CN109656385A - Input prediction method and device based on knowledge graph and electronic equipment - Google Patents
Input prediction method and device based on knowledge graph and electronic equipment Download PDFInfo
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- CN109656385A CN109656385A CN201811621360.8A CN201811621360A CN109656385A CN 109656385 A CN109656385 A CN 109656385A CN 201811621360 A CN201811621360 A CN 201811621360A CN 109656385 A CN109656385 A CN 109656385A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/02—Input arrangements using manually operated switches, e.g. using keyboards or dials
- G06F3/023—Arrangements for converting discrete items of information into a coded form, e.g. arrangements for interpreting keyboard generated codes as alphanumeric codes, operand codes or instruction codes
- G06F3/0233—Character input methods
- G06F3/0237—Character input methods using prediction or retrieval techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
- G06F40/295—Named entity recognition
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Abstract
The application provides an input prediction method and device based on a knowledge graph and electronic equipment, wherein the method comprises the following steps: acquiring a text in an input box before an input cursor and acquiring a current pinyin character string; performing word segmentation on the text, acquiring a plurality of word segments in the text, and performing Chinese character coding on the current pinyin character string; detecting whether the multiple participles comprise a first keyword or not, detecting whether a relation chain is included after the Chinese character is coded or not, and if the relation chain comprises the first keyword, querying a preset knowledge map database to obtain a second keyword corresponding to the first keyword and the relation chain; and displaying the Chinese character codes and the second key words corresponding to the relation chains in the cloud prediction column. Therefore, semantic analysis and understanding are carried out on the input text of the user, association of words is carried out through the knowledge map database, reasonable input recommendation can be given quickly, and the communication efficiency of the user is improved.
Description
Technical field
This application involves intelligent input technique field more particularly to a kind of input prediction methods of knowledge based map, dress
It sets and electronic equipment.
Background technique
Currently, the major function of input method, is to provide the user with keyboard, so that user passes through the need of typewriting completion input
It asks.However, the demand of input, is the demand linked up, if input method can prejudge user under current scene, it is desirable to input assorted
, and how can just quickly finish and enter and send, it is only and solves the most fundamental input demand of user.
In the related technology, the prediction of input method is all based on data statistics, and the entry that high frequency is counted on corpus is taken
With combination, the i.e. data results such as binary or ternary, such as left member hit, then provide the right member of high frequency as prediction entry for
Family selection.
Summary of the invention
The application is intended to solve at least some of the technical problems in related technologies.
For this purpose, the application proposes a kind of input prediction method of knowledge based map, for the input text of user, carry out
Semantic analysis and understanding, and by the association of knowledge mapping database progress word, can quickly provide reasonable input
Recommend, greatly improves the communication efficiency of user.
The application proposes a kind of input prediction device of knowledge based map.
The application proposes a kind of electronic equipment.
The application proposes a kind of computer readable storage medium.
The application first aspect embodiment proposes a kind of input prediction method of knowledge based map, comprising:
It obtains and inputs the text before cursor in input frame, and obtain current pinyin character string；
Word cutting is carried out to the text, obtains multiple participles in the text, and to the current pinyin character string
Carry out encoding of chinese characters；
Whether detect in the multiple participle includes the first keyword, and detection after the encoding of chinese characters whether include
Relation chain,
If knowing including first keyword and the relation chain, inquire preset knowledge mapping database obtain with
First keyword and corresponding second keyword of the relation chain；
The corresponding encoding of chinese characters of the relation chain and second keyword are shown and predict column in cloud.
Optionally, as the first possible implementation of the application first aspect, in the preset knowledge of inquiry
Spectrum data library obtains before the second keyword corresponding with first keyword and the relation chain, further includes:
Obtain multiple knowledge entries；
The multiple knowledge entry is identified, the keyword and relation chain in each knowledge entry are obtained；
Multiple keywords and multiple relation chains are subjected to storage according to preset mode and generate preset knowledge mapping data
Library.
Optionally, as second of possible implementation of the application first aspect, the multiple participle of detection
In whether include the first keyword, comprising:
The multiple participle is identified by default entity identification algorithms, obtains corresponding multiple entities；
If the multiple entity is matched to the first keyword in preset relation chain dictionary, it is determined that the multiple participle
In include the first keyword.
Optionally, as the third possible implementation of the application first aspect, the relation chain includes: the first pass
Tethers and the second relation chain；
The preset knowledge mapping database of the inquiry obtains corresponding with first keyword and the relation chain the
Two keywords, comprising:
It is obtained in preset knowledge mapping database according to first keyword and first relation chain corresponding
Third keyword；
It is obtained in preset knowledge mapping database according to the third keyword and second relation chain corresponding
Second keyword.
Optionally, as the 4th kind of possible implementation of the application first aspect, in the preset knowledge of inquiry
Spectrum data library obtains after the second keyword corresponding with first keyword and the relation chain, further includes:
The 4th key is matched in preset hot spot dictionary library according to first keyword and second keyword
Word；
4th keyword is shown into the second predeterminated position on prediction column.
The application second aspect embodiment proposes a kind of input prediction device of knowledge based map, which includes:
Module is obtained, for obtaining the text before inputting cursor in input frame, and obtains current pinyin character string；
Coding module is obtained, for carrying out word cutting to the text, obtains multiple participles in the text, and to institute
It states current pinyin character string and carries out encoding of chinese characters；
Whether detection module includes the first keyword, and detection in the Chinese character for detecting in the multiple participle
It whether include relation chain after coding；
Inquiry obtains module, if inquiring and preset knowing for knowing including first keyword and the relation chain
Know spectrum data library and obtains the second keyword corresponding with first keyword and the relation chain；
Display module is predicted for showing the corresponding encoding of chinese characters of the relation chain and second keyword in cloud
Column.
Optionally, as the first possible implementation of the application second aspect, described device, further includes:
First obtains module, for obtaining multiple knowledge entries；
Second acquisition module obtains the key in each knowledge entry for identifying to the multiple knowledge entry
Word and relation chain；
Generation module, it is preset for multiple keywords and multiple relation chains to be carried out storage generation according to preset mode
Knowledge mapping database.
Optionally, as second of possible implementation of the application second aspect, the detection module is specific to use
In:
The multiple participle is identified by default entity identification algorithms, obtains corresponding multiple entities；
If the multiple entity is matched to the first keyword in preset relation chain dictionary, it is determined that the multiple participle
In include the first keyword.
Optionally, as the third possible implementation of the application second aspect, the relation chain includes: the first pass
Tethers and the second relation chain；
The inquiry obtains module, is specifically used for:
It is obtained in preset knowledge mapping database according to first keyword and first relation chain corresponding
Third keyword；
It is obtained in preset knowledge mapping database according to the third keyword and second relation chain corresponding
Second keyword.
Optionally, as the 4th kind of possible implementation of the application second aspect, described device further include:
Matching module, for according to first keyword and second keyword in preset hot spot dictionary library
It is fitted on the 4th keyword；
The display module is also used to showing the 4th keyword into the second predeterminated position on prediction column.
The application third aspect embodiment proposes a kind of electronic equipment, comprising: memory, processor and is stored in storage
On device and the computer program that can run on a processor, when the processor executes described program, realize described in first aspect
Knowledge based map input prediction method.
The application fourth aspect embodiment proposes a kind of computer readable storage medium, is stored thereon with computer journey
Sequence when the program is executed by processor, realizes the input prediction method of knowledge based map described in first aspect.
Technical solution provided by the embodiment of the present application, may include it is following the utility model has the advantages that
It obtains and inputs the text before cursor in input frame, and obtain current pinyin character string, word cutting is carried out to text, is obtained
Multiple participles in text, and current pinyin character string is obtained, and encoding of chinese characters is carried out to current pinyin character string, it detects more
It whether include the first keyword in a participle, and whether detection includes relation chain after encoding of chinese characters, if knowing including first
Keyword and relation chain then inquire preset knowledge mapping database and obtain the second pass corresponding with the first keyword and relation chain
The corresponding encoding of chinese characters of relation chain and the second keyword are finally shown and predict column in cloud by keyword.It is directed to the input of user as a result,
Text carries out semantic analysis and understanding, and the association of word is carried out by knowledge mapping database, can quickly provide conjunction
The input of reason is recommended, and the communication efficiency of user is greatly improved.
Detailed description of the invention
The application is above-mentioned and/or additional aspect and advantage will become from the following description of the accompanying drawings of embodiments
Obviously and it is readily appreciated that, in which:
Fig. 1 is a kind of flow diagram of the input prediction method of knowledge based map provided by the embodiment of the present application；
Fig. 2 is a kind of flow diagram that preset poem corpus generates provided by the embodiment of the present application；
Fig. 3 is the schematic diagram of the input prediction of knowledge based map provided by the embodiment of the present application；
Fig. 4 is a kind of structural schematic diagram of the input prediction device of knowledge based map provided by the embodiments of the present application；
Fig. 5 is the structural representation of the input prediction device of another kind knowledge based map provided by the embodiment of the present application
Figure；
Fig. 6 is the structural representation of the input prediction device of another knowledge based map provided by the embodiment of the present application
Figure；And
Fig. 7 is the structural schematic diagram of the application electronic equipment one embodiment.
Specific embodiment
Embodiments herein is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, it is intended to for explaining the application, and should not be understood as the limitation to the application.
Below with reference to the accompanying drawings the input prediction method, apparatus and electronics for describing the knowledge based map of the embodiment of the present application are set
It is standby.
Fig. 1 is a kind of flow diagram of the input prediction method of knowledge based map provided by the embodiment of the present application.
As shown in Figure 1, method includes the following steps:
Step 101, it obtains and inputs the text before cursor in input frame, and obtain current pinyin character string.
Step 102, word cutting is carried out to text, obtains multiple participles in text, and carry out to current pinyin character string
Encoding of chinese characters.
In practical applications, user can according to need instant messaging application dialogue input frame or be to search
The positions such as the search input frame of rope application program input text.
Optionally, it obtains user and inputs the text before cursor in input frame, it is to be understood that the text can be one
A word or be in short or one section words etc..
To carry out word cutting processing to text by word cutting model or word cutting algorithm, it is multiple points corresponding to obtain text
Word, for example, text be " Xiao Ming's ", to text carry out word cutting after obtain " Xiao Ming ", " ".
Optionally, user is obtained in the pinyin character string of input, it is to be understood that the pinyin character string can be one
The pinyin character string of word or be the pinyin character string of a word or the pinyin character string etc. of one section of word.
Optionally, the available current phonetic word of encoding of chinese characters is carried out to the current pinyin character string in current input interface
Corresponding Chinese character, that is, the text information of symbol string.
Step 103, whether detect in multiple participles includes the first keyword, and detection after encoding of chinese characters whether include
Relation chain is inquired preset knowledge mapping database and is obtained and the first key if knowing including the first keyword and relation chain
Word and corresponding second keyword of relation chain.
Step 104, the corresponding encoding of chinese characters of relation chain and the second keyword are shown and predicts column in cloud.
Optionally, whether after obtaining multiple participles, can detecte in multiple participles includes the first keyword, is illustrated
It is as follows:
The first example, by whether inquiring in preset knowledge mapping database including the first keyword.
Second of example identifies multiple participles by default entity identification algorithms, obtains corresponding multiple entities,
If multiple entities are matched to the first keyword in preset relation chain dictionary, it is determined that include first crucial in multiple participles
Word.Such as text is " Xiao Ming's ", to text obtain after word cutting " Xiao Ming ", " ", pass through default entity identification algorithms pair
Multiple participles are identified to obtain entity " Xiao Ming ", and it is " small to be then matched to the first keyword in preset relation chain dictionary
It is bright ".
That is, can directly be inquired by preset knowledge mapping database, it can also be by the way that relation chain word be arranged
Library is inquired.
Similarly, the available current pinyin character of encoding of chinese characters is carried out to the current pinyin character string in current input interface
Go here and there after corresponding Chinese character, can directly be inquired by preset knowledge mapping database in preset knowledge mapping database whether
It including relation chain or is identified to the corresponding Chinese character of current pinyin character string, corresponding multiple entities is obtained, multiple
Entity is matched to relation chain in preset relation chain dictionary.
In the embodiment of the present application, knowledge mapping is then knowledge based itself, and all knowledge accumulation in internet are got up,
Really understand real world, " information ", which is collected, to rise becomes " knowledge " accumulation, with the knowledge understanding world.
Optionally, preset knowledge mapping database acquisition the second pass corresponding with the first keyword and relation chain is being inquired
Before keyword, need to generate preset knowledge mapping database, specific as shown in Figure 2:
Step 201, multiple knowledge entries are obtained.
Step 202, multiple knowledge entries are identified, obtains the keyword and relation chain in each knowledge entry.
Step 203, multiple keywords and multiple relation chains are subjected to storage according to preset mode and generate preset knowledge
Spectrum data library.
Specifically, all knowledge entries in internet are collected and identify the keyword in each knowledge entry and pass
Multiple keywords and multiple relation chains are carried out storage according to preset mode and generate preset knowledge mapping database by tethers,
For example " wife of Xiao Ming is small red " is identified to obtain keyword " Xiao Ming " and " small red " and relation chain " wife ", thus will
" Xiao Ming ", " wife " and " small red " are carried out storage in a manner of mapping table etc. and generate preset knowledge mapping database.
Therefore, preset knowledge mapping database can be inquired and obtain the second pass corresponding with the first keyword and relation chain
Keyword, and the corresponding encoding of chinese characters of relation chain and the second keyword are shown and predict column in cloud.
For example, it as shown in figure 3, obtaining the text before inputting cursor in input frame is " Xiao Ming's ", obtains in text
Multiple participles be " Xiao Ming ", " ", and obtain current pinyin character string " laopo ", and encoding of chinese characters is carried out to " laopo "
Obtain corresponding Chinese character " wife ", detection " Xiao Ming ", " " in include that the first keyword " Xiao Ming " and detection Chinese character " wife " are wrapped
Relation chain " wife " is included, preset knowledge mapping database is inquired and obtains the second keyword corresponding with " Xiao Ming " and " wife "
" wife " " small red " show is predicted column in cloud by " small red ".
In the input prediction method of the knowledge based map of the present embodiment, by obtaining the text before inputting cursor in input frame
This, and current pinyin character string is obtained, word cutting is carried out to text, obtains multiple participles in text, and obtain current phonetic
Character string, and encoding of chinese characters is carried out to current pinyin character string, whether detect in multiple participles includes the first keyword, Yi Jijian
Whether survey after encoding of chinese characters includes that relation chain if knowing including the first keyword and relation chain inquires preset knowledge graph
Modal data library obtains corresponding with the first keyword and relation chain the second keyword, finally by the corresponding encoding of chinese characters of relation chain with
Second keyword, which is shown, predicts column in cloud.It is directed to the input text of user as a result, carries out semantic analysis and understanding, and pass through
Knowledge mapping database carries out the association of word, can quickly provide reasonable input and recommend, greatly improve user's
Communication efficiency.
Based on the above embodiment, it will also be appreciated that relation chain is multiple situation in multiple participles, can as one kind
It is able to achieve mode, relation chain includes: the first relation chain and the second relation chain, according to the first keyword and the first relation chain default
Knowledge mapping database in obtain corresponding third keyword, according to third keyword and the second relation chain in preset knowledge
Corresponding second keyword is obtained in spectrum data library.
For example, obtaining the text before inputting cursor in input frame is " mother of the wife of Xiao Ming ", is obtained in text
Multiple participles be " Xiao Ming ", " ", and obtain current pinyin character string " laopodemama ", and to " laopodemama "
Carry out encoding of chinese characters and obtain corresponding Chinese character " mother of wife ", detection " Xiao Ming ", " " in include the first keyword " Xiao Ming "
Include relation chain " wife " and " mother " with Chinese character " mother of wife ", inquire preset knowledge mapping database obtain with it is " small
It is bright " and " wife " corresponding third keyword " small red ", it is " small then to obtain " small red " and " mother " corresponding second keyword
It is beautiful ", " mother of wife is small beautiful " is shown and predicts column in cloud.Further progress semantic analysis and understanding, and quickly provide conjunction
The input of reason is recommended, and improves the flexibility that input is recommended, greatly improves the communication efficiency of user.
Based on the above embodiment, it will also be appreciated that obtained and the first pass inquiring preset knowledge mapping database
It, can be according to the first keyword and the second keyword in preset hot spot word after keyword and corresponding second keyword of relation chain
It is matched to the 4th keyword in Kuku, the 4th keyword is shown into the second predeterminated position on prediction column.Hot spot word among the above
Library is the word for obtaining click volume or volumes of searches in network in advance and being greater than preset threshold, and carries out heat according to click volume or volumes of searches
Point word sequence.By according to the first keyword and the second Keywords matching to hot spot word in the hot spot word of sequence first be set to the 4th
Keyword, or the forward N number of hot spot word that will sort are set to the 4th keyword.
For example, obtaining the text before inputting cursor in input frame is " Xiao Ming's ", obtains multiple participles in text
For " Xiao Ming " and " ", and obtain current pinyin character string " laopo ", and encoding of chinese characters is carried out to " laopo " and is corresponded to
Chinese character " wife ", detection " Xiao Ming ", " " in include the first keyword " Xiao Ming " and detect Chinese character " wife " include relation chain
" wife " inquires preset knowledge mapping database and obtains the second keyword " small red " corresponding with " Xiao Ming " and " wife ", will
" Xiao Ming " and " small red " is matched to the 4th keyword " little Hua " in preset hot spot dictionary library, and " little Hua " is shown on prediction column
The second predeterminated position, such as prediction column the second bit selecting set, further satisfaction user input demand.
In order to realize above-described embodiment, the application also proposes a kind of input prediction device of knowledge based map.
Fig. 4 is a kind of structural schematic diagram of the input prediction device of knowledge based map provided by the embodiments of the present application.
As shown in figure 4, the device includes: to obtain module 41, obtain coding module 42, detection module 43, inquiry acquisition mould
Block 44 and display module 45.
Wherein, module 41 is obtained, for obtaining the text before inputting cursor in input frame, and obtains current pinyin character
String.
Coding module 42 is obtained, for carrying out word cutting to text, obtains multiple participles in text, and to current phonetic
Character string carries out encoding of chinese characters.
Whether detection module 43 includes the first keyword for detecting in multiple participles, and detection is after encoding of chinese characters
It whether include relation chain.
Inquiry obtains module 44, if inquiring preset knowledge mapping for knowing including the first keyword and relation chain
Database obtains the second keyword corresponding with the first keyword and relation chain.
Display module 45 predicts column in cloud for showing the corresponding encoding of chinese characters of relation chain and the second keyword.
Based on the above embodiment, the embodiment of the present application also provides a kind of input prediction device of knowledge based map can
The implementation of energy, Fig. 5 are the structure of the input prediction device of another kind knowledge based map provided by the embodiment of the present application
Schematic diagram, on the basis of fig. 4, described device further include: first, which obtains module 46, second, obtains module 47 and generation module
48。
Wherein, first module 46 is obtained, for obtaining multiple knowledge entries.
Second acquisition module 47 obtains the keyword in each knowledge entry for identifying to multiple knowledge entries
And relation chain.
Generation module 48, it is default for multiple keywords and multiple relation chains to be carried out storage generation according to preset mode
Knowledge mapping database.
Based on the above embodiment, the embodiment of the present application also provides a kind of input prediction device of knowledge based map can
The implementation of energy, detection module 43 are specifically used for: being identified, obtained to multiple participles by default entity identification algorithms
Corresponding multiple entities；If multiple entities are matched to the first keyword in preset relation chain dictionary, it is determined that multiple participles
In include the first keyword.
Based on the above embodiment, the embodiment of the present application also provides a kind of input prediction device of knowledge based map can
The implementation of energy, relation chain includes: the first relation chain and the second relation chain；Inquiry obtains module 44, is specifically used for: according to the
One keyword and the first relation chain obtain corresponding third keyword in preset knowledge mapping database；According to third key
Word and the second relation chain obtain corresponding second keyword in preset knowledge mapping database.
The embodiment of the present application also provides a kind of possible implementation of the input prediction device of knowledge based map, figures
6 be the structural schematic diagram of the input prediction device of another knowledge based map provided by the embodiment of the present application, in the base of Fig. 4
On plinth, described device further include: matching module 49.
Matching module 49, for being matched in preset hot spot dictionary library according to the first keyword and the second keyword
Four keywords.
Display module 45 is also used to showing the 4th keyword into the second predeterminated position on prediction column.
It should be noted that the aforementioned device that the embodiment is also applied for the explanation of embodiment of the method, herein not
It repeats again.
In the input prediction device of the knowledge based map of the present embodiment, by obtaining the text before inputting cursor in input frame
This, and obtain current pinyin character string；Word cutting is carried out to text, obtains multiple participles in text, and obtain current phonetic word
Symbol string, and encoding of chinese characters is carried out to current pinyin character string, whether detect in multiple participles includes the first keyword, Yi Jijian
Whether survey after encoding of chinese characters includes that relation chain if knowing including the first keyword and relation chain inquires preset knowledge graph
Modal data library obtains corresponding with the first keyword and relation chain the second keyword, finally by the corresponding encoding of chinese characters of relation chain with
Second keyword, which is shown, predicts column in cloud.It is directed to the input text of user as a result, carries out semantic analysis and understanding, and pass through
Knowledge mapping database carries out the association of word, can quickly provide reasonable input and recommend, greatly improve user's
Communication efficiency.
In order to realize above-described embodiment, the application also proposes a kind of electronic equipment, comprising: memory, processor and storage
On a memory and the computer program that can run on a processor, it when processor executes the program, realizes as preceding method is real
Apply the input prediction method of knowledge based map described in example.
The embodiment of the present application also provides a kind of electronic equipment, and electronic equipment includes device described in aforementioned any embodiment.
Fig. 7 is the structural schematic diagram of the application electronic equipment one embodiment, and method shown in the application Fig. 1-2 may be implemented
The process of embodiment, as shown in fig. 7, above-mentioned electronic equipment may include: shell 91, processor 92, memory 93, circuit board 94
With power circuit 95, wherein circuit board 94 is placed in the space interior that shell 91 surrounds, and processor 92 and the setting of memory 93 exist
On circuit board 94；Power circuit 95, for each circuit or the device power supply for above-mentioned electronic equipment；Memory 93 is for storing
Executable program code；Processor 92 is run and executable journey by reading the executable program code stored in memory 93
The corresponding program of sequence code, for executing video generation method described in aforementioned any embodiment.
Processor 92 to the specific implementation procedures of above-mentioned steps and processor 92 by operation executable program code come
The step of further executing may refer to the description of embodiment of the method shown in the application Fig. 1-2, and details are not described herein.
The electronic equipment exists in a variety of forms, including but not limited to:
(1) mobile communication equipment: the characteristics of this kind of equipment is that have mobile communication function, and to provide speech, data
Communication is main target.This Terminal Type includes: smart phone (such as iPhone), multimedia handset, functional mobile phone and low
Hold mobile phone etc..
(2) super mobile personal computer equipment: this kind of equipment belongs to the scope of personal computer, there is calculating and processing function
Can, generally also have mobile Internet access characteristic.This Terminal Type includes: PDA, MID and UMPC equipment etc., such as iPad.
(3) portable entertainment device: this kind of equipment can show and play multimedia content.Such equipment include: audio,
Video player (such as iPod), handheld device, e-book and intelligent toy and portable car-mounted navigation equipment.
(4) server: providing the equipment of the service of calculating, and the composition of server includes that processor, hard disk, memory, system are total
Line etc., server is similar with general computer architecture, but due to needing to provide highly reliable service, in processing energy
Power, stability, reliability, safety, scalability, manageability etc. are more demanding.
(5) other electronic equipments with data interaction function.
In order to realize above-described embodiment, the application also proposes a kind of computer readable storage medium, is stored thereon with calculating
Machine program when the program is executed by processor, realizes the input prediction of the knowledge based map as described in preceding method embodiment
Method.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is contained at least one embodiment or example of the application.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiment or examples.In addition, without conflicting with each other, the skill of this field
Art personnel can tie the feature of different embodiments or examples described in this specification and different embodiments or examples
It closes and combines.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include at least one this feature.In the description of the present application, the meaning of " plurality " is at least two, such as two, three
It is a etc., unless otherwise specifically defined.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing custom logic function or process the step of executable instruction code module, segment or portion
Point, and the range of the preferred embodiment of the application includes other realization, wherein can not press shown or discussed suitable
Sequence, including according to related function by it is basic simultaneously in the way of or in the opposite order, Lai Zhihang function, this should be by the application
Embodiment person of ordinary skill in the field understood.
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered use
In the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, for
Instruction execution system, device or equipment (such as computer based system, including the system of processor or other can be held from instruction
The instruction fetch of row system, device or equipment and the system executed instruction) it uses, or combine these instruction execution systems, device or set
It is standby and use.For the purpose of this specification, " computer-readable medium ", which can be, any may include, stores, communicates, propagates or pass
Defeated program is for instruction execution system, device or equipment or the dress used in conjunction with these instruction execution systems, device or equipment
It sets.The more specific example (non-exhaustive list) of computer-readable medium include the following: there is the electricity of one or more wirings
Interconnecting piece (electronic device), portable computer diskette box (magnetic device), random access memory (RAM), read-only memory
(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device and portable optic disk is read-only deposits
Reservoir (CDROM).In addition, computer-readable medium can even is that the paper that can print described program on it or other are suitable
Medium, because can then be edited, be interpreted or when necessary with it for example by carrying out optical scanner to paper or other media
His suitable method is handled electronically to obtain described program, is then stored in computer storage.
It should be appreciated that each section of the application can be realized with hardware, software, firmware or their combination.Above-mentioned
In embodiment, software that multiple steps or method can be executed in memory and by suitable instruction execution system with storage
Or firmware is realized.Such as, if realized with hardware in another embodiment, following skill well known in the art can be used
Any one of art or their combination are realized: have for data-signal is realized the logic gates of logic function from
Logic circuit is dissipated, the specific integrated circuit with suitable combinational logic gate circuit, programmable gate array (PGA), scene can compile
Journey gate array (FPGA) etc..
Those skilled in the art are understood that realize all or part of step that above-described embodiment method carries
It suddenly is that relevant hardware can be instructed to complete by program, the program can store in a kind of computer-readable storage medium
In matter, which when being executed, includes the steps that one or a combination set of embodiment of the method.
It, can also be in addition, can integrate in a processing module in each functional unit in each embodiment of the application
It is that each unit physically exists alone, can also be integrated in two or more units in a module.Above-mentioned integrated mould
Block both can take the form of hardware realization, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized and when sold or used as an independent product in the form of software function module, also can store in a computer
In read/write memory medium.
Storage medium mentioned above can be read-only memory, disk or CD etc..Although having been shown and retouching above
Embodiments herein is stated, it is to be understood that above-described embodiment is exemplary, and should not be understood as the limit to the application
System, those skilled in the art can be changed above-described embodiment, modify, replace and become within the scope of application
Type.
Claims (10)
1. a kind of input prediction method of knowledge based map, which comprises the following steps:
It obtains and inputs the text before cursor in input frame, and obtain current pinyin character string；
Word cutting is carried out to the text, obtains multiple participles in the text, and carry out to the current pinyin character string
Encoding of chinese characters；
Whether include first keyword, and whether detection includes relationship after the encoding of chinese characters if detecting in the multiple participle
Chain；
If knowing including first keyword and the relation chain, inquire preset knowledge mapping database obtain with it is described
First keyword and corresponding second keyword of the relation chain；
The corresponding encoding of chinese characters of the relation chain and second keyword are shown and predict column in cloud.
2. the method as described in claim 1, which is characterized in that in the preset knowledge mapping database acquisition of the inquiry and institute
Before stating the first keyword and corresponding second keyword of the relation chain, further includes:
Obtain multiple knowledge entries；
The multiple knowledge entry is identified, the keyword and relation chain in each knowledge entry are obtained；
Multiple keywords and multiple relation chains are subjected to storage according to preset mode and form preset knowledge mapping database.
3. the method as described in claim 1, which is characterized in that whether include first crucial in the multiple participle of detection
Word, comprising:
The multiple participle is identified by default entity identification algorithms, obtains corresponding multiple entities；
If the multiple entity is matched to the first keyword in preset relation chain dictionary, it is determined that wrapped in the multiple participle
Include the first keyword.
4. the method as described in claim 1, which is characterized in that the relation chain includes: the first relation chain and the second relation chain；
The preset knowledge mapping database of inquiry obtains corresponding with first keyword and the relation chain second and closes
Keyword, comprising:
Corresponding third is obtained in preset knowledge mapping database according to first keyword and first relation chain
Keyword；
Corresponding second is obtained in preset knowledge mapping database according to the third keyword and second relation chain
Keyword.
5. the method as described in claim 1, which is characterized in that in the preset knowledge mapping database acquisition of the inquiry and institute
After stating the first keyword and corresponding second keyword of the relation chain, further includes:
The 4th keyword is matched in preset hot spot dictionary library according to first keyword and second keyword；
4th keyword is shown into the second predeterminated position on prediction column.
6. a kind of input prediction device of knowledge based map, which is characterized in that described device includes:
Module is obtained, for obtaining the text before inputting cursor in input frame, and obtains current pinyin character string；
Coding module is obtained, for carrying out word cutting to the text, obtains multiple participles in the text, and work as to described
Preceding pinyin character string carries out encoding of chinese characters；
Whether detection module includes the first keyword, and detection in the encoding of chinese characters for detecting in the multiple participle
It whether include afterwards relation chain；
Inquiry obtains module, if inquiring preset knowledge graph for knowing including first keyword and the relation chain
Modal data library obtains the second keyword corresponding with first keyword and the relation chain；
Display module predicts column in cloud for showing the corresponding encoding of chinese characters of the relation chain and second keyword.
7. device as claimed in claim 6, which is characterized in that further include:
First obtains module, for obtaining multiple knowledge entries；
Second obtains module, for being identified to the multiple knowledge entry, obtain keyword in each knowledge entry and
Relation chain；
Generation module generates preset knowledge for multiple keywords and multiple relation chains to be carried out storage according to preset mode
Spectrum data library.
8. device as claimed in claim 6, which is characterized in that the detection module is specifically used for:
The multiple participle is identified by default entity identification algorithms, obtains corresponding multiple entities；
If the multiple entity is matched to the first keyword in preset relation chain dictionary, it is determined that wrapped in the multiple participle
Include the first keyword.
9. device as claimed in claim 6, which is characterized in that the relation chain includes: the first relation chain and the second relation chain；
The inquiry obtains module, is specifically used for:
Corresponding third is obtained in preset knowledge mapping database according to first keyword and first relation chain
Keyword；
Corresponding second is obtained in preset knowledge mapping database according to the third keyword and second relation chain
Keyword.
10. device as claimed in claim 6, which is characterized in that further include:
Matching module, for being matched in preset hot spot dictionary library according to first keyword and second keyword
4th keyword；
The display module is also used to showing the 4th keyword into the second predeterminated position on prediction column.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110096599A (en) * | 2019-04-30 | 2019-08-06 | 长沙知了信息科技有限公司 | The generation method and device of knowledge mapping |
| CN110941694A (en) * | 2019-10-14 | 2020-03-31 | 珠海格力电器股份有限公司 | Knowledge graph searching and positioning method and system, electronic equipment and storage medium |
| CN111353047A (en) * | 2019-12-30 | 2020-06-30 | 常熟鑫沐奇宝软件开发有限公司 | Method for generating artificial intelligence knowledge map based on Chinese character making method |
| CN112507129B (en) * | 2020-12-07 | 2023-09-08 | 云南电网有限责任公司普洱供电局 | Content change processing method of power distribution network operation file and related equipment |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH01260538A (en) * | 1988-04-11 | 1989-10-17 | Nippon Telegr & Teleph Corp <Ntt> | Device for generating experiental knowledge in knowledge base system |
| WO2005091120A1 (en) * | 2004-03-19 | 2005-09-29 | Dong Li | Hand writing input method and device for portable terminal |
| CN104142938A (en) * | 2013-05-07 | 2014-11-12 | 腾讯科技（深圳）有限公司 | Data processing method, device and system of micro-blog |
| CN106293114A (en) * | 2015-06-02 | 2017-01-04 | 阿里巴巴集团控股有限公司 | The method and device of prediction user's word to be entered |
| CN106527757A (en) * | 2016-10-28 | 2017-03-22 | 上海智臻智能网络科技股份有限公司 | Input error correction method and apparatus |
| CN106873801A (en) * | 2017-02-28 | 2017-06-20 | 百度在线网络技术（北京）有限公司 | Method and apparatus for generating the combination of the entry in input method dictionary |
| CN106896935A (en) * | 2017-02-22 | 2017-06-27 | 李晓明 | Input method |
| CN107169010A (en) * | 2017-03-31 | 2017-09-15 | 北京奇艺世纪科技有限公司 | A kind of determination method and device of recommendation search keyword |
| CN107358315A (en) * | 2017-06-26 | 2017-11-17 | 深圳市金立通信设备有限公司 | A kind of information forecasting method and terminal |
| CN107704634A (en) * | 2017-11-04 | 2018-02-16 | 辽宁工程技术大学 | A kind of method for forming knowledge and building knowledge chain |
| CN107765883A (en) * | 2016-08-22 | 2018-03-06 | 富士通株式会社 | The sort method and sequencing equipment of candidate's word of input method |
| CN107908671A (en) * | 2017-10-25 | 2018-04-13 | 南京擎盾信息科技有限公司 | Knowledge mapping construction method and system based on law data |
| CN108121455A (en) * | 2016-11-29 | 2018-06-05 | 渡鸦科技（北京）有限责任公司 | Identify method and device for correcting |
| CN109086408A (en) * | 2018-08-02 | 2018-12-25 | 腾讯科技（深圳）有限公司 | Document creation method, device, electronic equipment and computer-readable medium |
-
2018
- 2018-12-28 CN CN201811621360.8A patent/CN109656385B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH01260538A (en) * | 1988-04-11 | 1989-10-17 | Nippon Telegr & Teleph Corp <Ntt> | Device for generating experiental knowledge in knowledge base system |
| WO2005091120A1 (en) * | 2004-03-19 | 2005-09-29 | Dong Li | Hand writing input method and device for portable terminal |
| CN104142938A (en) * | 2013-05-07 | 2014-11-12 | 腾讯科技（深圳）有限公司 | Data processing method, device and system of micro-blog |
| CN106293114A (en) * | 2015-06-02 | 2017-01-04 | 阿里巴巴集团控股有限公司 | The method and device of prediction user's word to be entered |
| CN107765883A (en) * | 2016-08-22 | 2018-03-06 | 富士通株式会社 | The sort method and sequencing equipment of candidate's word of input method |
| CN106527757A (en) * | 2016-10-28 | 2017-03-22 | 上海智臻智能网络科技股份有限公司 | Input error correction method and apparatus |
| CN108121455A (en) * | 2016-11-29 | 2018-06-05 | 渡鸦科技（北京）有限责任公司 | Identify method and device for correcting |
| CN106896935A (en) * | 2017-02-22 | 2017-06-27 | 李晓明 | Input method |
| CN106873801A (en) * | 2017-02-28 | 2017-06-20 | 百度在线网络技术（北京）有限公司 | Method and apparatus for generating the combination of the entry in input method dictionary |
| CN107169010A (en) * | 2017-03-31 | 2017-09-15 | 北京奇艺世纪科技有限公司 | A kind of determination method and device of recommendation search keyword |
| CN107358315A (en) * | 2017-06-26 | 2017-11-17 | 深圳市金立通信设备有限公司 | A kind of information forecasting method and terminal |
| CN107908671A (en) * | 2017-10-25 | 2018-04-13 | 南京擎盾信息科技有限公司 | Knowledge mapping construction method and system based on law data |
| CN107704634A (en) * | 2017-11-04 | 2018-02-16 | 辽宁工程技术大学 | A kind of method for forming knowledge and building knowledge chain |
| CN109086408A (en) * | 2018-08-02 | 2018-12-25 | 腾讯科技（深圳）有限公司 | Document creation method, device, electronic equipment and computer-readable medium |
Non-Patent Citations (1)
| Title |
|---|
| 梁文娟: "汉字语法语义智能输入法搭配库设计与实现", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110096599A (en) * | 2019-04-30 | 2019-08-06 | 长沙知了信息科技有限公司 | The generation method and device of knowledge mapping |
| CN110096599B (en) * | 2019-04-30 | 2023-03-21 | 长沙知了信息科技有限公司 | Knowledge graph generation method and device |
| CN110941694A (en) * | 2019-10-14 | 2020-03-31 | 珠海格力电器股份有限公司 | Knowledge graph searching and positioning method and system, electronic equipment and storage medium |
| CN111353047A (en) * | 2019-12-30 | 2020-06-30 | 常熟鑫沐奇宝软件开发有限公司 | Method for generating artificial intelligence knowledge map based on Chinese character making method |
| CN112507129B (en) * | 2020-12-07 | 2023-09-08 | 云南电网有限责任公司普洱供电局 | Content change processing method of power distribution network operation file and related equipment |
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