CN110457491A - A kind of knowledge mapping reconstructing method and device based on free state node - Google Patents
A kind of knowledge mapping reconstructing method and device based on free state node Download PDFInfo
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Abstract
The present invention proposes a kind of knowledge mapping reconstructing method based on free state node, this method comprises: building ontology；Semantic analysis and entity relation extraction are carried out to knowledge document, and filtered, is obtained without the relationship between duplicate RDF triplet sets and entity and affiliated ontology；By the subject of all triples, predicate storage at an entity file E, all relationships are stored at two-dimentional relation array R, and using each entity in E and are shown as node；A pointer is distributed for each node, each pointer is directed toward another two-dimensional array r；Incidence relation is established between the ontology belonging to each node and the node, and according to different demands, selection target node or sub- knowledge mapping, and connect their relationship, reconstruct knowledge mapping.This method can need to reconstruct different knowledge mappings according under different crowd, different situations, additionally it is possible to save a large amount of memory space, accelerate inquiry, visualization speed, the effect under big data quantity is more obvious.
Description
Technical field
The present invention relates to artificial intelligence and field of computer technology, and in particular to a kind of knowledge based on free state node
Map reconstruction method and device.
Background technique
Web database technology exponentially form increases at present, contains more and more information, these information are related to each neck
Domain, the complicated multiplicity of form, constitutes people and understands various knowledge, the main path of event, also becomes the existence such as incorporated business
The key component of development.But the large-scale network information is faced, people are searched on major search website by keyword
Suo Hou, obtained knowledge document have usually contained a large amount of duplicate contents, advertisement, some meaningless descriptions etc..These are all to people
Rapidly understand knowledge document main contents, hold the main train of thought of knowledge, make decisions generate negative influence in time.And
Being constructed by for knowledge mapping is collected all kinds of knowledge, handles, and carries out storage and management again after obtaining the knowledge of structuring,
It by knowledge high level overview and visualizes, the relationship train of thought being clear that between each main body, for combing
Document knowledge tool has very great help.
Existing knowledge mapping building process is broadly divided into two steps: the building (ontological construction) of data pattern layer and entity
The study of layer.Due to lacking open link data and open knowledge base, Chinese online encyclopaedia class data are not so good as English encyclopaedia class
Data rich, without complete dictionary for assisting building knowledge mapping in Chinese, Chinese language structure is complicated changeable equal natures
Characteristic causes the processing difficulty of Chinese text to increase, and the building of Chinese knowledge mapping has difficulties.In order to solve these problems,
People frequently with top down the resource that can be multiplexed is found with the bottom-up method combined in all its bearings, to reduce
The workload of data processing, application accelerates building efficiency, reduces construction cost.
But since web database technology is huge, what is still obtained after processing is large-scale data volume, if utilizing tradition
Knowledge mapping construction method these data are all stored and are visualized after obtained knowledge mapping, very big memory can be occupied,
Computer performance is influenced, inquiry velocity is reduced, people are merely desired to quickly to obtain certain interested knowledge chain still not enough intuitively.
In addition, different crowd can be different to the focus of same knowledge mapping, same crowd is under different situations to the need of knowledge mapping
Asking also can be different, so being dynamic change to the demand of knowledge mapping under different angle in different knowledge hierarchies.In order to solve
These problems need a kind of knowledge mapping reconstructing method, can dynamically rebuild knowledge mapping, and effective solution is above-mentioned to ask
Topic.
Therefore, it is directed to the different demands of knowledge mapping at present, shortage copes with different demands, saves memory space, looks into
The method for asking speed fast, intuitive displaying destination node and relationship.
Summary of the invention
In order to solve the different demands proposed at present for knowledge mapping, shortage copes with different demands, saves storage
The method that space, inquiry velocity fastly, intuitively show destination node and relationship, the present invention provides one kind to be based on free state node
Knowledge mapping reconstructing method, comprising:
Ontology is constructed, the ontology includes the relationship between each ontology；
Semantic analysis and entity relation extraction are carried out to knowledge document, and filtered, is obtained without duplicate RDF triplet sets
And the relationship between entity and affiliated ontology, the knowledge document include semi-structured and non-structured document, the RDF tri-
Each triple in tuple-set includes subject, relationship and predicate, and the relationship between the entity and affiliated ontology is described
In the ontology of building；
By the subject of all triples, predicate storage at an entity file E, all relationships are stored
To two-dimentional relation array R, each entity, relationship have a uniquely identifiable coding, and by each of the E reality
Body is shown as node, the nodes encoding of subject described in the behavior of the R, and the node for being classified as the object of the R is compiled
Code, the array element of the R are the title of the relationship, and the relationship can be identified by unique ranks assembly coding,
The entity includes the subject and predicate, and the node is visualized to obtain by the entity；
A pointer is distributed for each node, each pointer is directed toward another two-dimensional array r, and the r is institute
The subnumber group for stating R, all relationships comprising connecting the node Yu other nodes；
Establish incidence relation between the ontology belonging to each node and the described node, and according to different demands,
Selection target node or sub- knowledge mapping, and their relationship is connected, reconstruct knowledge mapping.
Preferably, the semantic analysis and entity relation extraction are related to participle, part-of-speech tagging, name Entity recognition, is interdependent
The operation such as syntactic analysis.
Preferably, filter operation, comprising:
Delete duplicate triple in RDF triplet sets；
It deletes using pronoun as the triple of subject, object；
Delete subject perhaps predicate or the incomplete triple of object；
The triple of deletion error.
Preferably, described to distribute a pointer for each node, each pointer is directed toward another two-dimensional array
R, the r be the R subnumber group, wherein the row of the r by the row of the R there are the several of relationship between the node
Row element composition, the column of the r are made of several column elements between the node there are relationship in the column of the R, element
For all relation names relevant to the node.
Preferably, incidence relation, and root are established between the ontology belonging to each node and the described node
According to different demands, selection target node or sub- knowledge mapping, and their relationship is connected, reconstruct knowledge mapping, comprising:
It is established between ontology belonging to the node and the described node being added in the two-dimensional array r of the node
Incidence relation, by the node be referred to it is corresponding it is described belonging to ontology in；
The destination node is chosen, inquired by the pointer and shows that the institute between the node and other nodes is related
It is the two-dimensional array r constituted；
Several relationships in the two-dimensional array r are chosen, show several relationships and are closed with described several
It is the node of connection；
Continue other nodes for choosing several relationships to connect and relationship, constantly extends, form sub- knowledge mapping；
It is extended on the basis of sub- knowledge mapping in the same way, or will be each after the other sub- knowledge mappings of generation
Sub- knowledge mapping connection generates final until all nodes for wanting to show and relationship are formed knowledge train of thought and are visualized
Knowledge mapping.
Preferably, the method also includes:
Dynamically increase new ontology in the ontology；
Extract the semantic knowledge and entity relationship for belonging to the increased new ontology in the knowledge document；
The entity of the new extraction and relationship are respectively added in the entity file E and relationship array R, r；
The entity of the new extraction and relationship are referred in the ontology newly increased；
Visualize the knowledge mapping of the reconstruct.
A kind of knowledge mapping reconstruct device based on free state node, described device are made of disparate modules, comprising:
Construct module, for constructing body construction predetermined, the body construction include each ontology and each ontology it
Between relationship；
Abstraction module generates withdrawal device for carrying out natural language processing to knowledge document to extract semantic information and institute
State the entity relationship triple for including in knowledge document；
Filtering module, for deleting the repetition triple in the entity relationship triple, using pronoun as subject or guest
The triple of the triple of language, incomplete triple and mistake；
Memory module will for storing the subject, the object in the entity relationship triple at entity file E
The relationship in the entity relationship triple is stored into two-dimentional relation array R, will be each of with the entity file E
The relevant relationship of entity is respectively stored into the subnumber group r of the two-dimentional relation array R, and is directed toward the r with pointer；
Visualization model, for visualizing the knowledge mapping of reconstruct, the node of the knowledge mapping is the reality
Each entity in body file E, the side of the knowledge mapping are the element in the two-dimensional array r, are inquired by the pointer
The relationship and the node connecting with the relationship show the relationship figure of the knowledge mapping under different demands.
The present invention provides a kind of knowledge mapping reconstructing methods based on free state node, construct ontology first, and right
Semi-structured or non-structured knowledge document carries out semantic analysis and entity relation extraction, obtains without duplicate RDF ternary
Belonging relation between group and entity and constructed ontology.Then by triple subject and predicate store into entity file E
And with uniquely identifiable code identification, relationship is stored into two-dimentional relation array R, wherein the volume of behavior unique identification subject
Code is classified as the coding of unique identification object, and array element is for relationship and by ranks assembly coding unique identification.It is again each node
Distribute unique pointer be directed toward R subnumber group r, r row by the row of R there are several row element groups of relationship between node
At column are made of several column elements between node there are relationship in the column of R, and element is all relationship names relevant to node
Claim.Then the incidence relation established between ontology belonging to node and the node is being added in the two-dimensional array r of node, it will be described
Node is referred in the corresponding affiliated ontology.All entities in E are finally illustrated as the form of node and choose it
In some destination node be further continued for selecting several nodes or a kind of node by pointer selection target relationship and they be related
Relationship, constantly extension forms sub- knowledge mapping, and is extended on the basis of sub- knowledge mapping in the same way or structure
Each sub- knowledge mapping is connected after building new sub- knowledge mapping, finally interested knowledge mapping is obtained according to different demands and visualizes
As a result.The relationship by the node of free state and individually stored, can reconstruct not according to different knowledge requirements, different angle
Same knowledge mapping, it is clearer to show object knowledge train of thought, accelerate inquiry velocity, save memory space, is counted to be extensive
A kind of good method is provided according to the building and application of the knowledge mapping of amount.
Detailed description of the invention
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of application for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is a kind of knowledge mapping reconstruct side based on free state node according to a preferred embodiment of the present invention
The flow diagram of method；
Fig. 2 is a kind of knowledge mapping reconstruct side based on free state node according to a preferred embodiment of the present invention
The ontology configuration diagram of method；
Fig. 3 is a kind of knowledge mapping reconstruct side based on free state node according to a preferred embodiment of the present invention
The relational structure schematic diagram of method；
Fig. 4 is a kind of knowledge mapping reconstruct side based on free state node according to a preferred embodiment of the present invention
The sub- knowledge mapping of method generates and extension schematic diagram；
Fig. 5 is a kind of knowledge mapping reconstruct side based on free state node according to a preferred embodiment of the present invention
The partial knowledge map schematic diagram of method；
Fig. 6 is to reconstruct dress according to a kind of knowledge mapping based on free state node of a preferred embodiment of the present invention
The modular structure schematic diagram set.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of embodiments of the present application, instead of all the embodiments.It is based on
Embodiment in the application, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall in the protection scope of this application.
Fig. 1 is a kind of knowledge mapping reconstruct side based on free state node according to a preferred embodiment of the present invention
The flow diagram of method, as shown in Figure 1, the present invention provides a kind of knowledge mapping reconstructing method based on free state node,
Include:
S101, building ontology.
In the present embodiment, the ontology of building is between the abstract concept set and abstract concept in building knowledge mapping
A kind of expression way of relationship plays directive function to the framework of entire map.
For example, existing ontology can have food qualification category, time of origin, hair by taking structuring food prods security incident ontology as an example
Radix Rehmanniae point, She Shi enterprise relate to the violated item of thing, event scale, notification unit, victim, event result, livestock products, cream and cream
Multiple ontologies such as product.Food safety affair ontology is established by the relationship between each ontology and each ontology, such as shown in Fig. 2.
Wherein, above-mentioned ontology constitutes the main contents of food safety affair, it should be noted that Fig. 2 only illustrates food safety
The part body of event.
S102, semantic analysis and entity relation extraction are carried out to knowledge document, and filtered, obtained without duplicate RDF ternary
Group set and the relationship between entity and affiliated ontology.
In the present embodiment, semantic analysis and entity relation extraction are related to participle, part-of-speech tagging, name Entity recognition, is interdependent
The operation such as syntactic analysis.
Further, filter operation includes deleting duplicate triple in RDF triplet sets；It deletes using pronoun as master
The triple of language, object；Delete subject perhaps predicate or the incomplete triple of object；The triple of deletion error.It crosses
The quality of the entity relationship triple obtained after filter is improved, then knowledge mapping quality is also improved.
Entity is the specific things extracted from knowledge document, is closed between corresponding ontology there is corresponding
System.For example, food safety affair ontology can have with corresponding entity, malicious egg event, Feitian event, water-injected meat event etc. are specific
Event.
S103, the subject of all triples, predicate are stored into an entity file E, all relationships is stored into two dimension
Relationship array R, each entity, relationship have uniquely identifiable coding, and each entity in E is opened up as node
Show.
In the present embodiment, the nodes encoding of the behavior subject of the R, the nodes encoding for being classified as object of R, the array member of R
Element is the title of relationship, and relationship can be identified by unique ranks assembly coding, and entity includes subject and predicate, and node is by entity
Visualization obtains.
S104, a pointer is distributed for each node, each pointer is directed toward another two-dimensional array r, and r is the subset of R, packet
Containing all relationships relevant to affiliated node.
In the present embodiment, the row of the r is made of several row elements between node there are relationship in the R row, column
It is made of several column elements between node there are relationship in R column, element is all relationships relevant to the node
Title.
For example, relational structure exemplary diagram of the entity by taking " dried beef " as an example is as shown in figure 3, the r that wherein entity node is directed toward
In contain relationship related with the node, be the subset of R, all relationships for connecting all nodes contained in R.It will be in Fig. 3
Relationship connected with node, and with building and extend the sub- knowledge mapping obtained after the method for sub- knowledge mapping and generate and expand
Exhibition figure is as shown in Figure 4.
Incidence relation is established between S105, the ontology belonging to each node and the node, and according to different demands, selection
Destination node or sub- knowledge mapping, and their relationship is connected, reconstruct knowledge mapping.
In the present embodiment, ontology belonging to each entity should be in constructed ontology, otherwise can not be by entity and building
Ontology it is corresponding, associate, can not obtain existing with the consistent incidence relation of ontological relationship between entity.In the node
The two-dimensional array r in add the incidence relation established between ontology belonging to the node and the described node, will be described
Node is referred in the corresponding affiliated ontology；The destination node is chosen, inquired by the pointer and shows the section
The two-dimensional array r that all relationships between point and other nodes are constituted；Several relationships in the two-dimensional array r are chosen, are opened up
The node for showing several relationships and being connect with several relationships；Continue to choose its of several relationships connection
His node and relationship constantly extend, form sub- knowledge mapping；Expanded on the basis of sub- knowledge mapping in the same way
Exhibition, or generate after other sub- knowledge mappings will each sub- knowledge mapping connection, until by all nodes for wanting to show and pass
System forms knowledge train of thought and visualizes, and generates final knowledge mapping.Pass through the knowledge for choosing different node and relationship to generate
The partial visual result of map is as shown in Figure 5.
Based on the above embodiment, the method also includes: dynamically increase new ontology in the ontology；Extract the knowledge
Belong to the semantic knowledge and entity relationship of the increased new ontology in document；The entity of the new extraction and relationship are added respectively
It is added in the entity file E and relationship array R, r；The entity of the new extraction and relationship are referred to described newly increase
Ontology in；Visualize the knowledge mapping of the reconstruct.
Fig. 6 is that a kind of knowledge mapping based on free state node provided by the invention reconstructs the module map that device includes,
As shown in fig. 6, the module for the knowledge mapping reconstruct device that the embodiment of the invention also provides a kind of based on free state node is said
Bright, which includes constructing module 201, abstraction module 202, filtering module 203, memory module 204 and visualization model 205,
Wherein:
Module 201 is constructed, for constructing body construction predetermined, the body construction includes each ontology and each ontology
Between relationship；
Abstraction module 202, for knowledge document carry out natural language processing, generate withdrawal device with extract semantic information and
The entity relationship triple for including in the knowledge document；
Filtering module 203, for deleting the repetition triple in the entity relationship triple, using pronoun as subject or
The triple of the triple of object, incomplete triple and mistake；
Memory module 204, for storing the subject, the object in the entity relationship triple at entity file E,
By in the entity relationship triple the relationship store into two-dimentional relation array R, by with it is every in the entity file E
The relevant relationship of a entity is respectively stored into the subnumber group r of the two-dimentional relation array R, and is directed toward the r with pointer；
Visualization model 205, for visualizing the knowledge mapping of reconstruct, the node of the knowledge mapping is described
Each entity in entity file E, the side of the knowledge mapping are the element in the two-dimensional array r, are looked by the pointer
The node asking the relationship and connecting with the relationship shows the relationship figure of the knowledge mapping under different demands.
The present invention provides a kind of knowledge mapping reconstructing methods based on free state node, construct ontology first, and right
Semi-structured or non-structured knowledge document carries out semantic analysis and entity relation extraction, obtains without duplicate RDF ternary
Belonging relation between group and entity and constructed ontology.Then by triple subject and predicate store into entity file E
And with uniquely identifiable code identification, relationship is stored into two-dimentional relation array R, wherein the volume of behavior unique identification subject
Code is classified as the coding of unique identification object, and array element is for relationship and by ranks assembly coding unique identification.It is again each node
Distribute unique pointer be directed toward R subnumber group r, r row by the row of R there are several row element groups of relationship between node
At column are made of several column elements between node there are relationship in the column of R, and element is all relationship names relevant to node
Claim.Then the incidence relation established between ontology belonging to node and the node is being added in the two-dimensional array r of node, it will be described
Node is referred in the corresponding affiliated ontology.All entities in E are finally illustrated as the form of node and choose it
In some destination node be further continued for selecting several nodes or a kind of node by pointer selection target relationship and they be related
Relationship, constantly extension forms sub- knowledge mapping, and is extended on the basis of sub- knowledge mapping in the same way or structure
Each sub- knowledge mapping is connected after building new sub- knowledge mapping, finally interested knowledge mapping is obtained according to different demands and visualizes
As a result.The relationship by the node of free state and individually stored, can reconstruct not according to different knowledge requirements, different angle
Same knowledge mapping, it is clearer to show object knowledge train of thought, accelerate inquiry velocity, save memory space, is counted to be extensive
A kind of good method is provided according to the building and application of the knowledge mapping of amount.
Meanwhile using the knowledge mapping of the knowledge mapping reconstructing method building based on free state node, without that will own
Relationship all visualize, and do not need to repeat to store repeatedly, so advising greatly by identical relationship existing between different nodes
In the case where mould data volume, saving spatial extent can be more obvious, and inquiry velocity also greatly improves fastly.Further, since only will choosing
In node and relationship visualized, other nodes exist with free state, can reconstruct at any time other knowledge mappings with
Meet different needs, so can be clearly demonstrated out for various target entity relationship knowledge train of thoughts, for subsequent
Analysis and research are of great significance and a trend of knowledge mapping development.
Finally, for each method embodiment above-mentioned, for simple description, therefore, it is stated as a series of action groups
It closes, but those skilled in the art should understand that, the application is not limited by the described action sequence, because according to this Shen
Please, some steps may be performed in other sequences or simultaneously.Secondly, those skilled in the art should also know that, specification
Described in embodiment belong to preferred embodiment, necessary to related actions and modules not necessarily the application.
It should be noted that all the embodiments in this specification are described in a progressive manner, each embodiment weight
Point explanation is the difference from other embodiments, and the same or similar parts between the embodiments can be referred to each other.
For device class embodiment, since it is basically similar to the method embodiment, so being described relatively simple, related place ginseng
See the part explanation of embodiment of the method.
Step in each embodiment method of the application can be sequentially adjusted, merged and deleted according to actual needs.
Each embodiment kind device of the application and module in terminal and submodule can be merged according to actual needs, be drawn
Divide and deletes.
In several embodiments provided herein, it should be understood that disclosed terminal, device and method, Ke Yitong
Other modes are crossed to realize.For example, terminal embodiment described above is only schematical, for example, module or submodule
Division, only a kind of logical function partition, there may be another division manner in actual implementation, for example, multiple submodule or
Module may be combined or can be integrated into another module, or some features can be ignored or not executed.Another point is shown
The mutual coupling, direct-coupling or communication connection shown or discussed can be through some interfaces, between device or module
Coupling or communication connection are connect, can be electrical property, mechanical or other forms.
Module or submodule may or may not be physically separated as illustrated by the separation member, as mould
The component of block or submodule may or may not be physical module or submodule, it can and it is in one place, or
It may be distributed on multiple network modules or submodule.Some or all of mould therein can be selected according to the actual needs
Block or submodule achieve the purpose of the solution of this embodiment.
In addition, each functional module or submodule in each embodiment of the application can integrate in a processing module
In, it is also possible to modules or submodule physically exists alone, it can also be integrated with two or more modules or submodule
In a module.Above-mentioned integrated module or submodule both can take the form of hardware realization, can also use software function
Energy module or the form of submodule are realized.
Professional further appreciates that, unit described in conjunction with the examples disclosed in the embodiments of the present disclosure
And algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware and
The interchangeability of software generally describes each exemplary composition and step according to function in the above description.These
Function is implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Profession
Technical staff can use different methods to achieve the described function each specific application, but this realization is not answered
Think beyond scope of the present application.
The step of method described in conjunction with the examples disclosed in this document or algorithm, can directly be held with hardware, processor
The combination of capable software unit or the two is implemented.Software unit can be placed in random access memory (RAM), memory, read-only deposit
Reservoir (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technology
In any other form of storage medium well known in field.
Finally, it is to be noted that, herein, relational terms such as first and second and the like be used merely to by
One entity or operation are distinguished with another entity or operation, without necessarily requiring or implying these entities or operation
Between there are any actual relationship or orders.Moreover, the terms "include", "comprise" or its any other variant meaning
Covering non-exclusive inclusion, so that the process, method, article or equipment for including a series of elements not only includes that
A little elements, but also including other elements that are not explicitly listed, or further include for this process, method, article or
The intrinsic element of equipment.In the absence of more restrictions, the element limited by sentence "including a ...", is not arranged
Except there is also other identical elements in the process, method, article or apparatus that includes the element.
The foregoing description of the disclosed embodiments makes professional and technical personnel in the field can be realized or use the application.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the application.Therefore, the application
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (7)
1. a kind of knowledge mapping reconstructing method based on free state node characterized by comprising
Ontology is constructed, the ontology includes the relationship between each ontology；
Semantic analysis and entity relation extraction are carried out to knowledge document, and filtered, obtain without duplicate RDF triplet sets and
Relationship between entity and affiliated ontology, the knowledge document include semi-structured and non-structured document, the RDF triple
Each triple in set includes subject, relationship and predicate, and the relationship between the entity and affiliated ontology is in the building
Ontology in；
By the subject of all triples, predicate storage at an entity file E, by all relationship storages to two
Dimension relationship array R, each entity, relationship have uniquely identifiable coding, and each of the E entity is made
It is shown for node, the nodes encoding of subject described in the behavior of the R, the nodes encoding for being classified as the object of the R, institute
The array element for stating R is the title of the relationship, and the relationship can be identified by unique ranks assembly coding, the reality
Body includes the subject and predicate, and the node is visualized to obtain by the entity；
A pointer is distributed for each node, each pointer is directed toward another two-dimensional array r, and the r is the R's
Subnumber group, all relationships comprising connecting the node Yu other nodes；
Incidence relation is established between the ontology belonging to each node and the described node, and according to different demands, selection
Destination node or sub- knowledge mapping, and their relationship is connected, reconstruct knowledge mapping.
2. a kind of knowledge mapping reconstructing method based on free state node according to claim 1, which is characterized in that institute
It states semantic analysis and entity relation extraction is related to the operations such as participle, part-of-speech tagging, name Entity recognition, interdependent syntactic analysis.
3. a kind of knowledge mapping reconstructing method based on free state node according to claim 1, which is characterized in that mistake
Filter operates
Delete duplicate triple in RDF triplet sets；
It deletes using pronoun as the triple of subject, object；
Delete subject perhaps predicate or the incomplete triple of object；
The triple of deletion error.
4. a kind of knowledge mapping reconstructing method based on free state node according to claim 1, which is characterized in that institute
It states and distributes a pointer for each node, each pointer is directed toward another two-dimensional array r, and the r is the son of the R
Array, wherein the row of the r is made of several row elements between the node there are relationship in the row of the R, the r's
Column are made of several column elements between the node there are relationship in the column of the R, and element is relevant to the node
All relation names.
5. a kind of knowledge mapping reconstructing method based on free state node according to claim 1, which is characterized in that institute
It states and establishes incidence relation between the ontology belonging to each node and the described node, and according to different demands, select mesh
Node or sub- knowledge mapping are marked, and connects their relationship, reconstructs knowledge mapping, comprising:
The pass established between ontology belonging to the node and the described node is being added in the two-dimensional array r of the node
The node is referred in the corresponding affiliated ontology by connection relationship；
The destination node is chosen, inquired by the pointer and shows all relationship structures between the node and other nodes
At the two-dimensional array r；
Several relationships in the two-dimensional array r are chosen, show several relationships and are connected with several relationships
The node connect；
Continue other nodes for choosing several relationships to connect and relationship, constantly extends, form sub- knowledge mapping；
It is extended on the basis of sub- knowledge mapping, or knows each son in the same way after generating other sub- knowledge mappings
Know map connection, until all nodes for wanting to show and relationship are formed knowledge train of thought and visualized, generates and final know
Know map.
6. a kind of knowledge mapping reconstructing method based on free state node according to claim 1, which is characterized in that institute
State method further include:
Dynamically increase new ontology in the ontology；
Extract the semantic knowledge and entity relationship for belonging to the increased new ontology in the knowledge document；
The entity of the new extraction and relationship are respectively added in the entity file E and relationship array R, r；
The entity of the new extraction and relationship are referred in the ontology newly increased；
Visualize the knowledge mapping of the reconstruct.
7. a kind of knowledge mapping based on free state node reconstructs device, which is characterized in that described device is by disparate modules structure
At, comprising:
Module is constructed, for constructing body construction predetermined, the body construction includes between each ontology and each ontology
Relationship；
Abstraction module generates withdrawal device for carrying out natural language processing to knowledge document to extract semantic information and described know
Know the entity relationship triple for including in document；
Filtering module, for deleting the repetition triple in the entity relationship triple, using pronoun as subject or object
The triple of triple, incomplete triple and mistake；
Memory module will be described for storing the subject, the object in the entity relationship triple at entity file E
The relationship in entity relationship triple is stored into two-dimentional relation array R, by with each entity in the entity file E
The relevant relationship is respectively stored into the subnumber group r of the two-dimentional relation array R, and is directed toward the r with pointer；
Visualization model, for visualizing the knowledge mapping of reconstruct, the node of the knowledge mapping is the entity text
Each entity in part E, the side of the knowledge mapping are the element in the two-dimensional array r, by described in pointer inquiry
Relationship and the node connecting with the relationship show the relationship figure of the knowledge mapping under different demands.
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