CN109522465A - The semantic searching method and device of knowledge based map - Google Patents
The semantic searching method and device of knowledge based map Download PDFInfo
- Publication number
- CN109522465A CN109522465A CN201811231211.0A CN201811231211A CN109522465A CN 109522465 A CN109522465 A CN 109522465A CN 201811231211 A CN201811231211 A CN 201811231211A CN 109522465 A CN109522465 A CN 109522465A
- Authority
- CN
- China
- Prior art keywords
- user
- search
- semantic
- search information
- based map
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000013507 mapping Methods 0.000 claims abstract description 35
- 238000004422 calculation algorithm Methods 0.000 claims description 16
- 238000012937 correction Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 7
- 238000013519 translation Methods 0.000 claims description 6
- 230000005055 memory storage Effects 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 description 13
- 238000010586 diagram Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 7
- 230000008569 process Effects 0.000 description 6
- 238000013499 data model Methods 0.000 description 5
- 238000003058 natural language processing Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000009825 accumulation Methods 0.000 description 3
- 238000009833 condensation Methods 0.000 description 3
- 230000005494 condensation Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 239000012634 fragment Substances 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 206010028916 Neologism Diseases 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000007596 consolidation process Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 238000006116 polymerization reaction Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000007711 solidification Methods 0.000 description 1
- 230000008023 solidification Effects 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Abstract
This application discloses the semantic searching methods and device of a kind of knowledge based map, are related to searching engine field, for meeting the search need of user semantic level, to provide accurately and efficiently search experience for user.This method comprises: obtaining search information；Semantics recognition is carried out to described search information according to the knowledge mapping, to obtain user's intention；According to the user it is intended to that the first instance set is made inferences and inquired, to obtain second instance set, wherein the second instance collection, which is combined into the first instance set, is intended to the high entity sets of correlation with the user；User is notified using the second instance set as search result.The embodiment of the present application is applied to the semantic search of knowledge based map.
Description
Technical field
The present invention relates to searching engine field more particularly to a kind of semantic searching methods and device of knowledge based map
Background technique
As the accumulation of internet data amount is only obtained people are no longer content with only obtaining search and webpage set
Several document links relevant to user query, and be desirable to obtain the search result of the answer formula of more specificization.Especially exist
The accuracy of electric business industry, user's search is directly experienced concerning business.
Existing search technique is although achieve huge success, when user carries out information search, existing search
Technology can not carry out accurate semantization understanding to the search intention of user, especially in electric business industry, the accuracy of user's search
Directly experienced concerning business.
In electric business platform during operation, the part of most critical is exactly to understand user search intent to show corresponding goods
List, wherein also including the intervention such as automatic shopping guide, intelligent customer service to realize automation sale.Existing search technique has can not
User semantic is analyzed, can not identify the disadvantages of ambiguity trade name.
Summary of the invention
Embodiments herein provides the semantic searching method and device of a kind of knowledge based map, for meeting user's language
The search need of adopted level, to provide accurately and efficiently search experience for user.
In order to achieve the above objectives, embodiments herein adopts the following technical scheme that
In a first aspect, providing a kind of semantic searching method of knowledge based map, the knowledge mapping includes first real
The connection relationship between multiple entities in body set and the first instance set this method comprises:
Obtain search information；
Semantics recognition is carried out to described search information according to the incidence relation, to obtain user's intention；
According to the user it is intended to that the first instance set is made inferences and inquired, to obtain second instance set,
Wherein, the second instance collection, which is combined into the first instance set, is intended to the high entity sets of correlation with the user；
User is notified using the second instance set as search result.
Second aspect provides a kind of device, which includes:
Acquiring unit, for obtaining search information；
Recognition unit, for the association between multiple entities in multiple first instance set according to the knowledge mapping
Relationship carries out semantics recognition to described search information, to obtain user's intention；
Search unit, for according to the user being intended to that the first instance set of the knowledge mapping is made inferences and looked into
It askes, to obtain second instance set, wherein the second instance collection is combined into the first instance set to be intended to the user
The high entity sets of correlation；
Notification unit, for notifying user using the second instance set as search result.
The third aspect, provides a kind of computer readable storage medium for storing one or more programs, it is one or
Multiple programs include instruction, and described instruction makes the side of the computer execution as described in relation to the first aspect when executed by a computer
Method.
Fourth aspect provides a kind of computer program product comprising instruction, when described instruction is run on computers
When, so that computer executes semantic searching method as described in relation to the first aspect.
5th aspect, provides a kind of semantic search device, comprising: and processor and memory, memory are used to store program,
Processor calls the program of memory storage, to execute semantic searching method described in above-mentioned first aspect.
The semantic searching method for the knowledge based map that embodiments herein provides, by electric business knowledge mapping to user
Input carries out semantic analysis, obtains user and is precisely intended to, and then user is intended to scan for the entity in knowledge mapping, returns
Its most association search is returned as a result, improving the search experience of user.By semantic search, user, which can express, more complicated completely to be searched
Rope demand, and search result is also more close to the users the answer of demand, while can also be improved intention and understanding and search result
Efficiency and accuracy rate.
Detailed description of the invention
Fig. 1 is a kind of semantic search system structure diagram for knowledge based map that embodiments herein provides；
Fig. 2 is a kind of semantic searching method schematic diagram one for knowledge based map that embodiments herein provides；
Fig. 3 is a kind of semantic searching method schematic diagram two for knowledge based map that embodiments herein provides；
Fig. 4 is a kind of semantic searching method schematic diagram three for knowledge based map that embodiments herein provides；
Fig. 5 is a kind of semantic searching method schematic diagram four for knowledge based map that embodiments herein provides；
Fig. 6 is a kind of semantic search apparatus structure schematic diagram one for knowledge based map that embodiments herein provides；
Fig. 7 is a kind of semantic search apparatus structure schematic diagram two for knowledge based map that embodiments herein provides；
Fig. 8 is a kind of semantic search apparatus structure schematic diagram three for knowledge based map that embodiments herein provides.
Specific embodiment
Semantic searching method provided by the embodiments of the present application can be applied to the search system of knowledge based map, reference
Shown in Fig. 1, which may include interface layer 101, search layer 102, storage layer 103 and data Layer 104；
Interface layer 101, i.e., the relevant interface of all search externally provided mainly include main search interface, intelligent prompt
Interface and relevant search interface etc..Wherein, main search interface is the intelligent search main-inlet of knowledge based map；Intelligent prompt connects
Mouth is for searching for the calling of associational word (search term association, error correction, sequence etc.) and hot word data；Relevant search interface is used for user
The associated user of search term searches for content, i.e. association search word；
Layer 102, including search configuration, search kernel model and searching analysis are searched for, wherein search configuration mainly includes point
The configuration of word strategy, synonym setting, index field configuration, ordering strategy configuration, white list configuration, the configuration of violated word, filter word
The Base search configuration items such as configuration；Searching for kernel model includes natural language processing (natural language
Processing, NLP) model, knowledge based map intention assessment model and order models, wherein NLP model is used for: word
Importance analysis, syntactic structure, word position analysis；Order models include: 25 sort algorithm of best match (best match25,
BM25), learn sort algorithm (learning to rank, LTR) and search-engine results sort algorithm (Hilltop)；Search
Analysis is main to be provided for search content, the fundamental analysis function of search result, comprising: search source analysis, heat search word analysis,
Bad case (BadCase) analysis and new word discovery；
Wherein, increasing industry correlation dictionary can recommend more search terms candidate for user, and it is accurate to improve systematic search
Property.Industry dictionary is excavated from user's search and index data, is added in system after screening.The excavation category of industry word
In new word discovery scope.Mining algorithm traverses index data, by the left side for calculating solidification degree and word segment between word and word
Right freedom degree excavates industry word from data.
Optionally, which can be with are as follows: search data slot is cut into word fragment；Pass through two-dimensional grammar (2-
Gram) front and back time is spliced word fragments mosaicing into neologisms, and stores left and right splicing word and adjacent word；Word frequency statistics are carried out, if
Word frequency is less than threshold value, then calculates the freedom degree of word and word and the calculating of condensation degree；Otherwise process terminates；Carry out the freedom of word and word
After degree and condensation degree calculate, by filtering dictionary and artificial judgment, trade information is obtained.Optionally, may be used also after by word fragment
To pass through the freedom degree and condensation degree of iterative calculation word and word.
Accumulation layer 103 mainly includes that index datastore and knowledge mapping data store, in which: index datastore makes
With real-time distributed searching analysis engine (Elastic Search) storage architecture, hundred million grades of contents are supported to handle up, index upgrade reaches
To second grade；Knowledge mapping storage is integrated with diagram data model, relational data model, document data model and keyword value
(Key-Value) data model of four kinds of forms of data model, suitable for storing the complex data scene of a variety of data modes, bottom
Different basic repositories can be used in layer, has been integrated with Relational DBMS (microsoft at present
Structure quest language, MySQL), the database (MongoDB), distributed based on distributed document storage
The storage on the backstages such as PostgreSQL database (Hadoop Database, HBase) and computing engines (Spark) towards column is applicable in not
With the application demand of user.
Data Layer 104.That is original data layer is broadly divided into comprising all types of user data, daily record data, access information etc.
Structural data, semi-structured data and unstructured data etc.；
Optionally, which can be electric business knowledge mapping.
The main running environment of semantic searching method of knowledge based map provided by the embodiments of the present application is above-mentioned search system
The search layer of system.
The semantic searching method and device of knowledge based map provided by the embodiments of the present application, wherein knowledge mapping includes
The incidence relation of multiple entities in first instance set and first instance set.Search information is obtained, according to knowledge mapping pair
It searches for information and carries out semantics recognition, to obtain user's intention；According to user it is intended to that first instance set is made inferences and inquired,
To obtain being intended to the high second instance set of correlation with user；User is notified using second instance set as search result.
Embodiment 1,
The embodiment of the present application provides a kind of semantic searching method of knowledge based map, which may include
The incidence relation of multiple entities in one entity sets and first instance set, referring to fig. 2, this method includes S101-
S104:
S101, search information is obtained.
When user inputs information by the interface layer of the search system of knowledge based map, which can be physical name
Claim, is also possible to customer problem.
Optionally, referring to fig. 3, S101 may include S201-S202:
If S201, search information are phonetic, Chinese character is converted to for information is searched for according to phonetic library.
Wherein, phonetic library includes the pinyin marking of industry dictionary.
When the information of user's input occur text it is wrong it is defeated for phonetic when, search system can be automatically converted into Chinese character, and can
To identify polyphone situation.Phonetic turns Chinese character dependent on phonetic library, and phonetic library derives from the pinyin marking of industry dictionary.Industry word
Library can be the dictionary of electric business industry.
If S202, search information are wrong Chinese character, according to editing distance algorithm and industry dictionary is combined to search phase
Like the high Chinese character of degree.
When spelling words mistake, phonetically similar word occurs in the information of user's input, search system can be with automatic identification error correction.The Chinese
The error correction of word is realized using editing distance algorithm, searches Word similarity first in industry dictionary and pinyin similarity is higher
Word is as candidate word, to reduce editing distance computer capacity, calculates each candidate word later and needs the editing distance of error correction term,
The smallest candidate word of editing distance is taken, if editing distance value is more than the error correction threshold values of setting, is returned as a result.
S102, language is carried out to search information according to the incidence relation of multiple entities in knowledge mapping in first instance set
Justice identification, to obtain user's intention.
The integration that the building of knowledge mapping is originated from mass data is handled, and on the basis of knowledge mapping, passes through various logic
Algorithm analysis processing natural language, and semantics recognition is carried out, the crucial matching knowledge mapping for being best suitable for search information is found, thus
Determine that user is intended to.
The search service that knowledge based map construction gets up, with the traditional network search engine phase based on Keywords matching
Than more natural, complicated inquiry can be supported to input, understand the semanteme of query information, and directly give answer for inquiry；With
It is compared based on simple characters String matching traditional search engines, allows search result and inquiry content more consolidation, it can be to avoid across neck
Domain problem inquires deviation.
Optionally, referring to fig. 4, S102 may include S301-S302:
S301, multiple participles are divided into for information is searched for according to knowledge mapping；
Using knowledge mapping as segmentation methods dictionary, the field participle model of knowledge based map is constructed, by searching for user
The participle of rope information knowledge based map is divided into multiple words for information is searched for according to concept, entity, attribute and operator etc.
Language, the problem of carrying out participle understanding by participle model to customer problem, can divide to the full extent and understand user.
S302, semantic disambiguation, semantic understanding and translation are carried out to multiple participles, to obtain user's intention.
Entity recognition is named to the customer problem after participle, the main entity identified in knowledge mapping, concept, category
Property, operator etc., and semantic disambiguation is carried out to the participle entity in customer problem using knowledge mapping, and to the language of customer problem
Reason and good sense solution and translation obtain user and are accurately intended to.
Optionally, the semantic understanding to customer problem and translation can be realized by subgraph understanding and problem analysis module,
The module can be divided into three units: question and answer are to resolution unit, map question and answer resolution unit and business question and answer resolution unit；
Question and answer are to resolution unit: question and answer to parsing on the one hand for the existing standard question and answer data in integrating enterprise inside, more
Accurately answer the enquirement of user；On the other hand with the accumulation of question answering system data, it can refine or take out from customer problem
As standard question and answer information, feeds back into question answering system, question answering system is allowed the more to ask the more intelligent；
Map question and answer resolution unit, map question and answer parse to realize the son that customer problem is mapped to knowledge mapping
On figure, to understand and translate the query intention of user, corresponding quiz answers are searched from map and return to user；
Business question and answer resolution unit, the parsing of business question and answer is main to realize the customer problem knot that will be mapped to knowledge mapping subgraph
Business intelligence (business intelligence, BI) business rule is closed, business BI inquiry is converted to.
Optionally, this method can also include:
Inquiry conversion: figure query language or computing engines knot are converted by the customer problem after semantic understanding and parsing
Structure query language (spark structured query language, Spark SQL) query logic；It sort result and returns
It returns: inquiring the data obtained from knowledge mapping and arranged and sorted, return to user using suitable visualization component.
Optionally, referring to fig. 5, this method can also include S401-S403:
If S401, recognizing user's intention, carrying out semantic and attribute extension to search information and returning to search result.
In intention assessment, intelligent search module has known the intention of user.At this point, carrying out semantic similar word to search term
Extension, semanteme here includes synonym and semantic word.Then, pass through knowledge mapping, the concept attribute of query search target.
On the one hand the extension in search attribute domain is done, filter screen option is on the other hand generated.After the extension of search attribute domain, according to each domain
Search is added in weight, selected section Attribute domain.Then, dynamic calculating is carried out to the condition weight for generating querying condition set.Most
Afterwards, search is executed, and generates the suggestion of user's search
S402, if can not recognize user be intended to and searched for as a result, if to search information carry out it is synonymous extension simultaneously
And provide synonym.
Intention assessment lose one's life in, for the first time search have as a result, intention assessment miss, the keyword after being degenerated to synonymous extension
Search, wherein synonymous extended target promotes the recall rate of search；After synonymous extension, weight dynamic is carried out to expansion word set
It calculates.Finally, and by search result attribute polymerization generate filter screen option, finally return to result.
If S403, user's intention can not be recognized and searched for and come to nothing, error correction is carried out to search information, and again
Search.
Intention assessment lose one's life in, search for no result for the first time, it may be possible to keyword error correction can be used in search term mistake itself
It searches again, wherein the major way of error correction is that search term is converted to phonetic, then looks for the phonetic matching maximum word of lower probability；,
Similar calculating can also be carried out according to the manhatton distance of word, be determined whether to carry out error correction according to score and threshold value；If there is
Data return, and the search key for telling user to suggest error correction；If prompting not search data without data.
S103, according to user it is intended to that first instance set is made inferences and inquired, to obtain second instance set.
Wherein, second instance collection, which is combined into first instance set, is intended to the high entity sets of correlation with user.
It should be noted that may include the incidence relation between multiple entities and multiple entities in first instance set,
Therefore it can be intended to the second instance that the screening in the first instance set of knowledge mapping is more in line with user demand according to user
Set.
S104, user is notified using second instance set as search result.
Optionally, it after obtaining being intended to the high second instance set of correlation with user, can be incited somebody to action by sort algorithm
Multiple entities in second instance set are arranged according to certain sequence, check search result convenient for user.Wherein, sort algorithm can
To include: (best match25, the BM25) sort algorithm of best match 25 and LTR sort algorithm；User can also make justice-reparation by oneself
The parameter for changing sort algorithm reaches personalized search application.
Semantic searching method and device provided by the embodiments of the present application pass through on the basis of the knowledge mapping that building is completed
Knowledge mapping carries out semantic analysis to the search information that user inputs, and obtains user and is precisely intended to, and then user is intended to and is known
The entity or other mobile bodies known in map make inferences and inquire, return with user be intended to most associated list of entities or
Person's search result improves user's electric business activity experience.By semantic search, user can express more complicated complete search and need
It asks, and search result is also more close to the users the answer of demand, can be improved that intention understands and search is tied using semantic search
The efficiency and accuracy rate of fruit.
Embodiment 2,
The embodiment of the present application provides a kind of semantic search device of knowledge based map, referring to fig. 6, the semanteme
Searcher 100 may include:
Acquiring unit 110, for obtaining search information；
Recognition unit 120, for according to the incidence relation of multiple entities of first instance set in knowledge mapping to search
Information carries out semantics recognition, to obtain user's intention；
Search unit 130, for according to user being intended to that the first instance set of knowledge mapping is made inferences and inquired, with
Obtain second instance set, wherein second instance collection, which is combined into the first instance set, is intended to the high reality of correlation with user
Body set；
Notification unit 140, for notifying user using second instance set as search result.
Optionally, recognition unit 120 is specifically used for: being divided into multiple participles for information is searched for according to knowledge mapping；To more
A participle carries out semantic disambiguation, semantic understanding and translation, to obtain user's intention.
Optionally, referring to fig. 7, which can also include converting unit 210, be used for: if
When search information is phonetic, then Chinese character is converted to for information is searched for according to phonetic library, wherein phonetic library includes the spelling of industry dictionary
Phonetic symbol note；If search information is wrong Chinese character, according to editing distance algorithm and industry dictionary is combined to search similarity height
Chinese character.
Optionally, referring to fig. 8, which can also include judging unit 310, be used for: if known
It is clipped to user's intention, then semantic and attribute extension is carried out to search information and returns to search result；If user can not be recognized
It is intended to and has searched for as a result, then carrying out synonymous extension to search information and providing synonym；If user can not be recognized
It is intended to and search is come to nothing, then error correction is carried out to search information, and re-search for.
The embodiment of the present application provides a kind of computer readable storage medium for storing one or more programs, it is one or
Multiple programs include instruction, and described instruction makes the computer execute the side as described in Fig. 2-Fig. 5 when executed by a computer
Method.
Embodiments herein provides a kind of computer program product comprising instruction, when instruction is run on computers
When, so that computer executes the semantic searching method as described in Fig. 2-Fig. 5.
Embodiments herein provides a kind of semantic search device of knowledge based map, comprising: processor and memory,
Memory is for storing program, and processor calls the program of memory storage, to execute the semantic search as described in Fig. 2-Fig. 5
Method.
By the semantic search device of knowledge based map in the case of this application, computer readable storage medium,
Computer program product can be applied to the above method, therefore, can be obtained technical effect see also above method reality
Example is applied, details are not described herein for the embodiment of the present invention.
It should be noted that above-mentioned each unit can be the processor individually set up, also can integrate controller certain
It is realized in one processor, in addition it is also possible to be stored in the form of program code in the memory of controller, by controller
Some processor calls and executes the function of the above each unit.Processor described here can be a central processing unit
(Central Processing Unit, CPU) or specific integrated circuit (Application Specific
Integrated Circuit, ASIC), or be arranged to implement one or more integrated circuits of the embodiment of the present application.
It should be understood that magnitude of the sequence numbers of the above procedures are not meant to execute suitable in the various embodiments of the application
Sequence it is successive, the execution of each process sequence should be determined by its function and internal logic, the implementation without coping with the embodiment of the present application
Process constitutes any restriction.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually
It is implemented in hardware or software, the specific application and design constraint depending on technical solution.Professional technician
Each specific application can be used different methods to achieve the described function, but this realization is it is not considered that exceed
Scope of the present application.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description,
The specific work process of device and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In several embodiments provided herein, it should be understood that disclosed system, apparatus and method, it can be with
It realizes by another way.For example, apparatus embodiments described above are merely indicative, for example, the unit
It divides, only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components
It can be combined or can be integrated into another system, or some features can be ignored or not executed.Another point, it is shown or
The mutual coupling, direct-coupling or communication connection discussed can be through some interfaces, the indirect coupling of equipment or unit
It closes or communicates to connect, can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.
In the above-described embodiments, can come wholly or partly by software, hardware, firmware or any combination thereof real
It is existing.When being realized using software program, can entirely or partly realize in the form of a computer program product.The computer
Program product includes one or more computer instructions.On computers load and execute computer program instructions when, all or
It partly generates according to process or function described in the embodiment of the present application.The computer can be general purpose computer, dedicated meter
Calculation machine, computer network or other programmable devices.The computer instruction can store in computer readable storage medium
In, or from a computer readable storage medium to the transmission of another computer readable storage medium, for example, the computer
Instruction can pass through wired (such as coaxial cable, optical fiber, number from a web-site, computer, server or data center
Word user line (Digital Subscriber Line, DSL)) or wireless (such as infrared, wireless, microwave etc.) mode to another
A web-site, computer, server or data center are transmitted.The computer readable storage medium can be computer
Any usable medium that can be accessed either includes the numbers such as one or more server, data centers that medium can be used to integrate
According to storage equipment.The usable medium can be magnetic medium (for example, floppy disk, hard disk, tape), optical medium (for example, DVD),
Or semiconductor medium (such as solid state hard disk (Solid State Disk, SSD)) etc..
The above, the only specific embodiment of the application, but the protection scope of the application is not limited thereto, it is any
Those familiar with the art within the technical scope of the present application, can easily think of the change or the replacement, and should all contain
Lid is within the scope of protection of this application.Therefore, the protection scope of the application should be based on the protection scope of the described claims.
Claims (11)
1. a kind of semantic searching method of knowledge based map, which is characterized in that the knowledge mapping includes first instance set
And the incidence relation between multiple entities in the first instance set, the semantic searching method include:
Obtain search information；
Semantics recognition is carried out to described search information according to the incidence relation, to obtain user's intention；
According to the user it is intended to that the first instance set is made inferences and inquired, to obtain second instance set, wherein
The second instance collection, which is combined into the first instance set, is intended to the high entity sets of correlation with the user；
User is notified using the second instance set as search result.
2. the semantic searching method of knowledge based map according to claim 1, which is characterized in that the acquisition search letter
After breath, the semantic searching method includes:
If described search information is phonetic, described search information is converted to by Chinese character according to phonetic library, wherein the spelling
Sound library includes the pinyin marking of industry dictionary；
If described search information is wrong Chinese character, searched according to editing distance algorithm and in conjunction with the industry dictionary similar
Spend high Chinese character.
3. the semantic searching method of knowledge based map according to claim 1, which is characterized in that described to know according to
Know map and semantics recognition carried out to described search information, to obtain user's intention, comprising:
Described search information is divided into multiple participles according to the knowledge mapping；
Semantic disambiguation, semantic understanding and translation are carried out to the multiple participle, are intended to obtaining the user.
4. the semantic searching method of knowledge mapping according to claim 1, which is characterized in that the method also includes:
If recognizing user's intention, carrying out semantic and attribute extension to described search information and returning to search result；
If can not recognize the user be intended to and searched for as a result, if synonymous extension carried out to described search information and mention
For synonym；
If the user can not be recognized to be intended to and search for come to nothing, error correction is carried out to described search information, is laid equal stress on
New search.
5. a kind of semantic search device of knowledge based map characterized by comprising
Acquiring unit, for obtaining search information；
Recognition unit, for the incidence relation between multiple entities in multiple first instance set according to the knowledge mapping
Semantics recognition is carried out to described search information, to obtain user's intention；
Search unit, for according to the user being intended to that the first instance set of the knowledge mapping is made inferences and inquired,
To obtain second instance set, wherein the second instance collection, which is combined into the first instance set, is intended to phase with the user
The entity sets of Guan Xinggao；
Notification unit, for notifying user using the second instance set as search result.
6. the semantic search device of knowledge based map according to claim 5, which is characterized in that the semantic search dress
Setting further includes converting unit, is used for:
If described search information is phonetic, described search information is converted to by Chinese character according to phonetic library, wherein the spelling
Sound library includes the pinyin marking of industry dictionary；
If described search information is wrong Chinese character, searched according to editing distance algorithm and in conjunction with the industry dictionary similar
Spend high Chinese character.
7. the semantic search device of knowledge based map according to claim 5, which is characterized in that the recognition unit tool
Body is used for:
Described search information is divided into multiple participles according to the knowledge mapping；
Semantic disambiguation, semantic understanding and translation are carried out to the multiple participle, are intended to obtaining the user.
8. the semantic search device of knowledge based map according to claim 5, which is characterized in that the semantic search dress
Setting further includes judging unit, is used for:
If recognizing user's intention, carrying out semantic and attribute extension to described search information and returning to search result；
If can not recognize the user be intended to and searched for as a result, if synonymous extension carried out to described search information and mention
For synonym；
If can not recognize, the user is intended to and search is come to nothing, and carries out error correction to described search information, and again
Search.
9. a kind of computer readable storage medium for storing one or more programs, which is characterized in that one or more of journeys
Sequence includes instruction, and it is according to any one of claims 1-4 that described instruction when executed by a computer executes the computer
The semantic searching method of knowledge based map.
10. a kind of computer program product comprising instruction, which is characterized in that when described instruction is run on computers, make
Obtain the semantic searching method that the computer executes knowledge based map according to any one of claims 1-4.
11. a kind of semantic search device of the knowledge based map of knowledge based map characterized by comprising processor and
Memory, memory call the program of memory storage for storing program, processor, to execute as claim 1-4 is any
The semantic searching method of knowledge based map described in.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811231211.0A CN109522465A (en) | 2018-10-22 | 2018-10-22 | The semantic searching method and device of knowledge based map |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811231211.0A CN109522465A (en) | 2018-10-22 | 2018-10-22 | The semantic searching method and device of knowledge based map |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109522465A true CN109522465A (en) | 2019-03-26 |
Family
ID=65772433
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811231211.0A Pending CN109522465A (en) | 2018-10-22 | 2018-10-22 | The semantic searching method and device of knowledge based map |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109522465A (en) |
Cited By (41)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109977294A (en) * | 2019-04-03 | 2019-07-05 | 三角兽（北京）科技有限公司 | Information/query processing device, query processing/text query method, storage medium |
| CN110019687A (en) * | 2019-04-11 | 2019-07-16 | 宁波深擎信息科技有限公司 | A kind of more intention assessment systems, method, equipment and the medium of knowledge based map |
| CN110134796A (en) * | 2019-04-19 | 2019-08-16 | 平安科技（深圳）有限公司 | Clinical test search method, device, computer equipment and the storage medium of knowledge based map |
| CN110147437A (en) * | 2019-05-23 | 2019-08-20 | 北京金山数字娱乐科技有限公司 | A kind of searching method and device of knowledge based map |
| CN110232920A (en) * | 2019-06-21 | 2019-09-13 | 百度在线网络技术（北京）有限公司 | Method of speech processing and device |
| CN110245240A (en) * | 2019-05-31 | 2019-09-17 | 国网电子商务有限公司 | A kind of determination method and device of problem data answer |
| CN110246496A (en) * | 2019-07-01 | 2019-09-17 | 珠海格力电器股份有限公司 | Audio recognition method, system, computer equipment and storage medium |
| CN110264994A (en) * | 2019-07-02 | 2019-09-20 | 珠海格力电器股份有限公司 | A kind of phoneme synthesizing method, electronic equipment and smart home system |
| CN110502613A (en) * | 2019-08-12 | 2019-11-26 | 腾讯科技（深圳）有限公司 | A kind of model training method, intelligent search method, device and storage medium |
| CN110659366A (en) * | 2019-09-24 | 2020-01-07 | Oppo广东移动通信有限公司 | Semantic analysis method and device, electronic equipment and storage medium |
| CN110704743A (en) * | 2019-09-30 | 2020-01-17 | 北京科技大学 | Semantic search method and device based on knowledge graph |
| CN110795417A (en) * | 2019-10-30 | 2020-02-14 | 北京明略软件系统有限公司 | System and method for storing knowledge graph |
| CN111160564A (en) * | 2019-12-17 | 2020-05-15 | 电子科技大学 | Chinese knowledge graph representation learning method based on feature tensor |
| CN111274332A (en) * | 2020-01-19 | 2020-06-12 | 中国科学院计算技术研究所 | Intelligent patent retrieval method and system based on knowledge graph |
| CN111309944A (en) * | 2020-01-20 | 2020-06-19 | 同方知网(北京)技术有限公司 | Digital human search algorithm based on graph database |
| CN111353017A (en) * | 2020-02-20 | 2020-06-30 | 上海智臻智能网络科技股份有限公司 | Intelligent interaction method and device |
| CN111475722A (en) * | 2020-03-31 | 2020-07-31 | 百度在线网络技术（北京）有限公司 | Method and apparatus for transmitting information |
| CN111475623A (en) * | 2020-04-09 | 2020-07-31 | 北京北大软件工程股份有限公司 | Case information semantic retrieval method and device based on knowledge graph |
| CN111552821A (en) * | 2020-05-14 | 2020-08-18 | 北京华宇元典信息服务有限公司 | Legal intention searching method, legal intention searching device and electronic equipment |
| CN111708943A (en) * | 2020-06-12 | 2020-09-25 | 北京搜狗科技发展有限公司 | Search result display method and device and search result display device |
| CN111737499A (en) * | 2020-07-27 | 2020-10-02 | 平安国际智慧城市科技股份有限公司 | Data searching method based on natural language processing and related equipment |
| CN111782942A (en) * | 2020-06-24 | 2020-10-16 | 支付宝(杭州)信息技术有限公司 | Searching method and device based on brand protection |
| CN111859974A (en) * | 2019-04-22 | 2020-10-30 | 广东小天才科技有限公司 | Semantic disambiguation method and device combined with knowledge graph and intelligent learning equipment |
| CN111930913A (en) * | 2020-08-14 | 2020-11-13 | 上海茂声智能科技有限公司 | Knowledge graph-based question and answer method, system, device, equipment and medium |
| CN111930891A (en) * | 2020-07-31 | 2020-11-13 | 中国平安人寿保险股份有限公司 | Retrieval text expansion method based on knowledge graph and related device |
| CN112069808A (en) * | 2020-09-28 | 2020-12-11 | 深圳壹账通智能科技有限公司 | Financing wind control method and device, computer equipment and storage medium |
| CN112131461A (en) * | 2020-09-09 | 2020-12-25 | 重庆易宠科技有限公司 | Commodity searching method, system, terminal and computer readable storage medium |
| CN112231445A (en) * | 2020-03-27 | 2021-01-15 | 北京来也网络科技有限公司 | Searching method, device, equipment and storage medium combining RPA and AI |
| CN112380352A (en) * | 2020-10-28 | 2021-02-19 | 中国商用飞机有限责任公司北京民用飞机技术研究中心 | Interactive retrieval method and device, computer equipment and storage medium |
| CN112507123A (en) * | 2020-12-04 | 2021-03-16 | 北京搜狗科技发展有限公司 | Data processing method and device |
| CN112632225A (en) * | 2020-12-29 | 2021-04-09 | 天津汇智星源信息技术有限公司 | Semantic searching method and device based on case and event knowledge graph and electronic equipment |
| CN112685452A (en) * | 2020-12-31 | 2021-04-20 | 特赞（上海）信息科技有限公司 | Enterprise case retrieval method, device, equipment and storage medium |
| CN112749246A (en) * | 2019-10-30 | 2021-05-04 | 腾讯科技（深圳）有限公司 | Search phrase evaluation method, device, server and storage medium |
| CN112765447A (en) * | 2021-01-27 | 2021-05-07 | 维沃移动通信有限公司 | Data searching method and device and electronic equipment |
| CN112784058A (en) * | 2021-01-11 | 2021-05-11 | 北京欧拉认知智能科技有限公司 | Entity correlation obtaining method based on dynamic map |
| CN113139387A (en) * | 2020-01-17 | 2021-07-20 | 华为技术有限公司 | Semantic error correction method, electronic device and storage medium |
| CN113221578A (en) * | 2021-04-30 | 2021-08-06 | 平安科技（深圳）有限公司 | Disease entity retrieval method, device, equipment and medium |
| CN113434696A (en) * | 2021-06-28 | 2021-09-24 | 平安科技（深圳）有限公司 | Knowledge graph-based search result updating method and device and computer equipment |
| CN114003735A (en) * | 2021-12-24 | 2022-02-01 | 北京道达天际科技有限公司 | Knowledge graph question and answer oriented entity disambiguation method based on intelligence document |
| CN114186075A (en) * | 2021-12-14 | 2022-03-15 | 武汉数文科技有限公司 | Semantic search method for knowledge graph in culture field |
| CN114186075B (en) * | 2021-12-14 | 2024-04-26 | 武汉数文科技有限公司 | Semantic search method for knowledge graph in cultural domain |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105095195A (en) * | 2015-07-03 | 2015-11-25 | 北京京东尚科信息技术有限公司 | Method and system for human-machine questioning and answering based on knowledge graph |
| CN105550190A (en) * | 2015-06-26 | 2016-05-04 | 许昌学院 | Knowledge graph-oriented cross-media retrieval system |
| CN106874261A (en) * | 2017-03-17 | 2017-06-20 | 中国科学院软件研究所 | A kind of domain knowledge collection of illustrative plates and querying method based on semantic triangle |
-
2018
- 2018-10-22 CN CN201811231211.0A patent/CN109522465A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105550190A (en) * | 2015-06-26 | 2016-05-04 | 许昌学院 | Knowledge graph-oriented cross-media retrieval system |
| CN105095195A (en) * | 2015-07-03 | 2015-11-25 | 北京京东尚科信息技术有限公司 | Method and system for human-machine questioning and answering based on knowledge graph |
| CN106874261A (en) * | 2017-03-17 | 2017-06-20 | 中国科学院软件研究所 | A kind of domain knowledge collection of illustrative plates and querying method based on semantic triangle |
Cited By (57)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109977294A (en) * | 2019-04-03 | 2019-07-05 | 三角兽（北京）科技有限公司 | Information/query processing device, query processing/text query method, storage medium |
| CN110019687A (en) * | 2019-04-11 | 2019-07-16 | 宁波深擎信息科技有限公司 | A kind of more intention assessment systems, method, equipment and the medium of knowledge based map |
| CN110019687B (en) * | 2019-04-11 | 2021-03-23 | 宁波深擎信息科技有限公司 | Multi-intention recognition system, method, equipment and medium based on knowledge graph |
| CN110134796A (en) * | 2019-04-19 | 2019-08-16 | 平安科技（深圳）有限公司 | Clinical test search method, device, computer equipment and the storage medium of knowledge based map |
| CN110134796B (en) * | 2019-04-19 | 2023-06-02 | 平安科技（深圳）有限公司 | Knowledge graph-based clinical trial retrieval method, device, computer equipment and storage medium |
| CN111859974A (en) * | 2019-04-22 | 2020-10-30 | 广东小天才科技有限公司 | Semantic disambiguation method and device combined with knowledge graph and intelligent learning equipment |
| CN110147437A (en) * | 2019-05-23 | 2019-08-20 | 北京金山数字娱乐科技有限公司 | A kind of searching method and device of knowledge based map |
| CN110245240A (en) * | 2019-05-31 | 2019-09-17 | 国网电子商务有限公司 | A kind of determination method and device of problem data answer |
| CN110232920A (en) * | 2019-06-21 | 2019-09-13 | 百度在线网络技术（北京）有限公司 | Method of speech processing and device |
| CN110246496A (en) * | 2019-07-01 | 2019-09-17 | 珠海格力电器股份有限公司 | Audio recognition method, system, computer equipment and storage medium |
| CN110264994A (en) * | 2019-07-02 | 2019-09-20 | 珠海格力电器股份有限公司 | A kind of phoneme synthesizing method, electronic equipment and smart home system |
| CN110502613A (en) * | 2019-08-12 | 2019-11-26 | 腾讯科技（深圳）有限公司 | A kind of model training method, intelligent search method, device and storage medium |
| CN110502613B (en) * | 2019-08-12 | 2022-03-08 | 腾讯科技（深圳）有限公司 | Model training method, intelligent retrieval method, device and storage medium |
| CN110659366A (en) * | 2019-09-24 | 2020-01-07 | Oppo广东移动通信有限公司 | Semantic analysis method and device, electronic equipment and storage medium |
| CN110704743A (en) * | 2019-09-30 | 2020-01-17 | 北京科技大学 | Semantic search method and device based on knowledge graph |
| CN110704743B (en) * | 2019-09-30 | 2022-02-18 | 北京科技大学 | Semantic search method and device based on knowledge graph |
| CN112749246B (en) * | 2019-10-30 | 2023-11-28 | 腾讯科技（深圳）有限公司 | Evaluation method and device of search phrase, server and storage medium |
| CN112749246A (en) * | 2019-10-30 | 2021-05-04 | 腾讯科技（深圳）有限公司 | Search phrase evaluation method, device, server and storage medium |
| CN110795417A (en) * | 2019-10-30 | 2020-02-14 | 北京明略软件系统有限公司 | System and method for storing knowledge graph |
| CN111160564A (en) * | 2019-12-17 | 2020-05-15 | 电子科技大学 | Chinese knowledge graph representation learning method based on feature tensor |
| CN113139387A (en) * | 2020-01-17 | 2021-07-20 | 华为技术有限公司 | Semantic error correction method, electronic device and storage medium |
| CN111274332A (en) * | 2020-01-19 | 2020-06-12 | 中国科学院计算技术研究所 | Intelligent patent retrieval method and system based on knowledge graph |
| CN111309944A (en) * | 2020-01-20 | 2020-06-19 | 同方知网(北京)技术有限公司 | Digital human search algorithm based on graph database |
| CN111309944B (en) * | 2020-01-20 | 2023-07-14 | 同方知网数字出版技术股份有限公司 | Digital humane searching method based on graph database |
| CN111353017A (en) * | 2020-02-20 | 2020-06-30 | 上海智臻智能网络科技股份有限公司 | Intelligent interaction method and device |
| CN112231445A (en) * | 2020-03-27 | 2021-01-15 | 北京来也网络科技有限公司 | Searching method, device, equipment and storage medium combining RPA and AI |
| CN111475722A (en) * | 2020-03-31 | 2020-07-31 | 百度在线网络技术（北京）有限公司 | Method and apparatus for transmitting information |
| CN111475623A (en) * | 2020-04-09 | 2020-07-31 | 北京北大软件工程股份有限公司 | Case information semantic retrieval method and device based on knowledge graph |
| CN111475623B (en) * | 2020-04-09 | 2023-08-22 | 北京北大软件工程股份有限公司 | Case Information Semantic Retrieval Method and Device Based on Knowledge Graph |
| CN111552821A (en) * | 2020-05-14 | 2020-08-18 | 北京华宇元典信息服务有限公司 | Legal intention searching method, legal intention searching device and electronic equipment |
| CN111708943B (en) * | 2020-06-12 | 2024-03-01 | 北京搜狗科技发展有限公司 | Search result display method and device for displaying search result |
| CN111708943A (en) * | 2020-06-12 | 2020-09-25 | 北京搜狗科技发展有限公司 | Search result display method and device and search result display device |
| CN111782942A (en) * | 2020-06-24 | 2020-10-16 | 支付宝(杭州)信息技术有限公司 | Searching method and device based on brand protection |
| CN111782942B (en) * | 2020-06-24 | 2024-04-19 | 支付宝(杭州)信息技术有限公司 | Searching method and device based on brand protection |
| CN111737499A (en) * | 2020-07-27 | 2020-10-02 | 平安国际智慧城市科技股份有限公司 | Data searching method based on natural language processing and related equipment |
| CN111930891B (en) * | 2020-07-31 | 2024-02-02 | 中国平安人寿保险股份有限公司 | Knowledge graph-based search text expansion method and related device |
| CN111930891A (en) * | 2020-07-31 | 2020-11-13 | 中国平安人寿保险股份有限公司 | Retrieval text expansion method based on knowledge graph and related device |
| CN111930913A (en) * | 2020-08-14 | 2020-11-13 | 上海茂声智能科技有限公司 | Knowledge graph-based question and answer method, system, device, equipment and medium |
| CN112131461A (en) * | 2020-09-09 | 2020-12-25 | 重庆易宠科技有限公司 | Commodity searching method, system, terminal and computer readable storage medium |
| CN112069808A (en) * | 2020-09-28 | 2020-12-11 | 深圳壹账通智能科技有限公司 | Financing wind control method and device, computer equipment and storage medium |
| WO2022088409A1 (en) * | 2020-10-28 | 2022-05-05 | 中国商用飞机有限责任公司北京民用飞机技术研究中心 | Interactive retrieval method and apparatus, and computer device and storage medium |
| CN112380352A (en) * | 2020-10-28 | 2021-02-19 | 中国商用飞机有限责任公司北京民用飞机技术研究中心 | Interactive retrieval method and device, computer equipment and storage medium |
| CN112507123A (en) * | 2020-12-04 | 2021-03-16 | 北京搜狗科技发展有限公司 | Data processing method and device |
| WO2022116527A1 (en) * | 2020-12-04 | 2022-06-09 | 北京搜狗科技发展有限公司 | Data processing method and device |
| CN112632225B (en) * | 2020-12-29 | 2022-08-30 | 天津汇智星源信息技术有限公司 | Semantic searching method and device based on case and event knowledge graph and electronic equipment |
| CN112632225A (en) * | 2020-12-29 | 2021-04-09 | 天津汇智星源信息技术有限公司 | Semantic searching method and device based on case and event knowledge graph and electronic equipment |
| CN112685452B (en) * | 2020-12-31 | 2021-08-10 | 特赞（上海）信息科技有限公司 | Enterprise case retrieval method, device, equipment and storage medium |
| CN112685452A (en) * | 2020-12-31 | 2021-04-20 | 特赞（上海）信息科技有限公司 | Enterprise case retrieval method, device, equipment and storage medium |
| CN112784058A (en) * | 2021-01-11 | 2021-05-11 | 北京欧拉认知智能科技有限公司 | Entity correlation obtaining method based on dynamic map |
| CN112765447A (en) * | 2021-01-27 | 2021-05-07 | 维沃移动通信有限公司 | Data searching method and device and electronic equipment |
| CN113221578B (en) * | 2021-04-30 | 2022-11-25 | 平安科技（深圳）有限公司 | Disease entity retrieval method, device, equipment and medium |
| CN113221578A (en) * | 2021-04-30 | 2021-08-06 | 平安科技（深圳）有限公司 | Disease entity retrieval method, device, equipment and medium |
| CN113434696A (en) * | 2021-06-28 | 2021-09-24 | 平安科技（深圳）有限公司 | Knowledge graph-based search result updating method and device and computer equipment |
| CN114186075A (en) * | 2021-12-14 | 2022-03-15 | 武汉数文科技有限公司 | Semantic search method for knowledge graph in culture field |
| CN114186075B (en) * | 2021-12-14 | 2024-04-26 | 武汉数文科技有限公司 | Semantic search method for knowledge graph in cultural domain |
| CN114003735B (en) * | 2021-12-24 | 2022-03-18 | 北京道达天际科技有限公司 | Knowledge graph question and answer oriented entity disambiguation method based on intelligence document |
| CN114003735A (en) * | 2021-12-24 | 2022-02-01 | 北京道达天际科技有限公司 | Knowledge graph question and answer oriented entity disambiguation method based on intelligence document |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109522465A (en) | The semantic searching method and device of knowledge based map | |
| Fensel et al. | Knowledge graphs | |
| CN110609902B (en) | Text processing method and device based on fusion knowledge graph | |
| US8965872B2 (en) | Identifying query formulation suggestions for low-match queries | |
| US9183511B2 (en) | System and method for universal translating from natural language questions to structured queries | |
| US9652799B2 (en) | Product recommendation with product review analysis | |
| US10303798B2 (en) | Question answering from structured and unstructured data sources | |
| US9846901B2 (en) | Product recommendation with ontology-linked product review | |
| US10025819B2 (en) | Generating a query statement based on unstructured input | |
| US10783159B2 (en) | Question answering with entailment analysis | |
| KR101793222B1 (en) | Updating a search index used to facilitate application searches | |
| US20180232443A1 (en) | Intelligent matching system with ontology-aided relation extraction | |
| EP3788503A1 (en) | Natural language interfaces for databases using autonomous agents and thesauri | |
| US8880512B2 (en) | Method, apparatus and system, for rewriting search queries | |
| US20170371923A1 (en) | Template-driven structured query generation | |
| CN109240901B (en) | Performance analysis method, performance analysis device, storage medium, and electronic apparatus | |
| US20120254143A1 (en) | Natural language querying with cascaded conditional random fields | |
| US20120136649A1 (en) | Natural Language Interface | |
| CN104915413A (en) | Health monitoring method and health monitoring system | |
| JP2013543172A (en) | Method, system, and computer program for automatically generating answers to questions | |
| JP2021507350A (en) | Reinforcement evidence retrieval of complex answers | |
| KR100980579B1 (en) | Method and system for searching by using natural language query in ontology | |
| JP2009151760A (en) | Method and system for calculating competitiveness metric between objects | |
| CN105808590A (en) | Search engine realization method as well as search method and apparatus | |
| KR20100075454A (en) | Identification of semantic relationships within reported speech |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190326 |