WO2007051397A1 - Systeme d’extraction d’informations et procede d’extraction d’informations - Google Patents

Systeme d’extraction d’informations et procede d’extraction d’informations Download PDF

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Publication number
WO2007051397A1
WO2007051397A1 PCT/CN2006/002804 CN2006002804W WO2007051397A1 WO 2007051397 A1 WO2007051397 A1 WO 2007051397A1 CN 2006002804 W CN2006002804 W CN 2006002804W WO 2007051397 A1 WO2007051397 A1 WO 2007051397A1
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WIPO (PCT)
Prior art keywords
user
search
information
feature
behavior information
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PCT/CN2006/002804
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English (en)
Chinese (zh)
Inventor
Wei Wang
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Huawei Technologies Co., Ltd.
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Application filed by Huawei Technologies Co., Ltd. filed Critical Huawei Technologies Co., Ltd.
Publication of WO2007051397A1 publication Critical patent/WO2007051397A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/335—Filtering based on additional data, e.g. user or group profiles

Definitions

  • the invention relates to the field of information retrieval technology, in particular to an information retrieval system and a retrieval method. Background of the invention
  • a search engine is a system that can obtain web page information, build a database, and provide queries. Depending on how you work, you can divide your search engine into two basic categories: the FullText Search Engine and the Directory.
  • the full-text search engine database relies on a software called “Spider” or “Crawlers” to automatically retrieve a large amount of web page content through various links on the network, and analyze and organize according to the rules. Forming. Both Google and Baidu are typical full-text search engine systems. A query to a full-text search engine is often referred to as a search for "all sites” or "all sites”, such as Google's full-text search ( http: ⁇ www.google.com/intl/zh-CN/ ).
  • the catalogue is a manual database that collects and organizes website data, such as Yahoo China and Sohu, Sina, and NetEase. In addition, some navigation sites on the Internet can also be attributed to the original category, such as "Website Home” (http://www.haol23.com/).
  • the query for the category directory is usually called the search "category directory” or search for "category website", such as "Sina search” (http://dir.sina.com.cn/) and "Yahoo China search” (http:/ /cn.search.yahoo.com/dirsrch/ ).
  • Full-text search engines and catalogs are used in different lengths.
  • the full-text search engine relies on software, so the capacity of the database is very large, but its query results are often not accurate enough.
  • the catalogue relies on manual collection and organization of websites, which can provide more accurate query results, but the collected content is very limited. .
  • search engines now offer both types of queries.
  • the integration of these two types of search engines has also produced other search services, here, We also call them search engines, mainly in the following two categories:
  • meta search engine (META Search Engine).
  • these search engines generally do not have their own network robots and databases, their search results are by calling, controlling and optimizing the search results of other independent search engines and in a uniform format Displayed in the same interface.
  • the meta search engine does not have "web robot” or “web spider” and does not have an independent index database, it has its own unique meta-search technology in terms of search request submission, retrieval interface proxy and search result display.
  • “metaFisher meta search engine” http:AVww.hsfz.net/fish/ )
  • it calls and integrates data from multiple search engines such as Google, Yahoo AlltheWeb, Baidu and OpenFind.
  • the integrated search engine uses web technology to link multiple independent search engines on a web page. When querying, click or specify a search engine, input once, multiple search engines simultaneously query, and search results are displayed by different search engines by different pages. Such as "Internet Swiss Army Knife, (http: ⁇ free.okey.net/%7Efree/searchl.htm).
  • the full-text search engine “web robot” or “web spider” is a kind of software on the network. It traverses the web space and can scan websites within a certain IP address range and along the network. The link on the page is from one page to another, from one website to another. In order to ensure the latest information collected, it will also return to the pages that have been captured. Web pages collected by web robots or web spiders must be analyzed by other programs. A large number of calculations are performed according to a certain correlation algorithm to create a web page index, which is added to the content index database.
  • the full-text search engine that we usually see is actually a search interface of a search engine system.
  • the search engine finds an index of all relevant web pages that match the keyword from the huge content index database. And presented to us according to certain ranking rules. Different search engines, different content index databases, and different ranking rules, so when we use different search engines to query with the same keyword, the search results are not the same.
  • a URL which is a URL (Uniform Resource Locator, through which a web browser can access the corresponding file); b. Different content words in the file and in some engines The relative address of each such content word related to the other content words of the file; c. A segment summary of the file, usually only a few lines or the first few lines of the file; d, may be provided in its HTML description section a description of the file.
  • a search engine When a user uses a search engine, the user is provided with a keyword-based query that attempts to find a file containing as many keywords as possible, and when requested, according to an operator or other specification (eg, a logical operation, such as: Look for the range with / or / not). For each such file it finds, the engine retrieves its file record and sorts by the number of key matches in the file relative to other such files to provide the record to the user.
  • a keyword-based query that attempts to find a file containing as many keywords as possible, and when requested, according to an operator or other specification (eg, a logical operation, such as: Look for the range with / or / not).
  • the engine retrieves its file record and sorts by the number of key matches in the file relative to other such files to provide the record to the user.
  • the search engine simply responds to the keyword query provided by the user, and the user may have different behavior habits at different times and on different machines, thus having different needs, and the content information that the user wants to retrieve may have Different, but existing search methods do not consider these situations to classify search engine search results. Summary of the invention
  • the present invention provides an information retrieval system and method.
  • An embodiment of the present invention provides an information retrieval system, including: a search engine, a content index database provided to a search engine for searching, and the following:
  • a user feature database that stores feature behavior information that the user has in different time periods
  • a content analysis system that respectively associates with the user feature database, the search engine, and the connected user
  • the communication network of the terminal is connected, and is used for determining the current time and receiving the user identifier transmitted by the user terminal, and querying the user feature database to obtain the characteristic behavior information of the current time of the user identifier; and searching by the search engine.
  • the result information is re-searched and sorted according to the obtained feature behavior information, and the retrieved search result is sent to the user terminal for display.
  • An embodiment of the present invention further provides an information retrieval method, which includes the following steps: saving feature behavior information corresponding to a user in different time periods;
  • the original search result searched by the search engine is secondarily searched according to the feature behavior information, and the search result including the feature behavior information is displayed to the user.
  • An embodiment of the present invention further provides an information retrieval method, which is characterized by comprising the following steps:
  • the characteristic behavior information corresponding to the user under different conditions is saved, and the original retrieval is performed according to the retrieval keyword input by the user;
  • the original retrieval result is subjected to a second retrieval according to the characteristic behavior information.
  • condition is identified as time, machine model or a combination of the two.
  • the embodiment of the present invention further provides an information retrieval system, which includes an interconnected search engine and a content index database provided for searching by the search engine, and further includes:
  • a user feature database which stores feature behavior information of the user under different conditions
  • a content analysis system which is respectively connected with a user feature database, a search engine, and a communication network connected to the user terminal, for acquiring the current condition identifier and the slave Transmitted by the user terminal User identifier, and according to the condition identifier and the user identifier, querying the user feature database to obtain the feature behavior information of the user identifier under the corresponding condition; and performing the search result information searched by the search engine based on the obtained feature behavior information
  • the sorting is searched again, and the sorted search result is again retrieved and sent to the user terminal for display.
  • conditional identification can be time, or a machine model or a combination of the two.
  • the solution provided by the embodiment of the present invention can search for the original collection result searched by the search engine according to the keyword input by the user according to the time characteristic and the personalized characteristic behavior of the user corresponding to the machine model.
  • the record is subjected to secondary screening and filtering, and the file record information that the user is really interested in is preferentially displayed to the user, which improves the accuracy and search efficiency of the user to retrieve relevant information.
  • Figure 1 shows the structure of a traditional search engine system.
  • FIG. 2 is a system frame diagram of an information retrieval system according to an embodiment of the present invention.
  • FIG. 3 is a framework diagram of a user feature database according to an embodiment of the present invention.
  • FIG. 4 is a skeleton diagram of a content analysis system according to an embodiment of the present invention.
  • Web Spider crawls web pages from the Internet, sends web pages to the "web database,” and repeats the loop until all web pages are crawled.
  • the system obtains the text information from the "web database” and sends it to the "text index” module to create an index to form an "index database”.
  • the server searches the relevant web pages in the "index database”, sorts by relevance according to the "query server”, and extracts The content summary of the keyword, the last page of the organization is returned to the "user".
  • One embodiment of the present invention considers that the user has different feature behavior information in different time periods. Therefore, after the search engine obtains the retrieval result, the retrieval result is processed according to the characteristic behavior information of the user corresponding to the current time period.
  • the search result that meets the user characteristic behavior information is preferentially displayed to the user, thereby improving the accuracy of the search engine retrieval, and making the retrieval result provided to the user more close to the user's needs.
  • FIG. 2 shows an information retrieval system according to an embodiment of the present invention, which includes a content analysis system 23, a user feature database 24, a search engine 22, and a content index database 21, wherein:
  • the content analysis system 23 is configured to receive the user identifier transmitted by the user terminal, the input search keyword, and the current time of obtaining the local server, and query the user feature database 24 to match the characteristic behavior of the user in the time period, and pass the search engine.
  • the searched pages are retrieved and filtered again, so that the retrieved pages are presented to the user in the order of priority behavior of the feature behaviors exhibited by the user during the time period.
  • the combination of the machine model, as well as the time and machine model can also be used as conditional identification, which allows the information retrieval system to provide users with higher retrieval accuracy and search efficiency.
  • the user feature database 24 is used to store the characteristic behavior information of the user, especially the characteristic behavior information of the user in different time periods, and the database is described in detail later, and details are not described herein.
  • the search engine 22 which is a text and keyword based search tool, returns a list of required file pointers with a file title after searching in the existing content index database 21, and usually has some extracted from the body of the file. Descriptive text.
  • the content index database 21 automatically accesses the website by activating an automated program implemented by the software (such as "web spider") and sequentially tracks the hypertext connection therein and extracts each file encountered therein by a so-called "keyword”, And stored in the database, provided to the search engine 22 for access.
  • an automated program implemented by the software such as "web spider”
  • FIG. 3 is an embodiment of the user feature database 24, which may be, but is not limited to, The preservation of the message.
  • the personal user information table, the time period information table, the feature behavior table, and the matching table are described in detail below.
  • the feature behavior information corresponding to the machine model number, the time, the machine model, and the group cooperation condition identifier of the two are saved in the user feature database.
  • the personal user information table is used to store the personal information of the user, and may be a letter input when the user registers.
  • Table 1 below shows a user information table:
  • the time period information table is used to store different time period numbers corresponding to different time segments, and the time segment number is used to facilitate the retrieval of the database, and the setting of the time period is more flexible. As shown in the table below
  • the table when the machine model is used for conditional identification is a machine model information table, which is similar to the time zone information table and has two machine model numbers and machine models.
  • the condition table when identifying the group cooperation conditions of time and machine model, here is an example (see Table 3 below), and its use and time period information Table 2 are similar, just one more.
  • the feature behavior table is used to store different feature lines corresponding to different feature behavior keywords of the user. For the number, one of the feature behavior keywords may also have a dependent keyword, which are all characteristic behavior information.
  • Each of the data in Table 5 above is a complete feature behavior information, which also includes a feature priority item, which is used to identify the priority of different feature behaviors of the user within a certain period of time.
  • the example shown in Table 5 indicates that the user U001 has a feature priority level 9 of the feature behavior number C001 and a feature priority level 8 of the feature behavior number C002 in the time period T001, indicating that the user U001 is more in the time period T001. It is biased to exhibit characteristic behavior with feature behavior number C001.
  • the data stored in the user feature database 24 may be provided by a system for collecting user behavior characteristics.
  • a system for collecting user behavior characteristics refer to the system and method for collecting user service behavior features applied by the applicant. "Invented.
  • FIG. 4 is a block diagram of the content analysis system 23, including a data unit 231, a search engine interface 232, an analysis unit 233, a retrieval analysis unit 234, and a retrieval data storage unit 235. In this embodiment, it is identified by time as a condition.
  • the data transceiver unit 231 is configured to implement interaction with the user terminal, and receive the user through the user terminal.
  • the search keyword input is input to the search engine interface 232, and the obtained user identifier is sent to the analysis unit 233.
  • the search engine interface 232 is configured to implement interaction with the search engine 22, send search keywords sent by the data transceiving unit 231 to the search engine 22, and receive search results of the search engine 22 to the search data storage unit 235.
  • the search data storage unit 235 saves the search results of the search engine 22 sent from the search engine interface 232 for analysis by the search analysis unit 234.
  • the analyzing unit 233 is configured to receive the user identifier sent by the data transceiver unit 231 and obtain the current search time, where the current search time may be the time obtained by the system, or may be the time reported by the machine, and according to the user.
  • the user search database 24 is identified and obtained by the current search time, and the feature behavior information corresponding to the user identifier and the search time is obtained and provided to the search analysis unit 234.
  • the behavioral feature information may include, but is not limited to, a feature behavior keyword and a feature behavior dependent keyword. Accordingly, when the machine model is used as the conditional identification, the analysis unit 233 is a machine type analysis unit (not shown) which performs processing similar to that of the analysis unit 233, and correspondingly acquires and processes the machine model information.
  • the analysis unit determines the corresponding machine model number (ie, the machine model identification) by the machine model reported by the machine, instead of the analysis unit 233, the current time can be obtained directly from the system side, or the current time can be obtained from the machine side, and then After obtaining the user identifier and the machine model identifier, the database 24 is retrieved to obtain corresponding feature behavior information.
  • the combination of time and machine model is used as the conditional identification, the corresponding steps and conditions are similar to the above steps and methods, except that the machine model is changed to the combination of time and machine model, so that the conditions become more specific.
  • the search analysis unit 234 is configured to receive the feature behavior keyword information sent by the analysis unit 233, and perform secondary search filtering and/or sorting on the search result stored in the search data storage unit 235, and filter and / or the sorted search result is sent to the data transceiving unit 231 to be returned to the user terminal for display to the user.
  • FIG. 4 reference is also made to the information retrieval system of the embodiment of the present invention shown in FIG.
  • the flow chart of the present search process the information retrieval method of the present invention is described in detail, wherein the steps are
  • 505 are also an embodiment of a conditional identification based retrieval method of the present invention. This embodiment is identified by time as a condition and includes the following parts:
  • Step 501 First, the user inputs a search keyword in a search engine provided by the user terminal according to the information to be queried, and may input a Boolean type (for example, "and” or "or") between consecutive keywords or Operators that other search engines can recognize.
  • a Boolean type for example, "and” or "or”
  • the user inputs a search keyword "game" at the user terminal, requesting to query related information.
  • Step 502 The information is transmitted to the content analysis system 23 through the network, and the data query unit 231 of the content analysis system 23 obtains the keyword information of the user query; and the data transceiver unit 231 also obtains the identifier of the user, and the user identifier can be obtained. It is input by the user through the user terminal, or may be entered when the user logs in when using the information retrieval system of the embodiment of the present invention.
  • Step 503 The data transceiving unit 231 sends the obtained keyword to the search engine interface 232, and sends the user identification information to the analyzing unit 233.
  • the data transceiving unit 231 sends the keyword "game” input by the user to the search engine interface 232; and the obtained identifier U001 of the user is sent to the analyzing unit 233.
  • Step 504 The search engine interface 232 sends the obtained keyword of the user query to the search engine 22, and the search engine 22 retrieves the related information in the content index database 21 according to the keyword, and returns the search result to the search engine interface 232, and then It is sent to the search data storage unit 235 for storage.
  • Step 505 The analyzing unit 233 finds the matching related feature behavior data from the user feature database 24 according to the obtained user identifier and current time information, and sends the matching characteristic behavior data to the retrieval analyzing unit 234.
  • the time information may be provided by a local server loaded with the content analysis system or by any computer device within the network, preferably provided by a local server.
  • the analysis unit 233 is a machine type analysis unit, or a group having the above unit functions. In conjunction with units (not shown), these units perform similar processing as the analysis unit 233, correspondingly acquiring and processing information such as machine models.
  • the analysis unit After obtaining the user identifier and the condition identifier (time, machine model or a combination of the two), the analysis unit searches the user feature database to obtain corresponding feature behavior information, and the retrieval process is:
  • the intermediate feature behavior information of the user is retrieved from the intermediate feature behavior information of the user by using the condition identifier.
  • the intermediate feature behavior information, the corresponding feature behavior information includes a feature behavior keyword and/or a feature behavior dependent keyword.
  • the two steps may be in no order, that is, the intermediate feature behavior information of the user may be retrieved from the user feature database by using the condition identifier, and then the user identifier is used to retrieve the intermediate feature behavior information from the user. Corresponding feature behavior information.
  • the user's characteristic behavior keywords and characteristic behavior subordinate keywords at the moment are: games, video games, computer games...; music, Classical, orchestral...
  • the user's characteristic behavior keywords and related feature priorities are sent to the retrieval analysis unit 234.
  • the corresponding model number (ie, the model number of the machine) can also be obtained from the database 24 according to the model information reported by the machine; and the user is retrieved from the table of the database 24 according to the user identifier and the model number of the machine.
  • User behavior preferences and priorities; these feature behavior keywords and related feature priorities of the user are then sent to the retrieval analysis unit 234.
  • the following describes the intermediate feature behavior information, and the corresponding feature behavior information.
  • the condition identification can be a combination of time, machine model or time and machine model.
  • match table 5 which shows individual feature behavior information.
  • the intermediate feature behavior information may be retrieved according to the time segment number (ie, the condition), and then the corresponding feature is obtained from the intermediate feature behavior information according to the user number (ie, the user identifier). Behavioral information.
  • the step of retrieving the corresponding characteristic behavior information is similar to the step when the time is the time, only the conditional identification has changed.
  • Step 506 The retrieval analysis unit 234 obtains the relevant retrieval result (such as page information) that the user has searched from the retrieval data storage unit 235 by using the user identifier, and then performs the secondary search by the received feature behavior keyword and related feature priority.
  • the result information is retrieved and reordered, so that the page information that the user really has is first displayed to the user.
  • the feature behavior keyword (game, electronic game, computer game%) with high priority is first searched, and the retrieved file is retrieved.
  • the information is listed first; then the low-priority feature behavior keywords (music, classical, orchestral%) are searched, and the retrieved file information is listed later; then the feature is not included in the secondary search.
  • the information of the original search result of the behavior keyword is listed last.
  • Step 507 The search analyzing unit 234 sends the search result sorted by the secondary search to the data transceiving unit 231, and the data transceiving unit 231 sends the result of the secondary search sorting (such as page information) to the user terminal for display to the user.
  • the above search scheme can be used in almost any information retrieval system to increase the search accuracy of the search engine, whether or not the engine is a regular engine.
  • embodiments of the present invention also improve the accuracy of retrieving information from a mass database, regardless of the language in which the textual information is used, such as Chinese, English, French, German, and the like.

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  • General Engineering & Computer Science (AREA)
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Abstract

La présente invention concerne un système d’extraction d’informations qui comprend un moteur de recherche, une base de données d’indexation de contenu fournie au moteur de recherche afin de réaliser la recherche, une base de données de caractères d’utilisateur, et un système d’analyse de contenu. L’invention concerne également un procédé d’extraction d’informations qui comprend les étapes suivantes : on stocke des informations comportementales caractéristiques correspondant à l’identité de l’utilisateur à plusieurs intervalles temporels, et on obtient les mots clés d’extraction saisis par l’utilisateur, le moteur de recherche effectue l’extraction en fonction du mot clé afin d’obtenir le résultat de l’extraction d’origine ; on obtient les informations relatives à l’identité de l’utilisateur et à l’heure actuelle et, en fonction de celles-ci, on extrait le mot clé des données comportementales caractéristiques de l’utilisateur correspondant ; et l’on effectue une seconde extraction sur le résultat de l’extraction d’origine effectuée par le moteur de recherche en fonction des informations comportementales caractéristiques, et on affiche à l’utilisateur, avec la priorité maximale, les résultats de l’extraction contenant le mot clé. L’invention concerne également un autre procédé d’extraction d’informations et un autre système d’extraction d’informations. Le système d’extraction d’informations est à même de filtrer la recherche de l’utilisateur en fonction des différents comportements caractéristiques de l’utilisateur et d’améliorer la précision et les performances de la recherche de l’utilisateur sur les informations associées
PCT/CN2006/002804 2005-11-01 2006-10-20 Systeme d’extraction d’informations et procede d’extraction d’informations WO2007051397A1 (fr)

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