CN1570920A - Knowledge flow network design method based on mode - Google Patents
Knowledge flow network design method based on mode Download PDFInfo
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Abstract
This invention can regulate and manage the knowledge spread in cooperated team by knowledge flow network. The features of this invention are: it takes mode as the foundation of analysis and design of knowledge flow network. By using the existed mode, it can increase the design effective and enhance the reliability and of knowledge flow network and make it easier to understand. By step-by-step refining and layer design from top to bottom, dividing the large and complicated knowledge flow network to several small and simple knowledge flow network component, component integration, it builds a knowledge flow network. It describes and realizes the knowledge flow network by two forms of chart logic and hierarchical structure. It can ensure the knowledge flow network is right and available by intelligent detecting and the verification method based on knowledge potential energy.
Description
Technical field
The present invention relates to field of computer technology, particularly the knowledge flow network design method based on pattern in information management and knowledge flow field.
Technical background
Effectively information management can improve the yield-power and the creativity of knowledge-intensive collaborative team.Correlative study shows in the tissue that the memory space of knowledge is bigger to tissue influence in the transmission of knowledge efficiency ratio tissue, is that the knowledge management method at center is more effective than knowledge management method focusing on people with the process.At present be with the process center knowledge management method also seldom, the reflection of knowledge flow network be transmission of knowledge order in the collaborative team, it attempts to help team to realize effective information management by as workflow being that the method at center is come formalization, optimized the information management of team with the process.
The knowledge flow network is made up of knowledge node and knowledge flow two dvielements, and knowledge node can be organizational member or role, and knowledge flow is represented the transmission of knowledge between knowledge node, and knowledge flow is always initial and end at knowledge node.Up to the present also there is not knowledge flow Network Design in the energy supporting tissue, for cooperative working process provides the method for knowledge timely and effectively.The present invention is based on the knowledge flow network design method of pattern, this method is fundamental analysis and design knowledge flow network with knowledge flow network design pattern, can design reliable, reusable efficiently, easily understands, correct effectively knowledge flow network comes the information management in the supporting tissue.
Summary of the invention
The object of the present invention is to provide a kind of knowledge flow network design method, realize effective information management in order to help tissue based on knowledge flow network design pattern.
This method mainly comprises following several respects:
With knowledge flow network design pattern as analyzing and the elementary cell of design knowledge flow network, by utilizing existing pattern to improve design efficiency and strengthening the reliability of knowledge flow network and the easy property understood; Construct new knowledge flow network by integrated several existing knowledge flow networks, to quicken the design of new knowledge flow network; By top-down progressively refinement and hierarchical design, big and complicated knowledge flow Network Design is decomposed into some little and designs of simple knowledge flow networking component, by the integrated knowledge flow network of constructing of assembly, to reduce design difficulty, improve the reusability and the ease for maintenance of knowledge flow network design.
The knowledge potential energy of examination knowledge node is as the approach of weighing the knowledge flow network efficiency, knowledge potential energy has reflected that the organizational member corresponding with this node has the degree of knowledge, guarantee during the design knowledge flow network that knowledge flow flows to the low node of potential energy from the high node of potential energy, to avoid invalid knowledge transmission; Intelligent design detection and knowledge flow network verification function are provided, and whether verification knowledge flow network is correct at any time in the design process, finally can design correct, effective knowledge flow network by verifying repeatedly and revising.
Hierarchical structure and two kinds of knowledge flow networks of graphics logic method for expressing are provided, the former describes the hierarchical structure of knowledge flow network, the latter describes between the node with layer, between the knowledge flow and the logical relation between node and the knowledge flow, two kinds of expression modes combine common description knowledge flow network, and are not only directly perceived but also be convenient to operation; The knowledge flow network has KFN (Knowledge Flow Network, the knowledge flow network) figure and two kinds of storage modes of XML (extend markup language), the former memory node and the attribute information of knowledge flow and logical relation between them, can directly revert to two kinds of expressions of knowledge flow network, so that continue design and modification, the latter makes it have professional platform independence with the form stored knowledge flow network of XML file, also is beneficial to retrieval and the personnel of confession reading.
The invention technical scheme
The present invention is based on the knowledge flow network design method of pattern.This method is at first analyzed cooperative working process and is disposed the initial knowledge flow network, refinement knowledge flow network then, the required knowledge flow networking component of search from Component Gallery, design other required component and it is added Component Gallery, integrated selected knowledge flow networking component forms final knowledge flow network, specify the property value of each node and knowledge flow by top-down order, verify and revise the knowledge flow network at last.
This programme comprises following several technical characterictic:
1. a basic knowledge flow network design pattern is the abstract of a class knowledge flow network.With the base unit of knowledge flow network design pattern, thereby reuse existing successfully design simply and easily as analysis and design knowledge flow network.Take out ubiquity and representative knowledge flow network design pattern is used for the designer, the designer is fundamental analysis and design knowledge flow network with the good and intelligible pattern of these definition, not only can improve design efficiency, strengthen the reliability of knowledge flow network, and be easy to the understanding between the designer.
2. the cooperative working process of a tissue is often complicated, and the knowledge flow network corresponding with it is inevitable also to be big and complicated.The knowledge flow network is divided into a series of can in advance the realization for this reason, is easy to the knowledge flow networking component that designs, understands and adjust.The present invention has provided the definition of knowledge flow networking component and integrality, correctness and validity.By top-down progressively refinement and hierarchical design big, the baroque knowledge flow Network Design of scale is decomposed into that plurality of scales is less, the design of the better simply knowledge flow networking component of structure, and preferential integrated existing knowledge flow networking component structure knowledge flow network, this solution not only can reduce design difficulty and complexity, and can strengthen the reusability and the ease for maintenance of design.
3. the selection of knowledge flow network design standard directly has influence on the quality of designed knowledge flow network.Knowledge potential energy is the quantized result of node knowledge, and it reflects that the Team Member corresponding with this node has the degree of knowledge, determines " grade " of this member in the knowledge flow network, therefore can be used as the standard of design knowledge flow network.The present invention provides the computing method of knowledge potential energy and based on the knowledge flow Network Design principle and the verification method of potential energy, avoids the efficient of Knowledge Flowing in invalid knowledge transmission, the raising team.
4. the graphics logic of knowledge flow network is represented to represent to describe jointly a knowledge flow network with hierarchical structure.The former describes the knowledge flow network in the mode of digraph: node table advises knows node or knowledge flow networking component, directed arc is represented knowledge flow, logical relation between the attribute of node and knowledge flow and each element is specified in digraph and is showed, is fit to do the figure mode and realize with dilatory by computer programming; The latter describes the hierarchical relationship of knowledge flow network can expand tree structure, and nonleaf node is refined as the knowledge flow network that is made of its child node.There is relation one to one between the node of two kinds of expressions.
5. each knowledge flow network all has KFN figure and two kinds of storage modes of XML, and the former can revert to the graphics logic of knowledge flow network and represents to represent with hierarchical structure, so that continue design and modification; The latter makes it have cross-platform characteristic, also is convenient to personnel's reading and understanding.
6. in order to guarantee knowledge flow network correct, effective, intelligent design and detection and knowledge flow network verification function are provided.The former represents to generate automatically the design operation that its hierarchical structure is represented and cancellation can lead to errors according to the graphics logic of knowledge flow network; The latter with node knowledge potential energy serve as the basis in conjunction with the logical relation of knowledge flow network checking knowledge flow network, help the designer to revise and also optimize the knowledge flow network, finally can design correct and knowledge flow network efficiently through verifying repeatedly and revising.
Description of drawings
Fig. 1 is a realization schematic diagram of the present invention.
Fig. 2 is " in proper order " knowledge flow network design pattern diagram.
Fig. 3 is " broadcasting " knowledge flow network design pattern diagram.
Fig. 4 is " netted " knowledge flow network design pattern diagram.
Fig. 5 is " resource-media " knowledge flow network design pattern diagram.
Fig. 6 is " Split-Join " knowledge flow network design pattern diagram.
Fig. 7 is a knowledge flow assembly refinement instance graph.
Fig. 8 is a knowledge flow network design process flow diagram flow chart of the present invention.
Embodiment
The present invention is based on knowledge flow network design pattern and knowledge flow networking component, top-down progressively refinement, hierarchical design knowledge flow network.
Fig. 1 is a realization schematic diagram of the present invention, mainly is made of three parts: the checking of the hierarchical structure display unit in left side, the graphics logic display unit on right side and bottom is display unit as a result.
The main design effort of model is finished in the graphics logic display unit, is figure by dilatory mouse therein and defines a knowledge flow network.A knowledge flow network is a digraph in these parts, and node table advises knows flow network assembly or knowledge node, and directed arc is represented knowledge flow.Knowledge flow networking component wherein further is refined as a sub-knowledge flow network.The top layer knowledge flow network km that shows in the graphics logic display unit of Fig. 1 comprises eight nodes, wherein, node C2 and C3 represent the knowledge flow networking component, are km-team1 and two sub-knowledge flow networks of km-team2 by further refinement respectively, and other node is a knowledge node.Double-click node or the directed arc in these parts and fill in the attribute that the respective dialog frame is specified knowledge node or knowledge flow, wherein included knowledge potential energy, whether effectively it be to weigh knowledge flow network basis.But the tree-shaped extension layer aggregated(particle) structure that in the hierarchical structure display unit is the knowledge flow network represents, the hierarchical relationship of its expression knowledge flow network, that is and, nonleaf node is represented the knowledge flow network that is made of the knowledge flow between its child node and the child node.Root node km during the hierarchical structure in Fig. 1 left side is represented represents by its child node K1, C2, and C3, K4, the knowledge flow network that K5 and K6 and the knowledge flow between them constitute, nonleaf node C2 (km-team1) represents by its child node K
2.1, K
2.2K
2.6And the sub-knowledge flow network of the formation of the knowledge flow between them, nonleaf node C3 (km-team2) then represents by its child node K
3.1, K
3.2K
3.6And the sub-knowledge flow network of the formation of the knowledge flow between them.During representing, hierarchical structure except that root node, has relation one to one during other node and graphics logic are represented between the node.Any time in the design process can verify that checking is the result provide in the display unit as a result in checking to the knowledge flow network.
Be the introduction of several related contents below:
1. the knowledge potential energy of node
The knowledge potential energy of node and the dynamic change of knowledge potential energy have reflected the comprehension and the creativity of corresponding organizational member, have determined " grade " of this node in the knowledge flow network, therefore can be used as the standard of design knowledge flow network.Potential energy is one 0 to 1 number, passing in time and difference.The variation of potential energy can calculate by the potential energy value of its potential energy initial value, learning ability parameter and forerunner's node.Each knowledge flow that knowledge flow network is that effectively and if only if it comprised all is to flow to the low node of potential energy from the high node of potential energy.Follow the knowledge transmission during design knowledge flow network and can only occur between the node that has potential energy difference, and knowledge always flows to the principle of the low node of potential energy from the high node of potential energy.The present invention guarantees effective transmission of knowledge according to this principle design knowledge flow network, avoids invalid knowledge transmission.
2. knowledge flow network design pattern
A knowledge flow network design pattern is the abstract of a class knowledge flow network, is ubiquity and representative structure in the knowledge flow network.Use the good and intelligible knowledge flow network design pattern of definition not only can improve design efficiency, strengthen the reliability of designed knowledge flow network, and help the understanding between the deviser.
1) " in proper order " pattern
The structure of this pattern is linear, and except start node and terminal node, each node all has and only have forerunner's node and a descendant node.The potential energy of each node all is lower than its forerunner's node and is higher than its descendant node.Fig. 2 is the example of " in proper order " pattern, and the knowledge potential energy of node is to successively decrease by the order of A, B, C, D.
2) " broadcasting " pattern
The similar of this pattern is in tree structure, Fig. 3 is the example of " broadcasting " pattern, and wherein the potential energy of root node A should be higher than other all nodes.
3) " netted " pattern
Each node in this pattern all can be arrived by a path that is made of node and directed arc by other node, it is characterized in that end-to-end, Fig. 4 (a) is an example of mesh mode, and the ring-type pattern shown in Fig. 4 (b) is a kind of special case of mesh mode, and its each node all has only unique go into arc and unique go out arc.
4) " resource-media " pattern
Do not have direct knowledge flow between the knowledge node in this pattern, any knowledge flow all occurs between knowledge node and the resource node, and resource can be blackboard, knowledge base, data form, any type of file, even can be software equipment.Fig. 5 is an example of this pattern, and wherein the ring-type node is represented resource node, and the rectangle node table advises the knowledge node, and downward arrow represents to write the knowledge flow of a class, and the arrow that makes progress represents to read the knowledge flow of a class.Knowledge flow between the knowledge node (dotting among the figure) can be pushed away by the knowledge flow between knowledge node and the resource node, for example, the knowledge flow from knowledge node A to knowledge node B (representing with A → B) can be pushed away by A → Resource1 and Resource1 → B.
5) " Split-join " pattern
This pattern is by a start node, and a terminal node and one " black box " constitute.Start node has a plurality of existence to concern CON
1The output knowledge flow; Terminal node has a plurality of existence to concern CON
2The input knowledge flow; " black box " receives the input knowledge flow of the output knowledge flow of start node as oneself, and oneself output knowledge flow is sent to terminal node imports knowledge flow as it.
Fig. 6 is a synoptic diagram of this pattern, and " black box " here is meant that its inner structure does not need the knowledge flow network understood, that is to say that its inner structure may be instantiated as the knowledge flow network of any structure except that its input knowledge flow and output knowledge flow.Guarantee the logically consistent CON of " Split-join " pattern
1And CON
2Must meet following rule:
If rule 1 CON
1=' and-split ' CON so
2=' and-join ' or ' or-join ';
If rule 2 CON
1=' or-split ' CON so
2=' or-join ';
If rule 3 CON
1=' xor-split ' CON so
2=' xor-join ' or CON
2=' or-join ';
If rule 4 CON
1=' and-join ' CON so
2=' and-split '.
Knowledge flow network shown in Figure 1 can be regarded as one and comprises that " split-join " schema instance of node K1, C2, C3, K4, K5 and " broadcasting " schema instance that one comprises node K5, K6, K7 constitute.Use Design Mode in the knowledge flow network design, can simply reuse successful design, not only improved knowledge flow Network Design efficient, reusability, maintainability and the reliability of knowledge flow network have been strengthened, reduce wrong probability of happening, and be easy to the understanding between the designer.
3. knowledge flow networking component
The knowledge flow networking component is the largest unit of structure knowledge flow network, each knowledge flow networking component all further is refined as a sub-knowledge flow network, has a sub-digraph and stalk tree corresponding with it in the graphics logic of knowledge flow network is represented to represent with hierarchical structure respectively.If a knowledge flow networking component satisfies following condition, we claim that it is that definition is complete:
1) each internal node has at least one input knowledge flow and at least one output knowledge flow;
2) all inner knowledge flows all point to an internal node except that stopping knowledge flow;
3) from the start node to the terminal node, there is a paths;
4) there are not isolated node or sub-knowledge flow network.
The present invention adopts and will be decomposed into a series of can in advance the realization to big and complicated knowledge flow network, be easy to the design proposal of the knowledge flow networking component that designs, understand and adjust, thereby realize big, the baroque knowledge flow Network Design of scale is reduced design difficulty; Each assembly is relatively independent, safeguards and needn't change whole knowledge flow network when revising a knowledge flow networking component, is convenient to safeguard; The knowledge flow networking component that designs in advance can be integrated in the new knowledge flow network, thereby quicken the design of new knowledge flow network.
Node C2 in the knowledge flow network in the graphics logic display unit of Fig. 1 and C3 are by the knowledge flow networking component of further refinement, corresponding with them is respectively km-team1 and two sub-knowledge flow networks of km-team2, and their concrete structure as shown in Figure 7; Other node is not subdivisible knowledge node.
4. knowledge flow network integration operation
A knowledge flow network can be got by two or more existing knowledge flow network integrations by following operation.
1) union operation: merge the common node that different knowledge flow networks are comprised;
2) add the knowledge flow operation: connect node in the different knowledge flow networks by adding knowledge flow;
3) adding conditional operation: " join " or " split " condition of interpolation is represented the relation between a plurality of knowledge flows relevant with same node;
4) embedding operation: a knowledge flow network is put into a node;
5) graphic operation: by uniting ' ∪ ', intersect ' ∩ ' or subtracting each other '-' integrate knowledge flow network.
For example, the knowledge flow network of Fig. 7 (a) expression is by the integrated node K that comprises respectively
2.1And K
2.2, node K
2.3And K
2.4And node K
2.5And K
2.6Three " in proper order " schema instances constitute, the integrated operation of being adopted comprises: add knowledge flow K
2.2→ K
2.5And K
2.4→ K
2.5Adding conditional " join " expression knowledge flow K
2.2→ K
2.5And K
2.4→ K
2.5Between relation, they and same node K
2.5Relevant.The knowledge flow network of Fig. 7 (b) expression is to comprise node K by integrated one
3.1, K
3.2, K
3.3" in proper order " schema instance and one comprise node K
3.3, K
3.4, K
3.5And K
3.6" broadcasting " schema instance constitute, the integrated operation of being adopted is the common node K that merges two schema instances
3.3
The design process of this method mainly comprises following seven steps (referring to Fig. 8):
S1: according to the cooperative working process in the top-down sequence analysis tissue, promptly, at first by the conspiracy relation tightness degree organizational member is divided in several groups, the cooperative working process between the analysis group is analyzed the cooperative working process between the member in the group more then;
S2: dispose initial knowledge flow network according to analysis result, that is, and according to the knowledge flow network of the design of the conspiracy relation between group top layer.Shown in Figure 1 is exactly the structure of a top layer knowledge flow network, and wherein node C2 represents two different groups respectively with node C3;
S3: refinement knowledge flow network promptly, serves as that the basis is according to the knowledge flow network in the conspiracy relation design group between the group member with knowledge flow network design pattern and experience.Node C2 among Fig. 1 and C3 are km-team1 and two sub-knowledge flow networks of km-team2 by further refinement respectively, and concrete structure is seen Fig. 7 (a) and Fig. 7 (b);
S4: other knowledge flow networking component that required knowledge flow networking component of search and designing institute lack from Component Gallery, the required knowledge flow networking component of other that lacks in the design component storehouse, checking is also revised newly-designed knowledge flow networking component, determine errorless after, new knowledge flow network assembly is added Component Gallery;
S5: operate integrated selected knowledge flow networking component according to the knowledge flow network integration that provides previously, form final knowledge flow network;
S6: the property value of specified node and knowledge flow, promptly, by top-down order, by in the graphics logic display unit, double-clicking node or directed arc and filling in corresponding dialog box, be each node and knowledge flow specified attribute value, the attribute of appointment comprises the knowledge potential energy initial value of title, the node of node and directed arc, earliest start time that node is executed the task and concluding time etc. the latest;
S7: checking is also revised the knowledge flow network.Any moment in design process can verify that guarantee that it is correct, effective, the result provides in the display unit as a result in checking to the knowledge flow network.The wrong corresponding checking of each that exists in the model is a record in the display unit as a result, and every record all is made of errors present and two fields of wrong content.These two fields illustrate title and the wrong specific descriptions that this wrong knowledge flow network or knowledge flow networking component take place respectively, and the former helps the user to dwindle examination scope, and latter's guides user corrects mistakes.The checking content mainly comprises: whether the knowledge flow network exists isolated node or isolated knowledge flow network, whether the knowledge flow networking component has more than a start node or terminal node, whether whether the knowledge flow networking component is that definition is complete, effective etc. according to the logical relation judgemental knowledge flow network of knowledge node potential energy and knowledge flow network.The designer finally can design correct, effective knowledge flow network by verifying repeatedly and revising.
The several problems that solve during the design knowledge flow network:
1. deletion of node: deletion originates in or ends at the knowledge flow of this node, cancels the logical relation relevant with this node, deletes this node.If deleted node is the knowledge flow networking component, delete all elements and and the pairing KFN figure of this assembly and the XML storage file of this assembly.
2. deletion knowledge flow: cancel between the node of setting up owing to the connection of this knowledge flow, between the knowledge flow and the logical relation between node and the knowledge flow, delete this knowledge flow.
3. intelligent design detects: knowledge flow must originate in and end at knowledge node, therefore forbids adding originating in non-node or originating in node but end at the knowledge flow of non-node; If a node can only have a descendant node, then forbid adding the knowledge flow that second originates in this node; If a node can only have forerunner's node, then forbid adding the knowledge flow that second ends at this node.
4. set up hierarchical structure is represented and graphics logic is represented corresponding relation, realize the switching between the knowledge flow networking component: set up hierarchical structure in representing nonleaf node and the one-to-one relationship between the knowledge flow networking component node of graphics logic in representing and hierarchical structure in representing leaf node and the one-to-one relationship between the knowledge node of graphics logic in representing.Nonleaf node during hierarchical structure is represented further is refined as a knowledge flow network in graphics logic is represented, nonleaf node during the double-click hierarchical structure is represented represents to switch to the pairing knowledge flow network of this node with graphics logic, and the node of clicking except that root node represents to switch to the pairing knowledge flow network of its father node with the graphics logic of knowledge flow network.
5. model storage: all to there being a filename identical, extension name is respectively two files of KFN and XML to each knowledge flow network.The former is the KFN map file, and the graphics logic that can revert to the knowledge flow network is represented to represent with hierarchical structure, so that continue design and modification; The latter writes down the hierarchical relationship of knowledge flow network, reach the logical relation between node and the knowledge flow between the node, between the knowledge flow, the association attributes of node and knowledge flow because the XML file can be browsed by application programs such as browsers, is convenient to personnel's reading and understanding.
Claims (8)
1. based on the knowledge flow network design method of pattern, this method is with the basis of knowledge flow network design pattern as analysis and design knowledge flow network, top-down progressively refinement, hierarchical design, to be decomposed into some little and designs of simple knowledge flow networking component a big and complicated knowledge flow Network Design, and integrated existing assembly is constructed the knowledge flow network; Whether effectively the knowledge potential energy of knowledge node also provided knowledge flow determination methods in view of the above as the standard of weighing the organizational member ability, detect and verify the knowledge flow network in real time; Each knowledge flow network all have levels structure and two kinds of method for expressing of graphics logic and KFN figure and two kinds of storage modes of XML.
2. the knowledge flow network design method based on pattern according to claim 1, it is characterized in that, take out basic knowledge flow network schemer, with it as analyzing and the basis of design knowledge flow network, by utilizing existing pattern to improve design efficiency and strengthening the reliability of knowledge flow network and the easy property understood.
3. the knowledge flow network design method based on pattern according to claim 1, it is characterized in that, by top-down progressively refinement and hierarchical design, big and complicated knowledge flow Network Design is decomposed into some little and designs of simple knowledge flow networking component, by the integrated knowledge flow network of constructing of assembly, thereby the reduction design difficulty, the reusability and the ease for maintenance of raising knowledge flow network design.
4. the knowledge flow network design method based on pattern according to claim 1, it is characterized in that, the knowledge potential energy of examination knowledge node is as the approach of weighing the knowledge flow network efficiency, knowledge potential energy has reflected that the organizational member corresponding with this node has the degree of knowledge, guarantee during the design knowledge flow network that knowledge flow flows to the low node of potential energy from the high node of potential energy, to avoid invalid knowledge transmission.
5. the knowledge flow network design method based on pattern according to claim 1, it is characterized in that, intelligent design detection and knowledge flow network verification method are provided, set up the logical relation of knowledge flow network according to deviser's operation, automatically the cancellation faulty operation generates corresponding hierarchical structure synchronously and represents in the design process; With node knowledge potential energy is validation criteria, in conjunction with knowledge flow cellular logic structure, provides real-time verification, helps the designer to revise, optimizes the knowledge flow network, guarantees designed knowledge flow network correct and effective.
6. the knowledge flow network design method based on pattern according to claim 1, it is characterized in that, hierarchical structure and two kinds of knowledge flow networks of graphics logic method for expressing are provided, the former describes the hierarchical structure of knowledge flow network, and the latter describes between the node with layer, between the knowledge flow and the logical relation between node and the knowledge flow.
7. the knowledge flow network design method based on pattern according to claim 1, it is characterized in that, two kinds of knowledge flow network storage modes are provided, and a kind of is can revert to graphics logic to represent that the KFN figure mode represented with hierarchical structure, another kind are to be convenient to the XML mode that personnel read and understand.
8. the knowledge flow network design method based on pattern according to claim 1, its concrete steps are as follows:
S1: according to the cooperative working process in the top-down sequence analysis tissue;
S2: dispose initial knowledge flow network according to analysis result;
S3: refinement knowledge flow network;
S4: other knowledge flow networking component that required knowledge flow networking component of search and designing institute lack from Component Gallery;
S5: operate integrated selected knowledge flow networking component according to the knowledge flow network integration, form final knowledge flow network;
S6: the property value of specified node and knowledge flow;
S7: checking is also revised the knowledge flow network.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1992728B (en) * | 2005-10-26 | 2010-12-01 | 国际商业机器公司 | Systems and methods for facilitating group collaborations |
| CN101593188B (en) * | 2008-05-30 | 2013-07-24 | 日电（中国）有限公司 | Method and system for integrating hiberarchies |
| WO2022032685A1 (en) * | 2020-08-14 | 2022-02-17 | Siemens Aktiengesellschaft | Method and device for constructing multi-level knowledge graph |
-
2004
- 2004-05-08 CN CN 200410043385 patent/CN1570920A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1992728B (en) * | 2005-10-26 | 2010-12-01 | 国际商业机器公司 | Systems and methods for facilitating group collaborations |
| CN101593188B (en) * | 2008-05-30 | 2013-07-24 | 日电（中国）有限公司 | Method and system for integrating hiberarchies |
| WO2022032685A1 (en) * | 2020-08-14 | 2022-02-17 | Siemens Aktiengesellschaft | Method and device for constructing multi-level knowledge graph |
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