CN115146075B - Data processing system for acquiring knowledge graph - Google Patents
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Abstract
The invention provides a data processing system for acquiring a knowledge graph, which enables a user to participate in a knowledge graph generation process by recording an entity process of clicking a triple by the user and a target instruction sent by the user in checking the triple, so that the recorded contents are all the contents required by the user, and huge contents in the knowledge graph are simplified, so that the knowledge graph is simpler, the user does not need to search the contents required by the user again from the huge triple when the user wants to perform backtrack checking, and the knowledge graph can be directly backtrack checked, thereby improving the user experience.
Description
Technical Field
The application relates to the technical field of knowledge graphs, in particular to a data processing system for acquiring a knowledge graph.
Background
In the prior art, when a user wants to query a certain content, a graph structure corresponding to a keyword input by the user is usually directly displayed, or a link is automatically generated according to the keyword input by the user and an association relation.
The problems existing in the prior art are that a knowledge graph obtained by searching based on an entity name input by a user is not concise, the user needs to query contents required by the user from a huge triple, and association relations in the triple cannot be screened, so that a large amount of time resources are occupied, a relation link diagram automatically generated based on keywords input by the user and the association relations can cause contents which are deviated from the contents which the user wants to view, and the two types of the contents do not have a function of recording nodes browsed by the user, so that the user cannot conveniently backtrack and view the contents, and the user experience is poor.
Disclosure of Invention
In order to solve the above technical problems, the present invention provides a data processing system for acquiring a knowledge graph, the system comprising: a preset ternary list, a processor and a memory storing a computer program which, when executed by the processor, performs the steps of:
s100, obtaining the associated entity A 1 w ，A 1 w Obtaining A for the w-th associated entity in the associated entity list corresponding to the target associated entity, wherein the value of w is 1 to n1, and n1 is the number of the associated entities corresponding to the target associated entity under the target association relationship 1 w Corresponding target associated entity list { A 2 w1 ，A 2 w2 ，……，A 2 wr ，……，A 2 wdw }，A 2 wr Is A 1 w The value of r is 1 to dw, dw is A 1 w The number of corresponding associated entities under the target association relationship;
s200, according to A 1 w Obtaining A by the corresponding target associated entity list and the target associated relation 2 wr Corresponding target associated entity list { A } i3 r1 ，A i3 r2 ，……，A i3 rf ，……，A i3 rpr }，A i3 rf Is A i2 wr F =1 \8230 \ 8230 \ 8230; pr is A in the f-th associated entity corresponding to the target association relationship i2 wr The number of associated entities corresponding to the target association relationship;
s300, replacing the target association relationship with a specified association relationship, and acquiring B = { B = } 1 ，B 2 ，……B u ，……B s }，B u Is A i The u < th > sub-knowledge-graph, u =1 \8230, and \8230s, s is the number of sub-knowledge-graphs corresponding to the target associated entity, and A i Is a target associated entity;
s400, acquiring a sporophore list B based on B h =（B h 1 ，B h 2 ，...，B h x ，...，B h sh ），B h u Is the x-th branch of the h-th sub-entity, the value of x is from 1 to sh, sh is the number of branches included in the h-th sub-entity, the value of h is from 1 to c,c is the number of branch entities;
s500, based on B h And acquiring an entity corresponding to the execution of the last first instruction, and constructing a final knowledge graph structure.
The application has at least the following technical effects: the method has the advantages that the user participates in the generation process of the knowledge graph by recording the entity process of clicking the triples by the user and the target instruction sent by the user in checking the triples, so that all recorded contents are the contents needed by the user, huge contents in the knowledge graph are simplified, the knowledge graph can be simpler, the user does not need to search the needed contents again from the huge triples when the user wants to backtrack and check, the knowledge graph can be directly backtracked and checked, and accordingly user experience is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart illustrating a computer program executed by a data processing system for obtaining a knowledge-graph according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the invention provides a data processing system for acquiring a knowledge graph, which comprises: a preset ternary list, a processor and a memory storing a computer program.
In an embodiment of the present invention, the method further comprises communicating with the processor and one or more display devices for executing the computer program.
In the embodiment of the present invention, the preset three-tuple list may be set based on actual needs, and those skilled in the art know that methods for constructing a knowledge graph through triples in the prior art all fall within the scope of the present invention.
When executed by a processor, the computer program performs the following steps, as shown in fig. 1:
s100, obtaining the associated entity A 1 w ，A 1 w Obtaining A for the w-th associated entity in the associated entity list corresponding to the target associated entity, wherein the value of w is 1 to n1, and n1 is the number of the associated entities corresponding to the target associated entity under the target association relationship 1 w Corresponding target associated entity list { A 2 w1 ，A 2 w2 ，……，A 2 wr ，……，A 2 wdw }，A 2 wr Is A 1 w The value of r is 1 to dw, dw is A 1 w The number of corresponding associated entities under the target association relationship.
In S100, the method includes the following steps:
s1, acquiring a target associated entity according to a keyword input by a user, and acquiring an associated entity list A = { A = corresponding to the target associated entity from a preset ternary group list according to the target associated entity 1 ，……，A i ，……，A m }，A i The method is the ith associated entity corresponding to the target associated entity, i =1 \ 8230 \ 8230: \ 8230, m, m is the number of associated entities corresponding to the target associated entity.
Specifically, the target associated entity refers to an entity input into the system by a user, and the target associated entity refers to an entity having an association relationship with the target associated entity in a preset triple list.
S2, obtaining A from A i And target entity and A i The target entity and A are related in a corresponding incidence relation i The corresponding incidence relation between the two is taken as the target incidence relation.
Specifically, the first instruction is a click instruction, which can be understood as an instruction sent to the processor when the user clicks any node; when detecting that the user clicks any node in the A, marking the node as A i And storing the first association relation in a database, and displaying the first association relation in the association relation input field.
In the embodiment of the present invention, the associated entity list corresponding to the target entity may also be obtained through the following steps:
s21, obtaining a target entity and a target incidence relation, and obtaining an incidence entity list A ' = { A ' corresponding to the target entity from a preset triple according to the target entity and the target incidence relation ' 1 ，……，A' k ，……，A' g }，A' k The target entity is the kth associated entity corresponding to the target entity under the target association relationship, k =1 \8230; \8230g, g is A' k The number of corresponding kth associated entities under the target association relationship.
Therefore, when the user determines the target association relation which needs to be searched, the user can directly input the target association relation, so that the associated entities of the target entities under the same association relation can be displayed on the display device, and the time resource of the user is saved.
S3, according to A i And target association relation, obtaining A from a preset three-tuple list i Corresponding associated entity list { A i1 1 ，A i1 2 ，……，A i1 j ，……，A i1 n1 }，A i1 j Is A i J =1 \ 8230; n1, n1 is A in the corresponding j association entity under the target association relationship i The number of corresponding associated entities under the target association relationship.
S4, based on A i1 j Obtaining A in relation with the target association i1 j Corresponding associated entity list { A i2 j1 ，A i2 j2 ，……，A i2 jr ，……，A i2 jd2 }，A i2 jr Is A i1 j The corresponding r-th association entity under the target association relationship, r =1 \8230 \ 8230, d2, d2 is A i1 j The number of corresponding associated entities under the target association relationship.
S200, according to A 1 w Obtaining A by the corresponding target associated entity list and the target association relation 2 wr Corresponding target associated entity list { A } i3 r1 ，A i3 r2 ，……，A i3 rf ，……，A i3 rpr }，A i3 rf Is A i2 wr F =1 \8230 \ 8230 \ 8230; pr is A in the f-th associated entity corresponding to the target association relationship i2 wr The number of associated entities corresponding to the target association relationship.
S300, replacing the target association relationship with a specified association relationship, and acquiring B = { B = } 1 ，B 2 ，……B u ，……B s }，B u Is A i The corresponding u < th > sub-knowledge graph, u =1 \ 8230 \ 8230, s, s is the number of sub-knowledge graphs corresponding to the target associated entity, and A is i Is a target associated entity.
Specifically, in the embodiment of the present invention, the specified association relationship is null or a key association relationship.
Further, the method also comprises the following steps:
s310, when the specified incidence relation is a key incidence relation, based on A i1 w Obtaining the u sub knowledge graph, wherein A i1 w Replacing the corresponding associated entity list with A i1 w Corresponding Key associated entity List { A i2 w1 ，A i2 w2 ，……，A i2 wr ，……，A i2 wd And A is i2 r Replacing the corresponding target incidence relation list with A i2 r Corresponding list of key associated entities { A } i3 r1 ，A i3 r2 ，……，A i3 rf ，……，A i3 rp }；
S330, when the designated association relationship is null, based on A i1 w Obtaining the u sub knowledge-graph, wherein A i1 w Replacing the corresponding associated entity list with A i1 w Corresponding associated entity list { A } i2 w1 ，A i2 w2 ，……，A i2 wr ，……，A i2 wd And A is i2 r Replacing the corresponding target incidence relation list with A i2 r Corresponding associated entity list { A } i3 r1 ，A i3 r2 ，……，A i3 rf ，……，A i3 rp }。
Specifically, in the embodiment of the present invention, the target association relationship is replaced with a specified association relationship through a target instruction.
Further, the target instruction comprises one or more of a first instruction, a second instruction, a third instruction and a fourth instruction.
The first instruction is an instruction sent when a user clicks the associated entity, the second instruction is an instruction sent when the user modifies the association relationship, the third instruction is a return instruction, and the fourth instruction is an instruction for outputting the final knowledge graph.
Specifically, in the embodiment of the present invention, when it is detected that a system executes a first instruction, a specified entity corresponding to the first instruction, a hierarchy where the specified entity is located, and an association relationship corresponding to the specified entity are obtained and stored in the system.
Further, the association relationship corresponding to the designated entity includes, when the first instruction is executed, the association relationship corresponding to the designated entity and the entity at the previous level and the association relationship corresponding to the designated entity and the entity at the next level.
The technical effects of S100 to S300 are that when a user does not only need to check the fixed association relationship under the current entity, the user can input the corresponding association relationship according to the own requirements to check; or when the user needs to know all the association relations under the current entity, the association relations in the association relation column can be deleted, and the association relations needed by the user can be reselected.
S400, acquiring a sporophore list B based on B h =（B h 1 ，B h 2 ，...，B h x ，...，B h sh ），B h u The value of x is from 1 to sh, sh is the number of the sub-entity sets included by the h sub-entity, the value of h is from 1 to c, and c is the number of the sub-entities.
Specifically, in the embodiment of the present invention, the method further includes the following steps:
s401, based on B h And when the h sub-entity position is detected to comprise the sh sub-entity set, constructing sh branches at the h sub-entity position according to the sh sub-entity sets.
S500, based on B h And acquiring an entity corresponding to the last executed first instruction, and constructing a final knowledge graph structure.
Specifically, in the embodiment of the present application, when the entity corresponding to the last first instruction execution is obtained as the target entity, the constructed final knowledge graph is an annular shape; and when the entity corresponding to the last first instruction execution is not the target entity, the constructed final knowledge graph is in a tree shape.
Further, the final knowledge graph in the tree structure comprises a root node, a leaf node and a path; the root node and the leaf node are entities corresponding to the knowledge graph, and the path is a connection path between the nodes and can be understood as an association relation between the nodes; each node may be represented by a circle and the connection path may be represented by a connection line.
The technical effect of S500 is that, equivalently, the browsing record of the user in the knowledge graph is recorded, the user can conveniently backtrack and check the triple, the browsing time of the user is saved, and the user experience is improved.
The embodiment of the invention provides a data processing system for acquiring a knowledge graph, which enables a user to participate in a generation process of the knowledge graph by recording an entity process of clicking a triple by the user and a target instruction sent by the user in checking the triple, so that the recorded contents are all contents required by the user, huge contents in the knowledge graph are simplified, the knowledge graph is simpler, the user does not need to search the contents required by the user again from the huge triple when the user wants to perform backtrack checking, the knowledge graph can be directly backtrack checked, and the user experience is improved.
Although some specific embodiments of the present invention have been described in detail by way of illustration, it should be understood by those skilled in the art that the above illustration is only for the purpose of illustration and is not intended to limit the scope of the invention. It will also be appreciated by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the invention. The scope of the present disclosure is defined by the appended claims.
Claims (9)
1. A data processing system for obtaining a knowledge graph, the system comprising: a predetermined ternary list, a processor and a memory storing a computer program which, when executed by the processor, performs the steps of:
s100, obtaining the associated entity A 1 w ，A 1 w Obtaining A for the w-th associated entity in the associated entity list corresponding to the target associated entity, wherein the value of w is 1 to n1, n1 is the number of the associated entities corresponding to the target associated entity in the target association relationship 1 w Corresponding target associated entity list { A } 2 w1 ，A 2 w2 ，……，A 2 wr ，……，A 2 wdw }，A 2 wr Is A 1 w The value of r is 1 to dw, dw is A 1 w The number of corresponding associated entities under the target association relationship;
s200, according to A 1 w Obtaining A by the corresponding target associated entity list and the target association relation 2 wr Corresponding target associated entity list { A } i3 r1 ，A i3 r2 ，……，A i3 rf ，……，A i3 rpr }，A i3 rf Is A 2 wr F =1 \8230 \ 8230 \ 8230; pr is A in the f-th associated entity corresponding to the target association relationship 2 wr The number of associated entities corresponding to the target association relationship;
s300, replacing the target association relationship with a specified association relationship, and acquiring B = { B = } 1 ，B 2 ，……B u ，……B s }，B u Is A i The corresponding u < th > sub-knowledge graph, u =1 \ 8230 \ 8230, s, s is the number of sub-knowledge graphs corresponding to the target associated entity, and A is i Is a target associated entity;
s400, acquiring a sporophore list B based on B h ＝(B h 1 ，B h 2 ，...，B h x ，...，B h sh )，B h u The value of x is from 1 to sh, sh is the number of the sub-entity sets included by the h sub-entity, the value of h is from 1 to c, and c is the number of the sub-entities;
s500, based on B h Fetching the last execution first instructionConstructing a final knowledge graph structure by corresponding entities;
in S300, the specified association relationship is null or a key association relationship;
s310, when the specified incidence relation is a key incidence relation, based on A 1 w Obtaining the u intermediate knowledge graph, wherein A 1 w Replacing the corresponding associated entity list with A 1 w Corresponding Key associated entity List { A i2 w1 ，A i2 w2 ，……，A i2 wr ，……，A i2 wd And A is 2 wr Replacing the corresponding target incidence relation list with A i2 wr Corresponding Key associated entity List { A i3 r1 ，A i3 r2 ，……，A i3 rf ，……，A i3 rp }；
S330, when the specified association relationship is null, based on A 1 w Obtaining the u intermediate knowledge-graph, wherein A 1 w Replacing the corresponding associated entity list with A 1 w Corresponding associated entity list { A } i2 w1 ，A i2 w2 ，……，A i2 wr ，……，A i2 wd } and A is i2 r Replacing the corresponding target incidence relation list with A i2 wr Corresponding associated entity list { A } i3 r1 ，A i3 r2 ，……，A i3 rf ，……，A i3 rp }。
2. The system according to claim 1, wherein in S300, the target association relationship is replaced by a specified association relationship through a target instruction.
3. The system according to claim 1, wherein in S500, the final knowledge-graph is a ring shape or a tree shape.
4. The system of claim 2, wherein the target instruction comprises one or more of a combination of a first instruction, a second instruction, a third instruction, and a fourth instruction.
5. The system according to claim 4, wherein the target instruction comprises a first instruction, a second instruction, a third instruction and a fourth instruction, wherein the first instruction is an instruction issued when the user clicks the associated entity, the second instruction is an instruction issued when the user modifies the association relationship, the third instruction is a return instruction, and the fourth instruction is an instruction for outputting the final knowledge graph.
6. The system of claim 5,
when the system is detected to execute a first instruction, acquiring a specified entity corresponding to the first instruction, a hierarchy where the specified entity is located and an incidence relation corresponding to the specified entity and storing the incidence relation into the system.
7. The system of claim 6, wherein specifying the corresponding relationship between the entity comprises specifying the corresponding relationship between the entity and an entity at a previous level and specifying the corresponding relationship between the entity and an entity at a next level when the first instruction is executed.
8. The system according to claim 1, wherein S500 is executed when detecting that the user clicks on a certain leaf node and no operation is performed subsequently.
9. The system according to claim 1, further comprising, in S400, the steps of:
s401, based on B h And when the h sub-entity position is detected to comprise the sh sub-entity set, constructing sh branches at the h sub-entity position according to the sh sub-entity sets.
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