KR101059557B1 - Computer-readable recording media containing information retrieval methods and programs capable of performing the information - Google Patents

Abstract

Disclosed are a computer-readable recording medium containing an information retrieval method and a program capable of performing the same.

The information retrieval method according to the present invention includes a data collection step of collecting documents and extracting keywords of the collected documents, a query input step of inputting a query by a searcher, and a topic rank of extracted keywords for the input query. A step of selecting an associated keyword associated with a query word among the keywords, a word clustering step of forming the related keywords into a word cluster by vectorizing the selected related keywords into a word vector, and calculating a rank of the formed word cluster. A cluster rank calculating step and a related keyword providing step of providing related keywords related to the input query word using the calculated rank of the word cluster and the subject rank of the keyword are included.

Information search, topic rank, word clustering, word vector

Description

Method for searching information and computer readable medium storing information that includes a method for searching for information and a program capable of performing the same

The present invention relates to a computer-readable recording medium containing a method for retrieving information and a program capable of performing the same, and more particularly, a method for providing a related keyword through a subject rank and a program capable of performing the same. The present invention relates to a computer-readable recording medium.

The use of the Internet is increasing, and accordingly, the amount of information available through the Internet is also increasing. As a result, the necessity of information retrieval becomes more and more important. However, as the amount of information increases, it becomes more difficult to find exactly the information that a searcher, a user searching for information, wants to obtain.

Initially, the results of information retrieval provided on the Internet could be obtained by human gathering and prioritizing, and providing such results according to the searcher's needs.

As the amount of information on the Internet has increased, the conventional method has reached its limit, and accordingly, a method of collecting information by a search robot and classifying and providing the information through mechanical processing has become popular. However, the result of the information retrieval by the mechanical processing does not provide exactly the results desired by the searcher, causing inconvenience that the searcher needs to find the desired information again.

SUMMARY OF THE INVENTION The present invention has been made in an effort to provide a computer-readable recording medium containing an information retrieval method capable of satisfying a searcher's needs and a program capable of performing the same.

In order to solve the above technical problem, the present invention provides a computer-readable recording medium containing the following information retrieval method and a program capable of performing the same.

The information retrieval method according to the present invention includes a data collection step of collecting documents and extracting keywords of the collected documents, a query input step in which a query word is input by a searcher, and a topic rank of the extracted keywords for the input query word. a related keyword selection step of selecting related keywords related to the query word by calculating (topic rank), and vectorizing the selected related keywords into a word vector to form the related keywords into a word cluster; And a cluster rank calculating step of calculating a rank of the word cluster, and a related keyword providing step of providing related keywords related to the query word input using the calculated rank of the word cluster and the subject rank of the keyword.

The subject rank TR (K, w) can be calculated by the following equation.

Where K is the query word, w is the keyword, DF (K, w) is the frequency of documents containing K and w, DF (w) is the frequency of documents containing w, and p (w) is w Probabilities α and β are positive real numbers by weight.

In the selecting a related keyword, N keywords may be selected as the related keyword in the order of increasing the subject rank.

In the word clustering step, the associated keywords may be vectorized into a word vector by the following equation.

는 벡터화한 i번째 연관 키워드, TR_ij는 i번째 연관 키워드에 대한 j번째 연관 키워드의 주제 랭크(여기서 i와 j는 1과 N 사이의 정수)이다.here,

Is the vectorized i-th association keyword, TR _ij is the subject rank of the j-th association keyword for the i-th association keyword, where i and j are integers between 1 and N.

In the word clustering step, similarity of the related keywords may be measured by the following equation, and thus similar related keywords may be formed into a word cluster.

은

와

사이의 유사도,

은 벡터인

와

사이의 cosine값이다.here,

silver

Wow

The similarity between

Silver vector

Wow

Cosine value between.

The rank of the word cluster may be an average value of a subject rank of each of the related keywords constituting the word cluster.

The providing of the related keywords may provide the word clusters in ascending order of the word clusters, and provide the related keywords in order of subject rank of the related keywords in the provided word clusters.

The method may further include providing a related document among the provided related keywords, wherein the related keyword selected by the searcher is an extracted document.

According to the present invention, it is possible to provide a searcher with related keywords and documents corresponding to a specific query word, and improve search performance by using the searcher's feedback information. In particular, by providing an associated keyword associated with a query input by the searcher, the searcher can select exactly the information desired by the searcher without inputting the correct query.

Hereinafter, an information retrieval method according to embodiments of the present invention and a computer-readable recording medium containing a program capable of performing the same will be described in detail with reference to the accompanying drawings, but the present invention is limited to the following embodiments. The present invention may be embodied in various other forms without departing from the technical spirit of the present invention. That is, specific structural to functional descriptions are merely illustrated for the purpose of describing embodiments of the present invention, and embodiments of the present invention may be embodied in various forms and should be construed as being limited to the embodiments described herein. No. It is not to be limited by the embodiments described in the text, it should be understood to include all changes, equivalents, and substitutes included in the spirit and scope of the present invention.

Terms such as first and second may be used to describe various components, but such components are not limited by the terms. The terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component.

When a component is said to be "connected" or "connected" to another component, it may be directly connected to or connected to that other component, but it may be understood that other components may exist in the middle. Will be. On the other hand, when a component is said to be "directly connected" or "directly connected" to another component, it will be understood that there is no other component in between. Other expressions describing the relationship between components, such as "between" and "immediately between" or "neighboring to" and "directly neighboring", will likewise be interpreted.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, the terms "comprise" or "include" are intended to indicate that there is a feature, number, step, action, component, or combination thereof described, and one or more other features or numbers, It will be understood that it does not exclude in advance the possibility of the presence or addition of steps, actions, components, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. Terms such as those defined in the commonly used dictionaries are to be interpreted as having meanings consistent with the meanings in the context of the related art, and are not construed in ideal or excessively formal meanings unless expressly defined in this application. .

1 is a schematic diagram showing the configuration of an information retrieval system for implementing an information retrieval method according to an embodiment of the present invention.

Referring to FIG. 1, the information retrieval system 1 is connected through a network 100 and includes a controller 1000 and a storage 2000. The controller 1000 includes a collector 1100, an analyzer 1200, an indexer 1300, a subject rank processor 1400, a word clustering processor 1500, a cluster rank processor 1600, and a language analyzer 1700. , A provider 1800, a user feedback processor 1900, and the like. The storage unit 2000 may include a main storage unit 2100, an index storage unit 2200, a rank storage unit 2300, a log storage unit 2400, and the like. The controller 1000 is connected to the Internet document 10 or the searcher device 200 through the network 100 to collect and provide information.

The collector 1100 may collect and translate the Internet documents 10 through the network 100 to generate an Internet document structure for each Internet document 10. Detailed functions and configurations of the collection unit 1100 will be described later. The Internet document 10 is a generic term that encompasses documents such as various web pages containing information including text on the Internet. Specifically, the Internet document 10 may include, for example, a typical web page, blog, news article, and the like. In addition to this, any information containing text or information that can be represented by text may be applicable. For example, various posts of a specific community (named cafes, clubs, clubs, etc.), web pages contained in various company or individual web sites, press or portal sites. News articles, etc., posts posted on various blogs, etc. may be included in the Internet document 10. In addition, the Internet document 10 may include not only text information but also various multimedia data such as pictures, videos, and music. In particular, the Internet document 10 mainly composed of multimedia data may include information consisting of text such as a title.

The analyzer 1200 may analyze the collected Internet document 10, specifically, the Internet document structures generated by the collector 1100, and generate analysis information including keywords, characteristic keyword vectors, and the like. The index unit 1300 may index the collected Internet document 10 and the analysis information to generate index information including a keyword and a characteristic keyword vector. The collected Internet document 10, the Internet document structure, analysis information, and index information may be stored in the main storage unit 2100, and the analysis unit 1200 or the index unit 1300 may be the collection unit 1100 or the analysis, respectively. The information may be received from the unit 1200 or the information stored in the main storage unit 2100 may be used. Detailed functions and configurations of the analysis unit 1200 will be described later.

The characteristic keyword vector is generated for each Internet document 100, specifically, for each Internet document structure, and represents the characteristic of the information contained in the individual Internet document 100 in the form of a word vector. The word vector contains keywords representing the characteristics of the individual Internet document 100 and the weights of the keywords. The weight is calculated using the term frequency (TF) of each keyword and the inverse document frequency (IDF), which is the inverse of the frequency of each keyword in the Internet document set. The word frequency is the number of occurrences of a particular keyword in an individual Internet document and is a measure of how much the particular keyword represents the content of the Internet document. Reverse document frequency is also the inverse of the number of Internet documents that a particular keyword appears in an Internet document set. Keywords that appear in a few Internet documents have a greater ability to distinguish the Internet document that the keyword appears from other Internet documents. .

The topic rank processing unit 1400 may include a topic rank between keywords of the Internet document 10 or a query input by the searcher through the searcher device 200 and keywords extracted from the Internet documents 10. The subject rank can be calculated. In addition, the related keyword for the query may be selected based on the calculated topic rank. The method of calculating the subject rank will be described later.

The word clustering processor 1500 may vectorize the selected related keyword in the form of a word vector so that similar related keywords form a word cluster. A method of forming a word cluster will be described later.

The cluster rank processor 1600 may obtain an average value of a subject rank of each of the related keywords included in the formed word cluster, and obtain a rank of the word clusters.

The language analyzer 1700 may perform language analysis on the query word input by the searcher through the searcher device 200 connected through the network 100. The language analyzer 1700 may determine a language of the input query word, and analyze and select a core query word when the query word is a sentence or a plurality of words.

The provider 1800 provides the searcher apparatus 200 with a result processed by the information retrieval system 1 from the input query. In this case, an associated keyword for the input query word may be provided, or an associated Internet document may be provided from the related keyword.

The user feedback processor 1900 may store a searcher's selection result for the result provided by the provider 1800 in the log storage 2400 and reflect the result in selecting the associated keyword.

In addition to the main storage unit 2100, the storage unit 2000 includes an index storage unit 2200 for storing index information, a rank storage unit 2300 for storing a subject rank and a cluster rank, and a searcher including log information of searchers. It includes a log storage unit 2400 for storing information. The main storage unit 2100, the index storage unit 2200, the rank storage unit 2300, and the log storage unit 2400 may be physically divided storage devices, but logically divide one or a plurality of storage devices. It may be a division unit.

2 is a flowchart illustrating an information retrieval method according to an embodiment of the present invention.

1 and 2, the Internet document 10 is collected through the collection unit 1100 (S112). The collection unit 1100 may collect a specific kind of Internet document 10, or may collect a wide variety of Internet documents 10. For example, it may collect certain kinds of Internet documents 10, such as news articles or posts posted on blogs, and other types of web pages of companies or individuals, posts of specific communities, multimedia data, etc. Internet documents 10 may be collected. This may be determined according to the type of service of the information retrieval system 1 to be provided to the searcher.

The analysis unit 1200 analyzes the Internet document 10 and extracts a keyword representing each Internet document 10 (S114). Together with the keyword, it is possible to form a characteristic keyword vector representing the characteristic of the information contained in the Internet document 10 in the form of a word vector. The keyword is at least one word having meaning in the corresponding Internet document 10, and the characteristic keyword vector is a word vector including the keyword and the weight of the keyword.

On the other hand, when a searcher inputs a query word through the searcher device 200 (S122), the language analyzer 1700 analyzes the query word (S124). The searcher device 200 may search for an information retrieval system through the network 100. It can be connected with (1). The network 100 may include a wired or wireless Internet, a local LAN, an intranet, or the like. The language analyzer 1700 may classify at least one core query word by analyzing the input language, format, etc. of the query word input by the searcher, and replace the at least one core query word with a query word in a later process. Subsequently, the description of the query word may be one input query word or one classified key query word, or may be a combination of at least two classified key query words. Also, the term "query" may be a query or core query in the input language, or may be translated into a main language processed by the information retrieval system 1.

Thereafter, a subject rank TR (K, w) of the extracted keyword w for the analyzed query word K is calculated (S130). The subject rank TR (K, w) can be calculated by the following equation.

Where K is the query word, w is the keyword, DF (K, w) is the frequency of documents containing K and w, DF (w) is the frequency of documents containing w, and p (w) is w Probabilities α and β are positive real numbers by weight.

Specifically, the topic rank TR (K, w) represents the degree of association of the keyword (w) with respect to the query (K). DF (K, w) and DF (w) represent Document Frequency of (K and w) and (w), respectively. The document frequency refers to the number of documents in which the keywords or query terms are included in the collected documents (here, the document means individual Internet documents 10 collected). In other words, DF (K, w) is the frequency of the document containing the query (K) and the keyword (w), DF (w) is the frequency of the document containing the keyword (w).

The first part of the topic rank TR (K, w) formula, DF (K, w) / DF (w), is used for documents containing keyword (w) and query (K) together. Means percentage. Therefore, the larger the value of DF (K, w) / DF (w), the higher the correlation between the keyword w and the query word K.

However, for example, when the values of DF (w) and DF (K, w) are small, as in the case where DF (w) is 1 and DF (K, w) is 1, the keyword (w) ) Can be difficult to determine if the association between the query and the query is really high. In other words, even if there are documents in which the keyword w is present with the query word K, very rarely among a large number of documents, it may be difficult to see that the correlation between the keyword w and the query word K is high.

On the contrary, even if both DF (w) and DF (K, w) are close to large values, for example, the total number of documents, it may be difficult to determine whether the correlation between the keyword w and the query word K is really high. . In other words, it means that both the keyword w and the query word K are frequently used, and it is difficult to give an association. For example, the case where a, the, is, of, and the like is selected as the keyword w and the query word K in English can be considered.

The second part of the topic rank TR (K, w) formula, -p (w) log (p (w)), can be used to give an accuracy to the association of the keyword (w) with the query (K). Hereinafter, the second part of the subject rank TR (K, w) formula, -p (w) log (p (w)), may be called an entropy part. By the entropy part, TR (K, w) may be zero when p (w) is zero or one. Accordingly, the inaccuracy may minimize the inaccuracy of association that may occur when p (w), which is a probability that the keyword w is present in the documents, is close to zero or close to one.

That is, subject rank TR (K, w) is an entropy part (second part of the formula) that can occur when the association between the keyword (w) and the query word (K) is calculated mechanically (the first part of the formula). This can be improved by improving the accuracy. α and β are weights given for the influence between the first part and the second part of the subject rank TR (K, w) formula, respectively, and may use positive real numbers. For example, the subject rank TR (K, w) can be calculated using α = 3 and β = 2.

In addition, the subject rank TR (K, w) is calculated at the stage where the query word K by the searcher is input. Therefore, the values of DF (K, w), DF (w), p (w), etc. for a specific keyword w may vary depending on a search point. Through this, search results reflecting the passage of time can be obtained.

After the subject rank processing unit 1400 calculates the subject rank of each keyword w for the query word K, the keywords w of N (where N is a positive integer greater than 1) in the order of the largest subject ranks. Is selected as a related keyword (S142). The related keywords may be determined according to the setting of the information retrieval system 1, and, for example, 100 related keywords may be selected by setting N = 100.

The word clustering processor 1500 may vector the selected N related keywords in the form of a word vector to form a word cluster (S144). The word cluster means that the selected N related keywords are classified into similar ones and formed into clusters. In order to compare the similarity of the selected N related keywords, the N related keywords selected by the following equation may be vectorized in the form of a word vector.

는 벡터화한 i번째 연관 키워드, TR_ij는 i번째 연관 키워드(w_i)에 대한 j번째 연관 키워드(w_j)의 주제 랭크(여기서 i와 j는 1과 N 사이의 정수)를 의미한다. 즉, TR_ij는 하기 식으로 나타낼 수 있다. here,

Is the vectorized i th association keyword, TR _ij means the subject rank of the j th association keyword w _j with respect to the i th association keyword w _i (where i and j are integers between 1 and N). That is, TR _ij can be represented by the following formula.

Where w _i and w _j are the i-th and j-th related keywords, respectively.

Through this vectorization, a word vector, which is N vectorized related keywords, may be formed.

The N vectorized related keywords may be measured for similarity by the following equation.

은

와

사이의 유사도를 의미하고,

은 벡터인

와

의 cosine값을 의미한다. here,

silver

Wow

The similarity between

Silver vector

Wow

Means the cosine of.

As such, by measuring similarity of the N vectorized related keywords, the N related keywords may be formed into word clusters.

After forming the word cluster, the cluster rank processing unit 1600 calculates an average of the topic rank TR (K, w) for the query words of the associated keywords included in each word cluster, and calculates a cluster rank (S146). The topic rank TR (K, w) of the related keywords for the query word is stored in the rank storage unit 2300 after storing a value already obtained at the time of subject rank calculation (S130) and selecting the related keyword (S142), and then cluster rank. It can be called and used by the processor 1600.

The provider 1800 provides a keyword related to the searcher in consideration of the cluster rank and the topic rank obtained above (S148). That is, the associated keywords constituting each word cluster in the cluster rank order calculated by the cluster rank processor 1600 may be provided. In this case, related keywords in each word cluster may be provided in topic rank order. In this case, the searcher can easily distinguish the related keywords as compared to simply providing the selected N related keywords in the order of each topic rank.

As such, by providing an associated keyword corresponding to the searcher's query, the searcher can help the searcher to search the desired information accurately without inputting the correct query.

Subsequently, when the searcher selects a specific related keyword from related keywords provided through the searcher device 200, the provider 1800 may additionally provide a related document which is the Internet document 10 from which the related keyword is extracted ( S150). In this case, the related documents provided to the searcher may be provided so that the weights of the keywords are sorted in order of the related documents from which the related keywords are extracted.

Whether the searcher selects the related keyword or the Internet document 10 provided through the searcher device 200 may be stored in the log storage 2400 through the user feedback processor 1900. The user feedback by the searcher may be reflected at the time of selecting the relevant keyword (S142) to reflect whether or not the searcher is selected (S160). Specifically, when compared with other related keywords, if the searcher's selection rate is low and does not exceed a predetermined threshold, it may be determined that the degree of association is inferior and may be excluded from the related keyword result.

3 is a flowchart illustrating an information retrieval method according to a variation of an embodiment of the present invention.

1 and 3, the modification of the embodiment of the present invention is different from the embodiment of the present invention shown in FIG. 2 in the main rank calculation step S126. After collecting the Internet document 10 through the collection unit 1100 (S212), the analysis unit 1200 analyzes the Internet document 10 to extract a keyword representing each Internet document 10 index storage unit ( 2200) (S214). Thereafter, the subject rank between each keyword is calculated in advance and stored in the rank storage unit 2300 (S216). At this time, the calculation of the subject rank between each keyword may be set to proceed every time the keywords of the collected Internet document 10 is extracted, or may be set to calculate the subject rank at a specific time point.

As such, when the subject rank between each keyword is set to be calculated at a specific time point, the time point may be determined in consideration of the amount of collected Internet documents 10 once a day, once a week, and once a month. It can be set to proceed at a time when the load of the information retrieval system 1 is relatively low, that is, when the use of the searcher is low, such as at night.

On the other hand, when the searcher inputs a query word through the searcher device 200 (S222), the language analyzer 1700 analyzes the query word (S224), and calculates a topic rank calculated in advance for the keyword corresponding to the query word. It may be read from the rank storage unit 2300.

Through this, whenever a query is input, it is possible to prevent a time delay that may occur when calculating the subject rank.

4 to 5 is a schematic diagram showing the configuration of the collecting unit and the analysis unit according to an embodiment of the present invention. 4 to 5, a process of storing collected Internet documents in a form that can be provided to a searcher will be described by taking blog posts as an example.

4 is a schematic view showing a configuration of a collecting unit according to an embodiment of the present invention.

Referring to FIG. 4, since the Internet document 10 and its surrounding information may be described in various languages, the language judging module 1110 may first determine a writing language such as Korean, Japanese, Chinese, and English. In addition, the collection module including the Internet document collection module 1122 and the peripheral information collection module 1124 may collect the Internet document 10 and its surrounding information together. For example, when the Internet document 10 is one post of a blog, the Internet document collection module 1122 determines an address providing a feed such as RSS / ATOM provided by the blog, thereby determining the Internet document 10. Can be collected. However, not all Internet documents 10 are provided with addresses that provide feeding. For example, since most blogs provide only a part of recent posts, in this case, the Internet document 10 may be collected through post body extraction. Similarly, the peripheral information collection module 1124 may extract and collect the peripheral information including a comment and a trackback.

The Internet document 10 and the surrounding information collected as described above are collected separately from the original form, and thus may be structured through the content restoration module 230. For example, when collecting a post of a blog, it can be analyzed and restored through the process of extracting the entire body of the post, commenting, linking trackback information, extracting existing HTML post contents, and structuring to RSS / ATOM format. In addition, according to the language determined by the language judging module 1110, the automatic translation module 1140 may translate the Internet document 10 written in a language different from the language to be provided by the automatic translation into a language to provide information about the Internet 10 and its surrounding information. Can be. The translation result of the Internet document 10 and its surrounding information restored by the content restoration module 1130 and the automatic translation module 1140 may be generated by the unit structure generation module 1150 of one Internet document 10, for example, a blog. In this case, each post can be created as an Internet document structure. The Internet document structure may be generated in a format that can be processed by a machine such as a computer, for example, XML format or RSS format.

The Internet document structure generated as described above is stored in the main storage unit 2100, and the analysis unit 1200 receives the Internet document structure from the collection unit 1100 or calls the Internet document structure stored in the main storage unit 2100 for analysis. You can work.

5 is a schematic diagram illustrating a configuration of an analysis unit according to an exemplary embodiment of the present invention.

Referring to FIG. 5, the analyzer 1200 may generate analysis information by analyzing an Internet document structure collected and generated by the collector 1100 by using a text mining technique. The analysis unit 1200 receives the Internet document structure generated by the collection unit 1100 or the Internet document structure stored in the main storage unit 2100, and performs entity name analysis in the entity name analysis module 1210 to extract the main entity names. Can be. The entity name analysis extracts words having meanings such as person name, company name, product name, service name, date, etc. by analyzing the text of the Internet document structure and can be extracted through the entity name dictionary and extraction rules. Thereafter, the extracted main entity name and information included in the Internet document structure may be statistically analyzed by the feature extraction module 1220 to extract keywords representing the Internet document structure.

The automatic classification module 1230 may automatically classify Internet document structures. Such automatic classification may be classified based on the predefined classification list 1232 and the machine learning data 1234 according to the classification list 1232. Automatic classification has high performance when the difference of classification object is clear, and classification in similar group may tend to decrease performance. Especially in the case of multi-stage classification, the analysis rate may be reduced. For example, large categories such as sports, society, and economy can be classified to some extent by the system. However, categorizing sports balls into baseball, volleyball, basketball, etc. may be less analyzed than the former. The automatic classification module 1230 may be implemented by, for example, an algorithm such as Bayesian or SVM (Support Vector Machine). In this case, the automatic classification module 1230 may automatically classify only the highest category. This is because automatic classification into multi-level categories reduces accuracy and burdens the system for machine learning.

The automatic clustering module 1240 clusters the automatically classified Internet document structures for each classification category. Auto-clustering allows the system to statistically group Internet document structures in any unit. The automatic cluster module 1240 may be implemented using, for example, a K-means algorithm. The clustered Internet document structures may have an information quantity index measured by the information quantity measurement module 1250. The characteristic keyword vector generated by combining the keyword and the information quantity index can be used for searching as a word vector representing each Internet document structure. Analysis information including the extracted keyword and the generated characteristic keyword vector may be stored in the main storage unit 2100 again.

As such, the Internet document structure and keywords stored in the main storage unit 2100 may be provided in a form convenient for a searcher by the above-described information retrieval method.

6 is a screen illustrating an information search result by an information search method according to an exemplary embodiment of the present invention.

Referring to FIG. 6, a list of related keywords is provided when "Samsung" is entered as a query. In this case, the bolded part of the related keyword list is the first related keyword of each word cluster, that is, the related keyword having the largest value of the subject rank in one word cluster. have. This display method is merely an example that may vary depending on the design of the information retrieval system. For example, a list of related keywords may be provided by allowing lines to be changed for each word cluster.

As such, even when a query word having a comprehensive meaning is input, it can be confirmed that a related keyword is efficiently provided so that a searcher can find desired information.

In addition, embodiments of the present invention can be written as a program that can be executed in a computer system. In addition, the program read from the computer-readable recording medium containing the program can be executed in the digital computer system.

Examples of computer-readable recording media include ROM, RAM, CD-ROM, DVD-ROM, magnetic tape, floppy disks, optical data storage devices, and the like, as well as carrier wave (eg, transmission over the Internet). It also includes implementations in form. The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion. And functional programs, codes and code segments for implementing the present invention can be easily inferred by programmers in the art to which the present invention belongs.

1 is a schematic diagram showing the configuration of an information retrieval system for implementing an information retrieval method according to an embodiment of the present invention.

2 is a flowchart illustrating an information retrieval method according to an embodiment of the present invention.

3 is a flowchart illustrating an information retrieval method according to a variation of an embodiment of the present invention.

4 is a schematic view showing a configuration of a collecting unit according to an embodiment of the present invention.

5 is a schematic diagram illustrating a configuration of an analysis unit according to an exemplary embodiment of the present invention.

6 is a screen illustrating an information search result by an information search method according to an exemplary embodiment of the present invention.

<Description of main parts of drawing>

DESCRIPTION OF REFERENCE NUMERALS 1: information retrieval system, 10: internet document, 200: searcher device, 1000: control unit, 1100: collection unit, 1200: analysis unit, 1300: index unit, 1400: subject rank processing unit, 1500: word clustering processing unit, 1600: Cluster rank processing unit, 1700: language analysis unit, 1800: providing unit, 1900: user feedback processing unit, 2000: storage unit, 2100: main storage unit, 2200: index storage unit, 2300: rank storage unit, 2400: log storage unit

Claims (9)

A data collection step of collecting documents and extracting keywords of the collected documents; A query input step of inputting a query word by a searcher; A related keyword selection step of selecting related keywords associated with the query word among the keywords by calculating a topic rank of the extracted keyword for the input query word; A word clustering step of vectorizing the selected related keywords into a word vector to form the related keywords into a word cluster; A cluster rank calculation step of calculating a rank of the formed word cluster; And A related keyword providing step of providing related keywords related to the query word input using the calculated rank of the word cluster and the subject rank of the keyword. The method according to claim 1, The subject rank TR (K, w) is calculated by the following formula.

here, K is the query word, w is the keyword, DF (K, w) is the frequency of the document containing K and w, DF (w) is the frequency of the document containing w, p (w) is the probability that w is in the document, α, β are positive real numbers by weight. The method of claim 2, The selecting of the related keyword may include selecting N keywords as the related keywords in order of increasing the subject rank, wherein N is a positive integer greater than one. The method of claim 2, The word clustering step, And the related keywords are vectorized into a word vector according to the following equation.

here,

Is the vectorized i-th association keyword, TRij is the subject rank of the j-th association keyword for the i-th association keyword, where i and j are integers between 1 and N. 5. The method of claim 4, The word clustering step, And measuring similarity of the related keywords by the following equation to form similar related keywords into word clusters.

here,

silver

Wow

The similarity between

Silver vector

Wow

Cosine value between. The method according to claim 1, The rank of the word cluster, And an average value of a subject rank of each of the related keywords constituting the word cluster. The method according to claim 6, The related keyword providing step, And providing the word clusters in ascending order of the word clusters, and providing the related keywords in order of subject rank of the related keywords within the provided word clusters. The method according to claim 1, And providing a related document among the provided related keywords, wherein the related keyword selected by the searcher is an extracted document. A computer-readable recording medium containing a program capable of performing the navigation according to any one of claims 1 to 8.

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