Abstract:
An information searching method is disclosed that is able to effectively utilize non-content descriptive information to optimize preliminary search results and thus improve searching precision. The information searching method includes the steps of extracting one or more non-content characteristics of a document from a document set, analyzing the extracted non-content characteristics, generating an optimizing tool, and optimizing a preliminary search result with the generated optimizing tool.

Description:
BACKGROUND OF THE INVENTION  
       [0001]     1. Field of the Invention  
         [0002]     The present invention relates to a method and a device for information searching, and particularly, to a method and a device able to optimize search results based on non-content characteristics of a document.  
         [0003]     2. Description of the Related Art  
         [0004]     In recent years and continuing, more and more digital text documents appear on the Internet and in digital libraries, news, and internal documents of companies. In order to manage these digital data, more and more attention has been put on information searching techniques. On the other hand, the information searching techniques are becoming more and more open and flexible, so that nowadays information searching is open, dynamic, and very fast. At the same time the amount of information has increased explosively, so a high-precision searching technique is required.  
         [0005]     It is noted that text information includes not only a portion expressing the content of the text document, but also non-content descriptive information, such as the creation time and attributes of the text document. In most information searching systems of the related art, during information searching, usually, only keyword-matching and word distributions are utilized, which are internal content characteristics of the documents, but non-content characteristics are not used.  
       SUMMARY OF THE INVENTION  
       [0006]     The present invention may solve one or more problems of the related art.  
         [0007]     A preferred embodiment of the present invention may provide an information searching method and an information searching device able to effectively utilize non-content descriptive information to optimize search results and thus improve searching precision.  
         [0008]     According to a first aspect of the present invention, there is provided an information searching method, comprising the steps of:  
         [0009]     extracting one or more non-content characteristics of a document from a document set;  
         [0010]     analyzing the extracted non-content characteristics;  
         [0011]     generating an optimizing tool based on the analyzing results; and  
         [0012]     optimizing a preliminary search result with the generated optimizing tool.  
         [0013]     According to a second aspect of the present invention, there is provided an information searching device, comprising:  
         [0014]     a non-content characteristics extractor configured to extract one or more non-content characteristics of a document from a document set;  
         [0015]     an analyzer configured to analyze the extracted non-content characteristics and generate an optimizing tool based on the analyzing results; and  
         [0016]     an optimizer configured to optimize a preliminary search result with the generated optimizing tool.  
         [0017]     According to an embodiment of the present invention, it is possible to effectively optimize search results and improve searching precision.  
         [0018]     These and other objects, features, and advantages of the present invention will become more apparent from the following detailed description of preferred embodiments given with reference to the accompanying drawings. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0019]      FIG. 1  is a flowchart illustrating a general concept of an embodiment of the present invention;  
         [0020]      FIG. 2  is a block diagram illustrating an information searching method according to an embodiment of the present invention;  
         [0021]      FIG. 3  is a diagram illustrating configurations and operations of an information searching device for executing the information searching method of the embodiment of the present invention;  
         [0022]      FIG. 4  is a diagram illustrating configurations and operations of the time-based optimizing tool  304  according to the embodiment of the present invention;  
         [0023]      FIG. 5  is a diagram illustrating configurations and operations of the document relationship-based optimizing tool  305  according to the embodiment of the present invention;  
         [0024]      FIG. 6  is a diagram illustrating document relationships in a data group; and  
         [0025]      FIG. 7  is a diagram illustrating document relationships in a data group. 
     
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0026]     Below, preferred embodiments of the present invention are explained with reference to the accompanying drawings.  
         [0027]      FIG. 1  is a flowchart illustrating a general concept of an embodiment of the present invention.  
         [0028]     As shown in  FIG. 1 , in step S 150 , any known searching engine is used to perform searching.  
         [0029]     In step S 151 , non-content characteristics of documents are used to optimize preliminary search results and output the optimized search results.  
         [0030]      FIG. 2  is a block diagram illustrating an information searching method according to an embodiment of the present invention.  
         [0031]     As shown in  FIG. 2 , first, for a given data group, for example, a digital data group, non-content characteristics of a document in the document set are extracted by a non-content characteristics extractor.  
         [0032]     The non-content characteristics of the documents include intrinsic characteristics of the document, external descriptions, and relationships between documents. The intrinsic characteristics of the document are characteristics of the document itself, such as a creation time, a file size, document formats (for example, PDF format, WORD format, TXT format, and so on), and author of the document. The external descriptions of a document are descriptions independent of contents of the document, for example, the version information of the document, classification information of the document, and the storage place of the document. The relationships between documents may be a citation relation between documents, or the relationship like an electronic mail message and a reply.  
         [0033]     After the non-content characteristics of the document are extracted, each of the extracted non-content characteristics is analyzed by an analyzer to generate respective optimizing tools. The optimizing tool can optimize search results. The search results may be the results obtained by searching a given data group with a search engine, or the results obtained by another optimizing tool. For example, the results include a document sequence, and each document in the document sequence has a reliability value indicating the probability of the document being the final search result. Then, the optimizing tools optimize the search results and return the optimized search results to a user who is performing the search.  
         [0034]      FIG. 3  is a diagram illustrating configurations and operations of an information searching device for executing the information searching method of the embodiment of the present invention.  
         [0035]     As shown in  FIG. 3 , the information searching device includes a search engine  301 , a non-content characteristics extractor  302 , an analyzer  310 , and optimizing tools  303  through  306 .  
         [0036]     When a user inputs a query, the query is sent to the search engine  301 , and the search engine  301  performs searching to obtain preliminary search results.  
         [0037]     The non-content characteristics extractor  302  extracts non-content characteristics of documents in a document set.  
         [0038]     The analyzer  310  analyzes the extracted non-content characteristics and generates optimizing tools  303  through  306  based on the analyzing results. For example, the optimizing tool  303  is generated based on document classification information (hence, the optimizing tool  303  is referred to as “a classification-based optimizing tool” herein after), the optimizing tool  304  is generated based on document creation time (hence, the optimizing tool  304  is referred to as “a time-based optimizing tool” herein after), the optimizing tool  305  is generated based on document relationship (hence, the optimizing tool  305  is referred to as “a document relationship-based optimizing tool” herein after), and the optimizing tool  306  is generated based on other characteristic N (the optimizing tool  306  is referred to as “data optimizing tool” herein after).  
         [0039]     In other words, the search engine  301  performs preliminary searching to obtain preliminary search results, and the classification-based optimizing tool  303 , the time-based optimizing tool  304 , the document relationship-based optimizing tool  305 , and the data optimizing tool  306  optimize the preliminary search results; thus final optimized search results are obtained, and the final optimized search results are returned to the user.  
         [0040]     Below, configurations of the optimizing tools  303  through  306  are described in detail.  
         [0041]     Time-Based Optimizing Tool  304   
         [0042]     In various fields of society, there are usually some hot topics in a specified time interval, and documents discussing these hot topics are usually concentrated in time. Thus when searching for one of the hot topics, the search results are also concentrated in time. Hence time characteristics can be used to filter some noise in the preliminary search results.  
         [0043]      FIG. 4  is a diagram illustrating a configuration and operations of the time-based optimizing tool  304  according to the embodiment of the present invention.  
         [0044]     As shown in  FIG. 4 , the time-based optimizing tool  304  includes a reliable result selector  401 , an arranging unit  402 , a grouping unit  403 , a calculator  404 , an adjusting unit  405 , and an ordering unit  406 .  
         [0045]     The reliable result selector  401  selects from the preliminary search results, a resulting document sequence having relatively high reliability.  
         [0046]     Specifically, two parameters N and M are provided, where N represents the number of the output results, M represents the maximum uncertainty of the number of the output results, and M&lt;N.  
         [0047]     For example, if the number of the documents in the preliminary results is less than N, the reliable result selector  401  returns the preliminary results directly as the final results (that is, the resulting document sequence having relatively high reliability). If the number of the documents in the preliminary results is less than N+M, the reliable result selector  401  sets the maximum number MAX of the documents to be processed to be the number of the output results N. If the number of the documents in the preliminary results is equal to or greater than N+M, the reliable result selector  401  set the maximum number MAX of the documents to be processed by itself to be N+M.  
         [0048]     Then, the reliable result selector  401  calculates differences of the reliability values of every two documents from a (N−M)-th document to a MAX-th document, and obtains a document corresponding to the maximum difference. If the maximum difference corresponds to plural documents, the first document is chosen. The order number of the obtained documents is denoted to be X.  
         [0049]     Then, the reliable result selector  401  returns the first document to the X-th document as the selection results; that is, the resulting document sequence having relatively high reliability.  
         [0050]     The arranging unit  402  arranges the documents of the resulting document sequence having relatively high reliability in order of creation time, for example, from the earliest one to the latest one.  
         [0051]     The grouping unit  403  groups the documents arranged in order of creation time into Y groups.  
         [0052]     Specifically, the grouping unit  403  calculates differences of the creation times of every two input documents, calculates an average AVG of all the differences of the creation times, then selects documents having a creation time differences greater than a*AVG, where a is a parameter for controlling grouping granularity. Then the grouping unit  403  groups the input documents arranged in order of creation time into Y groups with the selected documents as group boundaries. Then the grouping unit  403  returns the grouping results.  
         [0053]     The calculator  404  calculates a sum of the reliability values of documents in each group to obtain a group reliability value and a group action range of each group. For example, a maximum value and a minimum value of the reliability values of the documents in each group are used as limits of the group action range. Then, the calculator  404  returns the group reliability values and the group action ranges of all groups.  
         [0054]     The adjusting unit  405  adjusts the reliability values of the documents included in the preliminary search results.  
         [0055]     For example, the adjusting unit  405  determines whether each of the documents is in one or more group action ranges of the groups, and if the reliability value of the document is in the group action range of a group, the adjusting unit  405  increases the reliability value of the document by b*Rg to obtain a new group reliability value. Here, b is a parameter for controlling adjustment magnitude of the document creation time on the preliminary search result, where the maximum of b is not greater than 5% of the average reliability values of all documents, and Rg represents the group reliability value of the current group.  
         [0056]     The ordering unit  406  arranges the documents in descending order of the adjusted reliability values to obtain optimized results.  
         [0057]     Document Relationship-Based Optimizing Tool  305   
         [0058]     Usually, documents have certain relationship with each other. For example, one document may cite contents of other documents, or one electronic mail message (a document) replies to another electronic mail message (another document). Those documents having a relationship are usually associated with the same subject; hence search results of those documents having a relationship are closely related to each other. Due to this, document-relationship characteristics can be used to filter some noise in the preliminary search results.  
         [0059]      FIG. 5  is a diagram illustrating configurations and operations of the document relationship-based optimizing tool  305  according to the embodiment of the present invention.  
         [0060]     As shown in  FIG. 5 , the document relationship-based optimizing tool  305  includes a reliable result selector  501 , a distance calculator  502 , an adjusting unit  503 , and an ordering unit  504 .  
         [0061]     The reliable result selector  501  selects a resulting document sequence having relatively high reliability from the preliminary search results. The reliable result selector  501  operates in the same way as the reliable result selector  401 , and detailed explanations are omitted.  
         [0062]     The distance calculator  502  calculates a distance from each of the documents in the resulting document sequence to each of the other documents in the resulting document sequence, which has relatively high reliability.  
         [0063]     Since there are several kinds of document relationships, a weight is assigned to each kind of document relationship. For example, the citation relationship is represented by Q 1 , the weight of a document which cites other documents is defined to be Q 11 , and the weight of a document which is cited by other documents is defined to be Q 12 , where the second digit “1” in the suffix “11” represents the citing relationship, and the second digit “2” in the suffix “12” represents the being cited relationship.  
         [0064]     In addition, the electronic mail reply relationship is represented by Q 2 , the weight of a document which is a reply to another electronic mail message is defined to be Q 21 , and the weight of a document which is replied to by another electronic mail message is defined to be Q 22 , where the second digit “1” in the suffix “21” represents replying relationship, and the second digit “2” in the suffix “22” represents being replied to relationship.  
         [0065]     With these notations, a data group can be expressed to be a directional diagram group as shown in  FIG. 6 .  
         [0066]      FIG. 6  is a diagram illustrating document relationships in a data group.  
         [0067]     In the directional diagram shown in  FIG. 6 , the above-defined weights are referred to as “weights of sides”. For example, the weight of the side from a point D to a point F is Q 11 , and the weight of the side from the point F to the point D is Q 12 .  
         [0068]     Between any two points, there may be one or more paths, or there may be no path; further, a path does not include cycles.  
         [0069]     The distance of any directional path is calculated as indicated below.  
         [0070]     First, the distance is defined to be a sum of the weights of sides passed through by the directional path. For example, in  FIG. 6 , the distance from a point B to a point G is Q 22 +Q 11 .  
         [0071]     If there is no path between two documents, it is defined that the distance between the two documents is infinite, and the distance from one document to itself is zero.  
         [0072]     If there are plural paths between the two documents, the distance L between the two documents is expressed by the following equation, 
 
 L= 1/((1 /L 1)+(1 /L 2)+ . . . +(1 /LX )) 
 
         [0073]     where L 1  represents the distance of a path  1 , L 2  represents the distance of a path  2 , . . . LX represents the distance of a path X.  
         [0074]     For example, in  FIG. 6 , the distance L from a point A to a point F equals: 
 
 L= 1/((1 /Q 21)+(1/( Q 22+ Q 11+ Q 21))+(1/( Q 11+ Q 11+ Q 21))) 
 
         [0075]     The adjusting unit  503  adjusts the reliability values of the documents included in the preliminary search results.  
         [0076]     For example, the adjusting unit  503  determines the distance from a document in the preliminary search results to a document in the selected resulting document sequence, which has a high reliability value, and increases the reliability value of each document included in the preliminary search results by 
 
b*Rd/(1+L), 
 
         [0077]     where b is a parameter, and the size of b controls the adjustment magnitude of the distance between two documents in the preliminary search result, Rd represents the reliability values of the documents in the selected resulting document sequence, and L represents the distance from the document in the preliminary search result to the document of the selected resulting document sequence.  
         [0078]     The ordering unit  504  arranges the documents in descending order of the adjusted reliability values to obtain optimized results.  
         [0079]     Document Relationship and Creation Time-Based Optimizing Tool  
         [0080]     Since the document relationship-based optimizing tool  305  involves many parameters and calculations in calculating the distance between two documents, sometimes it takes some time to find an appropriate value. To solve this problem, the characteristic of the document creation time can be introduced in the document relationship-based optimizing tool  305 .  
         [0081]     Operations of the optimizing tool of this example are basically the same as those of the document relationship-based optimizing tool  305  except that calculation of the distance between two documents is performed in a different way. In this example, the calculator for calculating the distance between two documents provides correlation between the two documents.  
         [0082]     Similarly, since there are several kinds of document relationships, a weight is assigned to each kind of document relationship. For example, and the weight of the citation relationship is represented by Q 1 , the weight of the electronic mail reply relationship is represented by Q 2 . Note that, here, the weight values are not directional.  
         [0083]     With these notations, a data group can be expressed to be a non-directional diagram group as shown in  FIG. 7 .  
         [0084]      FIG. 7  is a diagram illustrating document relationships in a data group.  
         [0085]     In the non-directional diagram shown in  FIG. 7 , the length of a side equals a product of the weight of the relationship between the two documents and the difference between the creation times of the two documents.  
         [0086]     In this example, the length of a side equals the product of Q 1  and the difference between the creation times of the two documents, or equals the product of Q 2  and the difference between the creation times of the two documents.  
         [0087]     The optimizing tool of this example calculates the distance between any two documents. Here, the distance of a non-directional path equals the sum of the lengths of the sides passed through by the non-directional path.  
         [0088]     For example, in  FIG. 7 , the distance from a point B to a point G is Q 2 *|TB−TC|+Q 1 *|TC−TG|, where, |TB−TC| represents the difference between the creation times of the document B and the document C, and |TC−TG| represents the difference between the creation times of the document C and the document G.  
         [0089]     Similar to the method shown in  FIG. 6 , in this example, if there is no path between two documents, it is defined that the distance between the two documents is infinite; the distance from one document to itself is zero.  
         [0090]     If there are plural paths between the two documents, the distance L between the two documents is expressed by the following equation, 
 
 L= 1/((1 /L 1)+(1 /L 2)+ . . . +(1 /LX )) 
 
         [0091]     While the present invention is described with reference to specific embodiments chosen for purpose of illustration, it should be apparent that the invention is not limited to these embodiments, but numerous modifications could be made thereto by those skilled in the art without departing from the basic concept and scope of the invention.  
         [0092]     This patent application is based on Chinese Priority Patent Application No. 200610148602 filed on Nov. 14, 2006, the entire contents of which are hereby incorporated by reference.