Patent Application
20140258283
1. Technical Field
Embodiments of the present disclosure relate to information searching systems and methods, and particularly to a computing device and a file searching method using the computing device.
2. Description of Related Art
In current search technologies, some useful information may be missed and overlooked, while on the other hand, if a search query expression is too broad, some useful information may be buried deep inside search results and obscured by more useless information. Furthermore, rankings of the search results are based on the perceived “importance” of the search results through analysis of the hyper-linked relationships between the search results. With this technology, the ranking rules are predefined by searching systems and user-specified interests have no impact on the ranking of the searching results. In other words, the query by the user is not being customized, and a more efficient method for performing file search is therefore desirable.
The present disclosure, including the accompanying drawings, is illustrated by way of examples and not by way of limitation. It should be noted that references to “an” or “one” embodiment in this disclosure are not necessarily to the same embodiment, and such references mean “at least one.”
In the present disclosure, the word “module,” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, written in a program language. In one embodiment, the program language may be Java, C, or assembly. One or more software instructions in the modules may be embedded in firmware, such as in an EPROM. The modules described herein may be implemented as either software and/or hardware modules and may be stored in any type of non-transitory computer-readable media or storage medium. Some non-limiting examples of a non-transitory computer-readable medium comprise CDs, DVDs, flash memory, and hard disk drives.
In the embodiment, the computing device 1 connects to one or more terminal devices 2 through a network, which can be a local area network (LAN) or a wide area network (WAN), such as an intranet or the Internet. The terminal device 2 may be a personal computer, a tablet device, a mobile phone or a personal digital assistant (PDA) device.
The at least one processor 11 can be a central processing unit (CPU), a microprocessor, or other suitable data processor chip that performs various functions of the computing device 1. In one embodiment, the storage device 12 can be an internal storage system, such as a flash memory, a random access memory (RAM) for temporary storage of information, and/or a read-only memory (ROM) for permanent storage of information. The storage device 12 can also be an external storage system, such as an external hard disk, a storage card, or a data storage medium.
In the embodiment, the storage device 12 stores a plurality of electronic files and a database that includes a keyword library, and a common term library. The electronic files stores various information to be the queried by a user of the terminal device 2. Each of the electronic files may be a webpage file, a document file, or a text file. The keyword library stores a plurality of keywords that are used frequently, and the keywords are also called “core terms.” For example, the keywords related “traffic” may include “highway,” “railway,” “subway,” “airplane,” “water transport” and etc. The common term library stores a plurality of common terms which are unimportant or unrelated to the keywords. For example, the common terms may include a plurality of periodic terms “today,” “yesterday,” “tomorrow,” and etc, a plurality of adjective terms “much,” “more,” “very,” and etc, and a plurality of pronoun term “we,” “they,” “he,” “she” and etc., for example.
In step S01, the file analysis module 100 obtains an electronic file from the database when a user inputs a file name from the terminal device 2, and analyzes the electronic file to obtain a title and text content of the electronic file. In one embodiment, the text content of the electronic file may be in an English form or a Chinese form. In one example with respect to
In step S02, the file segmenting module 101 divides the text content into a plurality of text segments using a term identification rule. In one embodiment, the term identification rule may be a word identification rule, a statistical word identification rule or a hybrid word identification rule. In the embodiment, the file segmenting module 101 performs a segmenting operation on the text content using the hybrid word identification rule, and an arithmetical statement of the segmenting operation refers to the following exemplary code: expression 1-1 denoted as F[i]>1, expression 1-2 denoted as TF[i]>1, and expression 1-3 denoted as F[i]=TF[i]. Wherein F[i] represents a first number of a specify term presented in the text content, TF[i] represents a second number of a same term related to the specify term presented in the text content. The file segmenting module 101 compares the title of the electronic file with a plurality of related common terms in the common term library using the hybrid word identification rule to divide the text content into a plurality of text segments.
It should be noted that the text content of the electronic file may be in an English form or a Chinese form. If the text content of the electronic file is in English form, step S02 is omitted, the file segmenting module 101 only performs a simple segmenting operation on the text content of the file, such as deleting blank symbols, space symbols, and punctuation symbols from the text content of the file, and then step S03 is implemented. If the text content of the electronic file is in Chinese from, step S02 is implemented to perform the segmenting operation on the text content of the file, as described above.
In step S03, the term extracting module 102 extracts keywords from each of the text segments using a term frequency-inverse document frequency (TF-IDF) rule or a term frequency (TF) rule. In one embodiment, the keywords are extracted from each of the text segments performing the following steps: (a) filtering a plurality of common terms from each of the text segments according to the common term library, for example, the terms “today,” “we,” “and,” and related terms which are recorded in the common term library are filtered from the text segment; (b) calculating a weight value of each term in each of the text segments; (c) ranking all the terms in a descending order according to the weight value of each term in the each of the text segments; and (d) determining m terms which are ranked from the first term to the mth term as the keywords. In one embodiment, the weight value of each term is calculated according to the following equation: Wi=N*Wc+M*Wt, wherein Wi represents a weight value of a term, N represents a number of times of the term which is presented in the text content of the file, Wc represents a weight value of the text content of the file, M represents a number of times of the term which is presented in the title of the file, and Wt represents a weight value of the title of the file. In the embodiment, the weight value of the text content of the file may be defined as “1”, and the weight value of the title may be defined as “3”. Referring to
In step S04, the statistics analysis module 103 calculates an importance factor of each of the keywords, obtains a history record of the keywords for querying the file in a recent period (e.g., one day, one week or one month), and obtains one or more interested terms from the keywords according to the importance factor of each of the keywords and the history record of the keywords. In the embodiment, the importance factor of each keyword is defined as a relevance or importance of the keyword relevant to the interested terms, and is calculated according to the following equation: Fitness=100×log Feq/log(|K−N/2|+1), wherein Fitness represents an importance factor of a keyword, Feq represents a term frequency of the keyword, K represents a total number of electronic files which include the keyword, and N represents a total numbers of the electronic files which are queried by users of the terminal device 2. In the embodiment, the statistics analysis module 103 ranks all the keywords in a descending order according to the importance factors of the keywords and the history records of the keywords, and determines r keywords which are ranked from the first keyword to the rth keyword as the interested terms. The interested terms represent terms of user's interest, that is, the files which includes information that the user most expects to view. Referring to
In step S05, the file searching module 104 obtains search results from the database by performing a search operation according to the interested terms, calculates a relevance degree between each file in the search results and the interested terms, ranks the files according to the calculated relevance degree, and sends the files with a ranking order to the terminal device 2. The search results may include a plurality of related files which are relevant to the interested terms. In one embodiment, the relevance degree is defined as a relationship between each file in the search results and the interested terms. The larger the value of the relevance degree, the more relevant the ranking content is to the file, that is, the file is, or is closer to, what the user most expects to query or view. In the embodiment, the file searching module 104 may rank the files in the search results in a descending order or in an ascending order according to the relevance degree between each file in the search results and the interested terms.
Although certain disclosed embodiments of the present disclosure have been specifically described, the present disclosure is not to be construed as being limited thereto. Various changes or modifications may be made to the present disclosure without departing from the scope and spirit of the present disclosure.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2013100761474 | Mar 2013 | CN | national |
