identified or selected. The next or Third Step 4030 (FIG. 4B) involves the step of identifying and/or finding an association between said First Word Element and said Second Word Elements through the use of an associative protocol such as CIRN. For example, CIRN (Conceptual Inter-relating Network Protocols) identifies and/or forms associations between different types of word elements of a particular data corpus. In such fashion, a sentence such as “fat cats and silly dogs” when analyzed by CIRN will find or form associations between “fat” and “cats” and also find or form another association between “silly” and “dogs.” The next of Fourth Step 4040 (FIG. 4B) involves the obvious step of associating the First Word Elements, Second Word Element and information identifying the data corpus wherein the association occurs. For example, in the sentence or data corpus “fat cats and silly dogs” two associations are identified. As a result, each of the associations is formed including the information identifying their corresponding data corpus. The next or Fifth Step 4050 (FIG. 4B) involves the step of sorting or arranging the word elements of the found or desired associations into a particular order or particular sequence. For example, in an association such as “fat” and “cats,” sorting the elements in ascending alphabetical order, results in placing the word “cats” first and the word “fat” as second. The next or Sixth Step 4060 (FIG. 4B) involves the obvious step of registering the information necessary for effectively forming the Associative Index'Table such as identifying the word elements, their association and their data corpus where they are found. For example, in indexing tables of the current art, every word in its index table has or uses an information for identifying the document(s) or page(s) wherein the word is present. In such fashion, search engines can quickly retrieve those documents comprising the word of the query. However, in this disclosed Associative Index Table, at least two words experiencing any particular association are sorted and registered along with the information for identifying the documents or pages wherein at least said both words are indeed present
  • [0052]
    FIG. 5A is a non-limiting block diagram of some of the main steps of the inventive method displayed in FIGS. 3A, 3B, 3C and 3D for retrieving information. The First Step 5010 (FIG. 5A) involves the step of identifying an association between a plurality of word elements from a first data corpus such as a query. For example, after analyzing a query such as “red hats” it is found that an association exists between the words “red” and “hats.” The next or Second Step 5020 (FIG. 5A) involves the step of identifying a first word element from said first data corpus or said association. For example, from the query “red hats” the word “red” is identified or selected. The next or Third Step 5030 (FIG. 5A) involves the step of searching an index table comprising at least one of a: said first word element, information identifying a data corpus comprising said first word element, and information identifying a second word element from said association. For example, from the query “red hats,” the word “red” is searched and/or found in an index table also identifying that word “hats” and also identifying at least one document wherein both “red” and “hats” are associated. In such fashion, looking up for the word “red” or “hats” on the index table, it provides information wherein the set of words are associated. The next or Fourth Step 5040 (FIG. 5A) involves the obvious step of identifying a match in said index table. For example, looking up on the index table, a match of associations between “red” and “hats” is found identical to that of “red” and “hats” in the query. The next or Fifth Step 5050 (FIG. 5) involves the next obvious step of identifying a second data corpus identified by said identified match from said index table. For example, the index table identifies documents X and Y to have identical associations between the same elements as the query. The final or Sixth Step 5060 (FIG. 5A) involves the step of retrieving the said identified second data corpus. For example, displaying or retrieving document X and Y as a match to the word elements of the query.
  • [0053]
    FIG. 5B is yet another non-limiting block diagram of some of the main steps of the inventive method displayed in FIGS. 3E, 3F, 3G and 3H for retrieving information. The First Step 5010 (FIG. 5B) involves the step of identifying an association between a plurality of word elements from a first data corpus such as a query. For example, after analyzing a query such as “red hats” it is found that an association exists or is being identified between the words “red” and “hats.” The next or Second Step 5020 (FIG. 5B) involves the step sorting the word elements from said association. For example, the word associated word elements “red” and “hats” are sorted or arranged in a particular and/or desired order such as arranging them in alphabetical descending order. As a result, the word “hats” is first or before the word “red” since “red” begins with the letter “r.” The next or Third Step 5030 (FIG. 5B) involves the simple step of identifying a word from said sorting event. For example, selecting the first word of the sorting process. In such fashion, an index table can be search for finding the elements of the query. The next or Fourth Step 5040 (FIG. 5B) involves the step of searching an index table comprising at least one of a: said first word element, information identifying a data corpus comprising said first word element, and information identifying a second word element from said association. For example, from the query “red hats,” the word “red” is searched and/or found in an index table also identifying that word “hats” and also identifying at least one document wherein said both “red” and “hats” are associated. In such fashion, looking up for the word “red” or “hats” on the index table, it provides information wherein the set of words are associated. The next or Fifth Step 5050 (FIG. 5B) involves the obvious step of identifying a match in said index table. For example, looking up on the index table, a match of the query's associations between “red” and “hats” is found. The next or Sixth Step 5060 (FIG. 5B) involves the next obvious step of identifying a second data corpus identified by said identified index table. For example, identifying documents X and Y of having the same associations as the query. The final or Seventh Step 5070 (FIG. 5B) involves the obvious step of retrieving the said second data corpus. For example, displaying, producing or retrieving documents X and Y as a resulting match to the query.
  • [0054]
    FIG. 5C is yet another non-limiting block diagram of some of the main steps of the inventive method displayed in FIG. 3J for retrieving information. The First Step 5010 (FIG. 5C) involves the step of identifying an association between a plurality of word elements from a first data corpus such as a query. For example, after analyzing a query such as “red hats” it is found that an association exists or is being identified between the words “red” and “hats.” The next or Second Step 5020 (FIG. 5B) involves the step of sorting the word elements from said association. For example, the word associated word elements “red” and “hats” are sorted or arranged in a particular and/or desired order such as arranging them in alphabetical descending order. As a result, the word “hats” is first or before the word “red” since “red” begins with the letter “r.” The next or Third Step 5030 (FIG. 5B) involves the simple step of identifying a word from said sorting event. For example, selecting the first word of the sorting process. In such fashion, an index table can be search for finding the elements of the query. The next or Fourth Step 5040 (FIG. 5B) involves the step of searching an index table comprising at least one of a: said first word element, information identifying a data corpus comprising said first word element, and information identifying a second word element from said association. For example, from the query “red hats,” the word “red” is searched and/or found in an index table also identifying that word “hats” and also identifying at least one document wherein said both “red” and “hats” are associated. In such fashion, looking up for the word “red” or “hats” on the index table, it provides information wherein the set of words are associated. The next or Fifth Step 5050 (FIG. 5B) involves the obvious step of identifying a match in said index table. For example, looking up on the index table, a match of the query's associations between “red” and “hats” is found. The next or Sixth Step 5060 (FIG. 5B) involves the next obvious step of identifying a second data corpus identified by said identified index table. For example, identifying documents X and Y of having the same associations as the query. The final or Seventh Step 5070 (FIG. 5B) involves the obvious step of retrieving the said second data corpus. For example, displaying, producing or retrieving documents X and Y as a resulting match to the query.
  • [0055]
    FIG. 6 is a non-limiting block diagram of the some steps of the inventive method exploring a more general view of the steps mentioned in FIG. 5A and FIG. 5B. The First Step 6010 (FIG. 6) involves the step of identifying an association between a plurality of word elements from a first data corpus such as a query. For example, finding an association between at least two elements in a query. The next or Second Step 6020 (FIG. 6), involves the step of searching an index table identifying said plurality of word elements being associated. The next or Third Step 6030 (FIG. 6) involves the step of identifying a match in the said index table. The next or Fourth Step 6040 (FIG. 6) involves the step of identifying a second data corpus identified by said match. For the index table indicates that documents X and Y have the word elements in identical or similar associations as those of the query. The final or Fifth Step 6050 (FIG. 6) involves the obvious step of retrieving the second data corpus. For example, this final step involves the retrieval, display and/or providing procedure of documents X and Y as results to the query.
  • [0056]
    The enablements described in detail above are considered novel over the prior art of record and are considered critical to the operation of at least one aspect of an apparatus and its method of use and to the achievement of the above described objectives. The words used in this specification to describe the instant embodiments are to be understood not only in the sense of their commonly defined meanings, but to include by special definition in this specification: structure, material or acts beyond the scope of the commonly defined meanings. Thus if an element can be understood in the context of this specification as including more than one meaning, then its use must be understood as being generic to all possible meanings supported by the specification and by the word or words describing the element.
  • [0057]
    The definitions of the words or drawing elements described herein are meant to include not only the combination of elements which are literally set forth, but all equivalent structure, material or acts for performing substantially the same function in substantially the same way to obtain substantially the same result. In this sense it is therefore contemplated that an equivalent substitution of two or more elements may be made for any one of the elements described and its various embodiments or that a single element may be substituted for two or more elements in a claim.
  • [0058]
    Changes from the claimed subject matter as viewed by a person with ordinary skill in the art, now known or later devised, are expressly contemplated as being equivalents within the scope intended and its various embodiments. Therefore, obvious substitutions now or later known to one with ordinary skill in the art are defined to be within the scope of the defined elements. This disclosure is thus meant to be understood to include what is specifically illustrated and described above, what is conceptually equivalent, what can be obviously substituted, and also what incorporates the essential ideas.
  • [0059]
    The scope of this description is to be interpreted only in conjunction with the appended claims and it is made clear, here, that each named inventor believes that the claimed subject matter is what is intended to be patented.
  • CONCLUSION
  • [0060]
    From the foregoing, a series of novel methods for forming and index table, implementing an indexing methodology and method for retrieving information can be appreciated. The described methods overcomes the limitations encountered by current information technologies such as search engines, speech recognition, word processors, and others which fail to identify and/or effectively implement the underlying associations between different kinds of word elements; which potentially leads to the generation of irrelevant data, irrational data, randomly isolated words and user confusion, to allow current and future information technologies to properly and effectively manipulate, identify, select, match and retrieve data.

Claims (4)

  1. 1. A Method for indexing information comprising the steps of:
    a) Identifying a first word element such as an information identifying a word, concept, idea, meaning, image and grammatical information in a first data corpus
    b) Identifying a second word element such as an information identifying a word, concept, idea, meaning, image and grammatical information in said first data corpus
    c) Identifying a first association between said first word element and said second word element implementing an associative protocol such as CIRN
    d) Implementing a first information for identifying said first association
    e) Implementing a second information for identifying said first data corpus
    f) Registering at least one of a said: first information and second information with at least one of a said: first word element and second word element
  2. 2. A method for retrieving information comprising the steps of:
    a) Identifying an association between one of a plurality of word elements from a first data corpus such as a query
    b) Identifying a word element from said plurality
    c) Searching an index table comprising at least one of a: said first word element, information identifying a data corpus of said first word element and information identifying an association of said first word element
    d) Searching an index table comprising at least one of a: said second word element, information identifying a data corpus of said second word element and information identifying an association of said second word element
    e) Identifying an information identifying a data corpus; wherein said first word element and said second word element have the same said information identifying an association
    f) Retrieving said data corpus
  3. 3. A method for providing and index table comprising the steps of:
    a) Identifying an index table
    b) Adding an information field to said index table for containing information for identifying at least one information identifying and association between its indexed word element with one other word element.
    c) Registering information in said information field
  4. 4. A method for identifying information of an index table in a data corpus such as a query, the method comprising the steps of:
    a) Identifying a first group of word elements in a data corpus such as a query,
    b) Identifying a first association between said first group of word elements,
    c) Identifying a second group of word elements in said data corpus,
    d) Identifying a second association between said second group of word elements,
    e) Assigning each said association a unique identifying information.
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