CN104504082A - Path showing method and system for target knowledge node sets of multiple knowledge networks - Google Patents
Path showing method and system for target knowledge node sets of multiple knowledge networks Download PDFInfo
- Publication number
- CN104504082A CN104504082A CN201410817499.5A CN201410817499A CN104504082A CN 104504082 A CN104504082 A CN 104504082A CN 201410817499 A CN201410817499 A CN 201410817499A CN 104504082 A CN104504082 A CN 104504082A
- Authority
- CN
- China
- Prior art keywords
- knowledge
- node
- digraph
- transitive closure
- network
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 230000001747 exhibiting effect Effects 0.000 claims description 13
- 238000012512 characterization method Methods 0.000 claims description 9
- 230000000007 visual effect Effects 0.000 abstract description 2
- 201000010099 disease Diseases 0.000 description 15
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 15
- 238000003745 diagnosis Methods 0.000 description 8
- 206010036205 Portal vein phlebitis Diseases 0.000 description 6
- 230000007170 pathology Effects 0.000 description 6
- 230000004069 differentiation Effects 0.000 description 5
- 239000012634 fragment Substances 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 239000003086 colorant Substances 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 208000004930 Fatty Liver Diseases 0.000 description 1
- 206010019708 Hepatic steatosis Diseases 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 208000010706 fatty liver disease Diseases 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 210000004185 liver Anatomy 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000001568 sexual effect Effects 0.000 description 1
- 231100000240 steatosis hepatitis Toxicity 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Computing Systems (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Devices For Executing Special Programs (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a path showing method for target knowledge node sets of multiple knowledge networks. The path showing method comprises the following steps: S101, constructing a directed graph of a certain binary relation for each of the multiple knowledge networks according to each target knowledge node in the target knowledge node sets based on the binary relation; S102, calculating transitive closure graphs of the target knowledge nodes in each knowledge network for each target knowledge node in the target knowledge node sets according to the binary relation and the directed graph; S103, combining all the transitive closure graphs obtained in the S102, removing repeated nodes and edges, and splicing to obtain a spliced directed graph; S104, representing the spliced directed graph. The invention also provides a corresponding path showing system. According to an intuitive, precise and complete knowledge network structure showing method, a user can quickly and clearly understand trans-systematic knowledge architecture from a user visual angle.
Description
Technical field
The present invention relates to graphic exhibition method, more specifically, relate to a kind of path exhibiting method based on the synonym knowledge node set of multiple knowledge hierarchy (network), the knowledge network relation that present invention also offers a kind of correspondence represents system.
Background technology
Current knowledge network represent the main accumulation by artificial experience, be mainly derived from teacher, the collection of illustrative plates that expert and training organization draw, also and there are no based on across knowledge network collection of illustrative plates automatic calculating and mutually than with merge scheme.In addition, in current techniques realization rate, lack and describe the differentiated of synonym knowledge node and represent means, often synonym knowledge node is not distinguished.And in reality, why knowledge node is considered to " synonym " or " ambiguity ", mainly because the content environment at this knowledge node place is similar or difference, as different subjects, in different works.
Even if be considered to the knowledge node of " synonym ", too owing to being present in different works or document, its source, classification, Knowledge route and purposes also there are differences, and this species diversity often can not provide user well, intuitively to express by word and language form.The form of the text description that compares, though language describes more clearly, user reads also and be not easy logically to understand knowledge hierarchy framework visually.
In current interactive process, how extract across architectonic relation according to the set of synonym knowledge node and reconfigure, the expression representing the environment difference of synonym knowledge node in each knowledge hierarchy remains a problem in the research further of human-computer interaction interface field needs.
Summary of the invention
The object of the invention is to realize: based on the extraction across system realized in multiple isomorphism knowledge hierarchy the Knowledge route of synonym knowledge node and whole and algorithm, and the system relation after being integrated is expressed by graphical tools.To provide user more directly perceived, and the knowledge hierarchy graph of a relation that text description form is not easily expressed.
A kind of path exhibiting method of object knowledge node set of multiple knowledge network, comprise: step S101, for each the object knowledge node in object knowledge node set, based on a certain binary relation, build the digraph of this binary relation for each of described multiple knowledge network; Step S102, for each the object knowledge node in described object knowledge node set, based on described binary relation and described digraph, calculates the transitive closure figure of object knowledge node in each knowledge network; Step S103, merges all transitive closure figure obtained in step S102, removes the node and limit that repeat, and splices, and obtains splicing digraph; Step S104, characterizes described splicing digraph.
The path that the present invention also proposes a kind of object knowledge node for multiple knowledge network represents system, comprise: digraph generation module, it is for each the object knowledge node in object knowledge node set, based on a certain binary relation, build the digraph of this binary relation for each of described multiple knowledge network; Transitive closure figure generation module, it is for each the object knowledge node in described object knowledge node set, based on described binary relation and described digraph, calculates the transitive closure figure of object knowledge node in each knowledge network; Transitive closure figure concatenation module, it merges all transitive closure figure that transitive closure figure generation module obtains, and removes the node and limit that repeat, and splices, and obtains splicing digraph; Characterization module, it characterizes described splicing digraph.
Relational calculus in knowledge based network, the technical scheme that the present invention proposes is mainly used in extracting close Knowledge route from multiple independently knowledge hierarchy, and realizes merging its relationship across system of displaying.Independently knowledge hierarchy refers to all disjunct two knowledge node set in any dimension.
Compared with prior art, the present invention can the local knowledge system train of thought of displaying knowledge hierarchy synonym knowledge node clearly, for user provides the information representation mode of a kind of close friend, and the integration relation that user can be provided to represent different dimensions.The method is also a kind of provide more effective information modelling approach for large data analysis.
Accompanying drawing explanation
Fig. 1 is the basic flow sheet of the method for first embodiment of the invention;
Fig. 2 is the digraph of the concept relation of inclusion of knowledge network A " medical diagnosis on disease ";
Fig. 3 is object knowledge node transitive closure figure in fig. 2;
Fig. 4 is the digraph of the concept relation of inclusion of knowledge network B " disease pathology ";
Fig. 5 is object knowledge node transitive closure figure in the diagram;
Fig. 6 is that the spliced knowledge network of first embodiment of the invention represents figure;
Fig. 7 is the basic flow sheet of the method for second embodiment of the invention;
Fig. 8 is that the spliced knowledge network of second embodiment of the invention represents figure;
Fig. 9 is the basic flow sheet of the method for third embodiment of the invention;
Figure 10 is that the spliced knowledge network of the method for third embodiment of the invention represents figure;
Figure 11 is the basic block diagram of the system of four embodiment of the invention.
Embodiment
The present invention relates to following proper noun, its implication is explained as follows:
Ontology model: body (Ontologies) is the clear and definite normalized illustration of generalities.Current ontology model is widely used in artificial intelligence field.Ontology model is exactly the model that the example of feature (i.e. attribute) and the concept had according to the relation between real-life concept, concept, concept takes out reality.Be made up of node, attribute and relation.Such as, in computer realm, can take out: concepts such as " computing machine, CPU, storer, computer fittings ", and " computing machine " and " CPU " is relation of inclusion.
Binary relation: set X is R=(X, Y, G (R)) with the binary relation on set Y, and wherein G (R), is called the figure of R, is the subset of cartesian product X × Y.(if x, y) ∈ G (R), then claim x to be that R-pass lies in y, and be denoted as xRy or R (x, y), i.e. binary relation.Otherwise claim a and b irrelevant R.In superincumbent ontology model example, the relation between " computing machine " and " CPU ", " CPU is a part for computing machine ", " calculating comprises CPU " are exactly binary relation.
Digraph: directly perceived, if the every bar limit in figure is all directive, is then called digraph.Digraph is two tuple <V, E>, and wherein, V is nonempty set, is called vertex set.E is the subset of V × V, is called arc collection.The ordered pair that limit in digraph is made up of two summits, ordered pair represents with angle brackets usually, and as <vi, vj> represent a directed edge, wherein vi is the initial point on limit, and vj is the terminal on limit.<vi, vj> and <vj, vi> represents two different directed edges.
Directed acyclic graph: in graph theory, if a digraph cannot get back to this point from certain summit through some limits, then this figure is a directed acyclic graph (DAG figure).If there is ring in knowledge network, such as conceptual relation has become a ring, and so, annular relation is without starting point and endless, and this knowledge concepts then represented has conflict or ambiguity.
Knowledge network: the multiple knowledge node structures divided from domain knowledge based on ontology model form, and wherein have binary relation between knowledge node.Knowledge node forms by extracting or summarized by expert from various independently content system.The form of expression of knowledge node can be understood as a word, phrase or a word.Content system can be understood as: the properties collection that books, monograph etc. are relatively independent and complete.Binary relation can be such as relation of inclusion, differentiation relation etc. various from reality abstract relationship type out.
Knowledge network dimension: refer in same knowledge network, the kind of the binary relation between knowledge node.There is a kind of binary relation type between knowledge node, then to represent this knowledge network dimension be 1, and there are two kinds of binary relations, then the dimension of this knowledge network is 2.In each dimension, at least to there is a binary relation example.
Synonym knowledge node: namely what meet following two kinds of conditions thinks synonym knowledge node, 1) of the same name, namely identical in the title of two or more knowledge node of same ambit; 2) there is identical semantic code attribute.Semantic code refers to and represents the discrepant word of name or phrase with code.If code is identical, then think synonym, as: the synonym knowledge node that ABS and " anti-lock braking system " refer to.Such as abbreviation " colour TV " and " colour television set ", both is in fact synonym, and the mode of giving identical semantic code attribute also can be adopted to show that these two knowledge node are synonyms.Or even different language versions, as: change and change.
Fundamental purpose of the present invention builds a kind of knowledge network structure ways of presentation that can be directly perceived, accurate and complete, to substitute by the fuzzy of various language and literal expression and may with the knowledge node relationship description mode of randomness, help user understand clearly fast active user visual angle across architectural knowledge framework.
First embodiment (single target knowledge node)
The path exhibiting method of the synonym knowledge node based on multiple knowledge network of the present invention as shown in Figure 1.With reference to Fig. 1, in step S101, based on a certain binary relation, build the digraph of this binary relation for each of described multiple knowledge network.Suppose there is n knowledge network M1 ~ Mn, for a certain binary relation, in each knowledge network, build the digraph of this binary relation, n digraph can be built at most altogether.
In fact, the mathematical model that knowledge network represents logic is built: the binary relation between the multiple knowledge node divided from domain knowledge based on ontology model and knowledge node builds knowledge network in step S101, the binary relation of knowledge based network builds Mi digraph, wherein Mi represents the relationship type number of binary relation, i.e. dimension, i represents i-th knowledge network.Knowledge node can think the certain semantic scope of domain knowledge system, and the relation between knowledge node can think the predicate between semanteme, namely between knowledge node for the primary relation of certain object.The multirelation network that knowledge network is made up of the relation between knowledge node and knowledge node.
Preferably, if there is ring in any one relation, then think and occur semantic ambiguity, need the relation rebuild between knowledge node.
With two knowledge network examples, this step is described below.Setting knowledge network A: " medical diagnosis on disease ", knowledge network B: " disease pathology ".Concept relation of inclusion is all there is in two knowledge networks.Concept relation of inclusion can be thought: the relation of subdivision of knowledge concepts, is mainly used in the classification of knowledge concepts, as automobile comprises engine, chassis, variator, vehicle frame, tire, brake etc.Differentiation relation can think the evolutionary relationship stage between knowledge, as: mathematical axiom, theorem, reasoning, just belong to differentiation relation.For another example: mathematical " Fourier transform " has several mutation: continuous fourier transform, Fourier series, discrete Fourier transformation, discrete time Fourier transform.These four is exactly clear and definite differentiation relation.Other relation can certainly be had to classify, and the type of the present invention's not restriction relation, in this example, only realizes principle with " concept comprises " relation to describe technology of the present invention.
The concept relation of inclusion of above-mentioned two knowledge networks is expressed as:
G
uS-1=[V
uS-1, E
uS-1]=[v
uS-1, v
uS-2..., v
uS-i, E
uS-1], G
uS-1represent the digraph of " medical diagnosis on disease " knowledge network concept relation of inclusion, V
uS-1for there is the A to Z of node set of concept relation of inclusion, V
uS-1={ v
uS-1, v
uS-2..., v
uS-i, E
uS-1represent concept relation of inclusion.
G
dT-1=[V
dT-1, E
dT-1]=[v
dT-1, v
dT-2..., v
dT-j, E
uS-2], G
dT-1represent " disease pathology " knowledge network concept relation of inclusion digraph, V
dT-1for there is the A to Z of node set of concept relation of inclusion, V
dT-1={ v
dT-1, v
dT-2..., v
dT-j, E
dT-1represent concept relation of inclusion.
With reference to Fig. 1, in step s 102, for object knowledge node, calculate this object knowledge node at described multiple knowledge network M based on a kind of binary relation (such as concept relation of inclusion)
1-M
ndigraph in transitive closure figure.
Further, the set of node (being called Input connection point set) with input relation and the directed edge set of object knowledge node can be obtained.
If a certain dimension of knowledge hierarchy adopts tree structure to store instead of figure, the closure node so calculating a node then can adopt upwards finds whole ancestor node algorithm, and the result obtained is a unidirectional knowledge node relation chain.
For object knowledge node, for a certain binary relation (such as concept relation of inclusion), calculate digraph (the i.e. G of this object knowledge node at 2 knowledge network A (" medical diagnosis on disease ") and knowledge network B (" disease pathology ") and B
uS-1and G
dT-1) in transitive closure figure.The calculating of transitive closure figure, can adopt the transitive closure computing method such as Warshall algorithm.Based on graph theory, the result of the output that transitive closure calculates still is digraph.Such as, the concept relation of inclusion digraph G of knowledge based network A
uS-1the object knowledge node transitive closure figure G calculated
uS-1'=[V
uS-1', E
uS-1'], figure G
uS-1' in set of node V
uS-1' be at former digraph G
uS-1middle all node set (Input connection point set) that can arrive destination node, limit collection E
uS-1' refer to V
uS-1' node set is at former figure G
uS-1in all limit.
Cite an actual example, if destination node is " pylephlebitis ".As an example, Fig. 2 shows the concept relation of inclusion digraph G of destination node at knowledge network A " medical diagnosis on disease "
uS-1, Fig. 3 shows the concept relation of inclusion digraph G of destination node at knowledge network A " medical diagnosis on disease "
uS-1the transitive closure digraph G of concept based relation of inclusion
uS-1'.As an example, Fig. 4 shows the concept bag relation digraph G of destination node at knowledge network B " disease pathology "
dT-1.Fig. 5 shows the concept bag relation digraph G of destination node at knowledge network B " disease pathology "
dT-1transitive closure digraph G
dT-1'.
Refer again to Fig. 1, in step s 103, all transitive closure figure obtained in step S102 are merged, remove the node and limit that repeat, and to splice, obtain being merged and connect digraph.The implementation algorithm of splicing is as follows:
1) the Input connection point set of all architectonic a certain binary relation transitive closure figure is done set union
Calculate, the result obtained is the set summation eliminating duplicate node, is formed without repeating semantic node collection
Close V ", carry out the node set as splicing digraph;
2) by the limit of the above-mentioned transitive closure figure calculated based on a certain binary relation of multiple knowledge network with
Step 1) gained without repeating semantic node set, build splicing digraph;
3) for described splicing digraph, identify from the limit of different knowledge network and node with district
Point, such as identified by real-world characteristics such as color, shape or thicknesses.
Fig. 6 is that transitive closure figure closes spliced result figure logic.The implication of the new knowledge network on behalf generated is: 1) " pylephlebitis " is object knowledge node; 2) there are two Knowledge routes can get at " pylephlebitis ", in figure, are presented as two starting points, show have about " pylephlebitis " synonym knowledge node in " ultrasonic examination " and " underlying diseases is theoretical "; 3) the synonym knowledge node " liver " in two Knowledge routes and " pylephlebitis " are the splice point of two figure; 4) the linear difference in two paths, represents from the source of knowledge hierarchy starting point different respectively.
Refer again to Fig. 1, in step S104, described splicing digraph is played up.Play up and various Graphics Control or software can be used to complete, such as, can adopt arborjs.org html5 control, described is use drawing instrument arborjs.org to complete with figure in this article.
When playing up, can with different color (text colors, background color, highlighted etc.) or lines etc. indicate knowledge node in different knowledge network and limit, such as, if with the knowledge node in green mark knowledge network A and limit, with the knowledge node in yellow mark knowledge network B and limit.If bridging line, then identify with orange.Or, distinguish object knowledge node and other nodes by different colors and contour shape.
Second embodiment (multiple object knowledge node)
First embodiment is only for an object knowledge node, but, object knowledge node can be multiple, thus forms an object knowledge node set V, thought of the present invention structure still can be utilized for the path exhibiting method of multiple knowledge network for object knowledge node set V.In this part, the step identical with the first embodiment will repeat no more.
As shown in Figure 7, step S201 is identical with the first embodiment step S101.
In step S202, for each object knowledge node of object knowledge node set V, based on a kind of binary relation, in the digraph of described multiple knowledge network M1 ~ Mn, calculate the transitive closure figure of object knowledge node.Further, Input connection point set and the directed edge set of object knowledge node set V can be obtained.
For each object knowledge node, for a certain binary relation (such as concept relation of inclusion), calculate digraph (the i.e. G of this object knowledge node at 2 knowledge network A (" Ultrasonic Diagnosis ") and knowledge network B (" underlying diseases is theoretical ") and B
uS-1and G
dT-1) in transitive closure figure.The calculating of transitive closure figure, can adopt the transitive closure computing method such as Warshall algorithm.Based on graph theory, the result of the output that transitive closure calculates still is digraph.Such as, the concept relation of inclusion digraph G of knowledge based network A
uS-1the object knowledge node transitive closure figure G calculated
uS-1'=[V
uS-1', E
uS-1'], figure G
uS-1' in set of node V
uS-1' be at former digraph G
uS-1middle all node set that can arrive destination node set V, limit collection E
uS-1' refer to V
uS-1' node set is at former figure G
uS-1in all limit.
In step S203, all transitive closure figure obtained in step S202 are merged, remove the node and limit that repeat, obtain splicing digraph.The implementation of splicing is identical with the step S103 of the first embodiment.In step S204, play up splicing digraph described in step, the concrete step S104 with the first embodiment.
In the present embodiment, the relation across multiple knowledge network that can construct for multiple object knowledge node moves towards figure.Fig. 7 illustrates for knowledge network A " Ultrasonic Diagnosis " and knowledge network B " underlying diseases is theoretical " two knowledge node simultaneously as the merging figure logical relation obtained during destination node.
Method of the present invention can realize across architectonic various dimensions relation retrieve and excavation, can help user's more intuitive understanding each parallel architectonic between incidence relation.Namely as Fig. 6, Fig. 8 expression is to the different understanding of the concept system of object knowledge node and comparison diagram in several knowledge network (system).
Based on said method, can carry out searching for across knowledge network in other dimensions defined, as the Knowledge Dependency sexual intercourse of imparting knowledge to students, its illustrated purposes just can represent several architectonic teaching plan contrast, and user can create according to this contrast the teaching plan being more suitable for oneself.
3rd embodiment (path exhibiting method)
In the present embodiment, based on the splicing digraph that step S103 or S203 generates, carry out a kind of new technique of expression, as shown in Figure 10, the second embodiment that step S301-S303 and Fig. 7 that described method comprises represents is identical, no longer describes in detail.In the step S304 of the present embodiment, the knowledge node represented by described node based on described splicing digraph is spliced successively according to the relation on limit.Thus, for each node in meeting point, extract various types of contents fragments of destination node set, and be combined into the encyclopaedia formula document read with complete meaning.
In step s 304, " successively " refers to for the node in splicing digraph, and the order of splicing performs according to the relation of digraph.
This embodiment is particularly useful in and realizes a kind of new encyclopaedia system, its be a kind of in multiple knowledge network with the search type encyclopaedia system that the mode of contents fragment dynamic combined realizes.The expansive approach that this encyclopaedia system realizes based on the digraph stitching algorithm described in this invention.
Further, in the encyclopaedia system realized based on described exhibiting method, each knowledge node can associate various relevant various document fragment resource semantic to this knowledge node, and these resource segment can derive from the relevant Internet resources of XML document fragment, Database field and various url.These resources can have different purposes classification marks, as: the application dimension that summary description, deagnostic test, using method etc. are different.The fusion of different architectonic content in the realistic case, is difficult to the full content needed for the user comprising a node that a knowledge hierarchy can be complete, so can provide more comprehensively information for user.
Such as, user wishes to obtain the information about " fatty liver " entry, and so system according to the result of coupling, can obtain the relevant documentation fragment of two kens (knowledge hierarchy A and knowledge hierarchy B) about pylephlebitis.And combined: as shown in figure 11.
When realizing this function, across any node on knowledge hierarchy spliced map, by said method realization to the search of content and Dynamic merge, and can inherently represent the differentiation train of thought of knowledge across knowledge hierarchy spliced map, to reader, there is very friendly directive function.
4th embodiment (path represents system)
The present invention also provides a kind of path of object knowledge node set of multiple knowledge network to represent system, as shown in figure 11.That is, in described object knowledge node set, object knowledge node can be one or more.
Described system comprises to figure generation module, and it is for each the object knowledge node in object knowledge node set, based on a certain binary relation, builds the digraph of this binary relation for each of described multiple knowledge network; .
Described system also comprises transitive closure figure generation module, and it is for each the object knowledge node in described object knowledge node set, based on described binary relation and described digraph, calculates the transitive closure figure of object knowledge node in each knowledge network;
Described system also comprises transitive closure figure concatenation module, and it merges all transitive closure figure that transitive closure figure generation module obtains, and removes the node and limit that repeat, and splices, and obtains splicing digraph.Transitive closure figure concatenation module for same node point in multiple digraph merging module, duplicate keys of being entered to fall by synonym node in two or more digraph calculates, consequently multiple disconnected digraphs are spliced into a digraph, become the relationship expression digraph of object knowledge node in multiple knowledge network.
Described system also comprises characterization module, and it splices described splicing digraph or play up.Described characterization module can be various Graphics Control or software, such as, can be arborjs.org html5 control or drawing instrument arborjs.org.
In a preferred version, described transitive closure figure generation module is configured to obtain Input connection point set in each knowledge network of each object knowledge node in object knowledge node set and Bian Ji according to described transitive closure, described Input connection point set is for arriving the set of all nodes of object knowledge node, and described limit collection is all limits that described input node is integrated into the digraph in knowledge network.
Described transitive closure figure concatenation module also comprises: duplicate removal module, and the Input connection point set of all architectonic a certain binary relation transitive closure figure is done set union and calculates by it, removes duplicate node to be formed without repeating semantic node set; Concatenation module, the limit of its transitive closure figure calculated based on a certain binary relation of all knowledge networks and the nothing of described duplicate removal module gained repeat semantic node set, build described splicing digraph.
In another preferred version, characterization module, for described splicing digraph, identifies to distinguish to from the limit of different knowledge network and node, and plays up based on described mark.Further, characterization module identifies to distinguish to from the limit of different knowledge network and node by color, background, shape or thickness.
Although combined and be considered to feasible illustrative embodiments at present and describe the present invention, but will understand, the invention is not restricted to disclosed illustrative embodiments, but on the contrary, the present invention is intended to cover and is included in various distortion in the spirit and scope of claims and equivalent arrangements.
Claims (10)
1. a path exhibiting method for the object knowledge node set of multiple knowledge network, is characterized in that, comprising:
Step S101, for each the object knowledge node in object knowledge node set, based on a certain binary relation, builds the digraph of this binary relation for each of described multiple knowledge network;
Step S102, for each the object knowledge node in described object knowledge node set, based on described binary relation and described digraph, calculates the transitive closure figure of object knowledge node in each knowledge network;
Step S103, merges all transitive closure figure obtained in step S102, removes the node and limit that repeat, and splices, and obtains splicing digraph;
Step S104, characterizes described splicing digraph.
2. the path exhibiting method of the object knowledge node of multiple knowledge network according to claim 1, is characterized in that,
In step s 102, Input connection point set in each knowledge network of each object knowledge node in object knowledge node set and Bian Ji is obtained according to described transitive closure, described Input connection point set is for arriving the set of all nodes of object knowledge node, and described limit collection is all limits that described input node is integrated into the digraph in knowledge network; And
Described step S103 also comprises:
1) the Input connection point set of all architectonic a certain binary relation transitive closure figure being done set union to calculate, removing duplicate node to be formed without repeating semantic node set;
2) limit of the transitive closure figure calculated based on a certain binary relation of all knowledge networks and step 1) gained without repeating semantic node set, build described splicing digraph.
3. the path exhibiting method of the object knowledge node set of multiple knowledge network according to claim 2, is characterized in that, in step S104, the knowledge node represented by described node based on described splicing digraph is spliced successively according to the relation on limit.
4. the path exhibiting method of the object knowledge node set of multiple knowledge network according to claim 2, it is characterized in that, in step S104, for described splicing digraph, identify to distinguish to from the limit of different knowledge network and node, and play up based on described mark.
5. the path exhibiting method of the object knowledge node set of multiple knowledge network according to claim 4, is characterized in that, identifies to distinguish to from the limit of different knowledge network and node by color, background, shape or thickness.
6. the path of the object knowledge node set of multiple knowledge network represents a system, it is characterized in that, comprising:
Digraph generation module, it is for each the object knowledge node in object knowledge node set, based on a certain binary relation, builds the digraph of this binary relation for each of described multiple knowledge network;
Transitive closure figure generation module, it is for each the object knowledge node in described object knowledge node set, based on described binary relation and described digraph, calculates the transitive closure figure of object knowledge node in each knowledge network;
Transitive closure figure concatenation module, it merges all transitive closure figure that transitive closure figure generation module obtains, and removes the node and limit that repeat, and splices, and obtains splicing digraph;
Characterization module, it characterizes described splicing digraph.
7. the path of the object knowledge node set of multiple knowledge network according to claim 6 represents system, it is characterized in that,
Described transitive closure figure generation module is configured to obtain Input connection point set in each knowledge network of each object knowledge node in object knowledge node set and Bian Ji according to described transitive closure, described Input connection point set is for arriving the set of all nodes of object knowledge node, and described limit collection is all limits that described input node is integrated into the digraph in knowledge network; And
Described transitive closure figure concatenation module also comprises:
Duplicate removal module, the Input connection point set of all architectonic a certain binary relation transitive closure figure is done set union and calculates by it, removes duplicate node to be formed without repeating semantic node set;
Concatenation module, the limit of its transitive closure figure calculated based on a certain binary relation of all knowledge networks and the nothing of described duplicate removal module gained repeat semantic node set, build described splicing digraph.
8. the path exhibiting method of the object knowledge node set of multiple knowledge network according to claim 7, is characterized in that, the knowledge node that described node represents based on described splicing digraph by described characterization module is spliced successively according to the relation on limit.
9. the path of the object knowledge node set of multiple knowledge network according to claim 7 represents system, it is characterized in that, characterization module, for described splicing digraph, identifies to distinguish to from the limit of different knowledge network and node, and plays up based on described mark.
10. the path of the object knowledge node set of described multiple knowledge networks according to claim 9 represents system, it is characterized in that, characterization module identifies to distinguish to from the limit of different knowledge network and node by color, background, shape or thickness.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410817499.5A CN104504082B (en) | 2014-12-24 | 2014-12-24 | The path of the object knowledge node set of multiple knowledge networks shows method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410817499.5A CN104504082B (en) | 2014-12-24 | 2014-12-24 | The path of the object knowledge node set of multiple knowledge networks shows method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104504082A true CN104504082A (en) | 2015-04-08 |
| CN104504082B CN104504082B (en) | 2018-05-18 |
Family
ID=52945480
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410817499.5A Expired - Fee Related CN104504082B (en) | 2014-12-24 | 2014-12-24 | The path of the object knowledge node set of multiple knowledge networks shows method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104504082B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105183824A (en) * | 2015-08-28 | 2015-12-23 | 重庆简悉大数据科技有限公司 | Data integration method and apparatus |
| CN106685716A (en) * | 2016-12-29 | 2017-05-17 | 平安科技（深圳）有限公司 | Network topology self-adapting data visualization method and device |
| CN106897446A (en) * | 2017-03-02 | 2017-06-27 | 中国农业银行股份有限公司 | A kind of data flow method for visualizing and device |
| CN107247813A (en) * | 2017-07-26 | 2017-10-13 | 北京理工大学 | A kind of network struction and evolution method based on weighting technique |
| CN108335363A (en) * | 2018-01-22 | 2018-07-27 | 上海星合网络科技有限公司 | Multidimensional knowledge system stereo exhibition method and device |
| CN109379441A (en) * | 2018-12-07 | 2019-02-22 | 华中科技大学 | Chain rule combined method and system are serviced in a kind of cloud environment |
| CN109840284A (en) * | 2018-12-21 | 2019-06-04 | 中科曙光南京研究院有限公司 | Family's affiliation knowledge mapping construction method and system |
| CN111597275A (en) * | 2019-02-21 | 2020-08-28 | 阿里巴巴集团控股有限公司 | Method and device for processing isomorphic subgraph or topological graph |
| CN112100294A (en) * | 2020-09-23 | 2020-12-18 | 杭州安恒信息安全技术有限公司 | User relationship analysis method and device for network platform and related equipment |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1658234A (en) * | 2004-02-18 | 2005-08-24 | 国际商业机器公司 | Hierchical visual structure of generating semantic network |
| CN101706790A (en) * | 2009-09-18 | 2010-05-12 | 浙江大学 | Clustering method of WEB objects in search engine |
| CN101930462A (en) * | 2010-08-20 | 2010-12-29 | 华中科技大学 | Comprehensive body similarity detection method |
| CN101630314B (en) * | 2008-07-16 | 2011-12-07 | 中国科学院自动化研究所 | Semantic query expansion method based on domain knowledge |
| CN102591988A (en) * | 2012-01-16 | 2012-07-18 | 宋胜利 | Short text classification method based on semantic graphs |
| CN103020206A (en) * | 2012-12-05 | 2013-04-03 | 北京海量融通软件技术有限公司 | Knowledge-network-based search result focusing system and focusing method |
-
2014
- 2014-12-24 CN CN201410817499.5A patent/CN104504082B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1658234A (en) * | 2004-02-18 | 2005-08-24 | 国际商业机器公司 | Hierchical visual structure of generating semantic network |
| US20050192926A1 (en) * | 2004-02-18 | 2005-09-01 | International Business Machines Corporation | Hierarchical visualization of a semantic network |
| CN101630314B (en) * | 2008-07-16 | 2011-12-07 | 中国科学院自动化研究所 | Semantic query expansion method based on domain knowledge |
| CN101706790A (en) * | 2009-09-18 | 2010-05-12 | 浙江大学 | Clustering method of WEB objects in search engine |
| CN101930462A (en) * | 2010-08-20 | 2010-12-29 | 华中科技大学 | Comprehensive body similarity detection method |
| CN102591988A (en) * | 2012-01-16 | 2012-07-18 | 宋胜利 | Short text classification method based on semantic graphs |
| CN103020206A (en) * | 2012-12-05 | 2013-04-03 | 北京海量融通软件技术有限公司 | Knowledge-network-based search result focusing system and focusing method |
Non-Patent Citations (2)
| Title |
|---|
| 朱明方 等: "《数据结构教程》", 31 January 2007, 机械工业出版社 * |
| 杨思洛 等: "《知识图谱研究现状及趋势的可视化分析》", 《情报资料工作》 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105183824B (en) * | 2015-08-28 | 2020-03-17 | 重庆简悉大数据科技有限公司 | Data integration method and device |
| CN105183824A (en) * | 2015-08-28 | 2015-12-23 | 重庆简悉大数据科技有限公司 | Data integration method and apparatus |
| CN106685716A (en) * | 2016-12-29 | 2017-05-17 | 平安科技（深圳）有限公司 | Network topology self-adapting data visualization method and device |
| WO2018120423A1 (en) * | 2016-12-29 | 2018-07-05 | 平安科技（深圳）有限公司 | Network topology adaptive data visualization method, device, apparatus and storage medium |
| EP3565181A4 (en) * | 2016-12-29 | 2020-10-21 | Ping An Technology (Shenzhen) Co., Ltd. | Network topology adaptive data visualization method, device, apparatus and storage medium |
| US10749755B2 (en) | 2016-12-29 | 2020-08-18 | Ping An Technology (Shenzhen) Co., Ltd. | Network topology self-adapting data visualization method, device, apparatus, and storage medium |
| CN106897446A (en) * | 2017-03-02 | 2017-06-27 | 中国农业银行股份有限公司 | A kind of data flow method for visualizing and device |
| CN107247813A (en) * | 2017-07-26 | 2017-10-13 | 北京理工大学 | A kind of network struction and evolution method based on weighting technique |
| CN108335363A (en) * | 2018-01-22 | 2018-07-27 | 上海星合网络科技有限公司 | Multidimensional knowledge system stereo exhibition method and device |
| CN109379441A (en) * | 2018-12-07 | 2019-02-22 | 华中科技大学 | Chain rule combined method and system are serviced in a kind of cloud environment |
| CN109840284A (en) * | 2018-12-21 | 2019-06-04 | 中科曙光南京研究院有限公司 | Family's affiliation knowledge mapping construction method and system |
| CN111597275A (en) * | 2019-02-21 | 2020-08-28 | 阿里巴巴集团控股有限公司 | Method and device for processing isomorphic subgraph or topological graph |
| CN111597275B (en) * | 2019-02-21 | 2023-06-20 | 阿里巴巴集团控股有限公司 | Isomorphic subgraph or topological graph processing method and device |
| CN112100294A (en) * | 2020-09-23 | 2020-12-18 | 杭州安恒信息安全技术有限公司 | User relationship analysis method and device for network platform and related equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104504082B (en) | 2018-05-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104504082A (en) | Path showing method and system for target knowledge node sets of multiple knowledge networks | |
| CN105659225A (en) | Query expansion and query-document matching using path-constrained random walks | |
| Saleena et al. | Using concept similarity in cross ontology for adaptive e-Learning systems | |
| De Toffoli et al. | An inquiry into the practice of proving in low-dimensional topology | |
| Everitt et al. | The homotopy theory of Khovanov homology | |
| Dorin et al. | The practice of agent-based model visualization | |
| CN106126885A (en) | A kind of product-design knowledge method of servicing and device | |
| CN107402919A (en) | Machine translation data selecting method and machine translation data selection system based on figure | |
| Li et al. | Note on uncertain linguistic Bonferroni mean operators and their application to multiple attribute decision making | |
| Schindel | Systems of innovation II: the emergence of purpose | |
| CN107506370A (en) | Multi-medium data depth method for digging, storage medium and electronic equipment | |
| CN116610874A (en) | Cross-domain recommendation method based on knowledge graph and graph neural network | |
| Haav | An application of inductive concept analysis to construction of domain-specific ontologies | |
| Méndez et al. | An antipode formula for the natural Hopf algebra of a set operad | |
| Saghafi | Visualizing ontologies–a literature survey | |
| CN104504228B (en) | Knowledge network based on aspect shows method and system | |
| Woof et al. | A framework for end-to-end learning on semantic tree-structured data | |
| Minato | Power of enumeration—recent topics on BDD/ZDD-based techniques for discrete structure manipulation | |
| CN110175197B (en) | Ontology construction method and system based on semantic Internet of things | |
| US20120141972A1 (en) | Untangled Euler Diagrams | |
| Van Ban et al. | Querying Data with Fuzzy Information in Object-Oriented Databases Based on Hedge Algebraic Semantic | |
| Cellucci | Models of science and models in science | |
| Königseder et al. | Analyzing generative design grammars | |
| Höhl | COVID-19 and digital transformation: developing an open experimental testbed for sustainable and innovative environments using Fuzzy Cognitive Maps | |
| d'Auriol | Engineering insightful visualizations |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20190704 Address after: 100012 Unit 702, Building 3, Wangchunyuan 17, Beiyuan Home, Chaoyang District, Beijing Patentee after: Luo Xiuchun Address before: 100012 Floor 10, 1022, Building 10, No. 1 Courtyard, Jinfang Road, Chaoyang District, Beijing Patentee before: BEIJING DETA PUBO SOFTWARE CO., LTD. |
|
| TR01 | Transfer of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20180518 Termination date: 20191224 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |