CN112784058A - Entity correlation obtaining method based on dynamic map - Google Patents
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Abstract
The invention belongs to the technical field of correlation analysis, and particularly discloses a method for acquiring entity correlation based on a dynamic map, which comprises the following steps: s1: establishing an initial knowledge graph; s2: acquiring a dynamic knowledge graph; s3: extracting a search entity in the search word information; s4: performing graph calculation according to the search entity to obtain all target entities related to the search entity; s5: acquiring the similarity between each target entity and the search entity; s6: and obtaining an entity correlation result according to all the target entities and the search entity and the similarity of all the target entities and the search entity. The invention solves the problems that the prior art lacks a method for directly analyzing the correlation and cannot intuitively obtain the correlation result.
Description
Technical Field
The invention belongs to the technical field of correlation analysis, and particularly relates to an entity correlation obtaining method based on a dynamic map.
Background
Correlation analysis is a statistical analysis method for studying the correlation between two or more equally positioned random variables. For example, between the height and weight of a person; the correlation between the relative humidity in the air and the rainfall is a problem of relevant analytical research. Differentiation between correlation and regression analysis: regression analysis focuses on studying the dependence between random variables in order to predict one variable for another; correlation analysis focuses on finding a myriad of correlation properties between random variables. The related analysis has application in the aspects of industry, agriculture, hydrology, meteorology, social economy, biology and the like.
The correlation analysis is to analyze the markers which are really connected in the population, and the main body is to analyze the markers with the causal relationship in the population. It is a process of describing the closeness of the relationship between objective things and expressing it with proper statistical indexes. The birth rate rises along with the rise of the economic level in a period of time, which shows that the two indexes are in positive correlation; in another period, the birth rate is reduced with the further development of economic level, and the two indexes are in negative correlation.
In the modern days of digitization, data speaking is increasingly advocated. Among data analysis, correlation analysis is one of the most common ways. Knowledge maps play an important role in relevance analysis. However, most of the existing knowledge maps have information hysteresis and do not have the function of updating data in real time. With the development of the information era, the information data volume is continuously enlarged, and the data volume of billions of nodes prevents a user from directly obtaining required contents for correlation analysis and directly obtaining correlation analysis results.
Disclosure of Invention
The present invention aims to solve at least one of the above technical problems to a certain extent.
Therefore, the invention aims to provide an entity correlation obtaining method based on a dynamic graph, which is used for solving the problems that the prior art lacks a direct correlation analysis method and cannot intuitively obtain a correlation result.
The technical scheme adopted by the invention is as follows:
an entity correlation obtaining method based on a dynamic map comprises the following steps:
s1: establishing an initial knowledge graph;
s2: updating real-time information of the initial knowledge graph to obtain a dynamic knowledge graph;
s3: acquiring search word information input by a user, and extracting a search entity in the search word information;
s4: based on the dynamic knowledge map, performing graph calculation according to the searched entity, and performing desalination treatment on other entities irrelevant to the searched entity to obtain all target entities relevant to the searched entity;
s5: acquiring the similarity between each target entity and the search entity;
s6: and obtaining an entity correlation result according to all the target entities and the search entity and the similarity of all the target entities and the search entity.
Further, the specific method of step S1 is: and acquiring original data information, and storing and processing the original data information to obtain an initial knowledge graph.
Further, the specific step of step S2 is:
s2-1: acquiring latest data information in real time, and taking the latest data information as a reference entity;
s2-2: extracting the existing entity in the initial knowledge graph, and comparing the reference entity with the existing entity to obtain a comparison result;
s2-3: if the comparison result shows that the comparison is wrong, manually judging and checking the reference entity and the comparison entity to select a final standard entity;
if the comparison result shows no error, the existing entity is taken as the final standard entity;
s2-4: and repeating the steps S2-1 to S2-3 to obtain the dynamic knowledge graph according to the final standard entity.
Further, in step S4, the specific method of the desalination process is: and deleting other entities irrelevant to the search entity and the relation between the other entities and the search entity in the dynamic knowledge map.
Further, in step S4, the specific method of the desalination process is: graph transparency is increased for other entities in the dynamic knowledge-graph that are not related to the search entity and for relationships between the other entities and the search entity.
Further, step S5 is preceded by step S4.5: all target entities and search entities are represented as entity vectors.
Further, the formula of the entity vector is:
Ii＝(E1i,E2i,...,Edi)T
in the formula IiAn entity vector that is an entity; i is an entity indicator quantity; ediIs the value of the d dimension of the ith entity; d is the total number of dimensions.
Further, in step S5, according to the cosine theorem, the similarity between each target entity and the search entity is obtained.
Further, the formula of the cosine theorem is:
wherein cos (θ) is a cosine value; a. theiIs a search entity; b isiIs a related entity; i is an indicating quantity; n is the total number of related entities.
Further, in step S6, the entity relevance result is an entity relevance statistical graph obtained by combining all the target entities with the search entity and the similarity between all the target entities and the search entity.
The invention has the beneficial effects that:
1) the invention realizes the knowledge map dynamization and the knowledge map visualization, and improves the practicability of the knowledge map and the adaptability in the correlation analysis;
2) the dynamic map is combined with the statistical map, the purposes of updating data in real time and reducing the hysteresis of the knowledge map are achieved, the noise reduction processing on a large amount of data is achieved, and the change and the relation of the correlation degree between the entities are reflected visually.
Other advantageous effects of the present invention will be described in detail in the detailed description.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an entity correlation obtaining method based on a dynamic graph.
Detailed Description
The invention is further described with reference to the following figures and specific embodiments. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Functional details disclosed herein are merely illustrative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments of the invention. When the terms "comprises," "comprising," "includes," and/or "including" are used herein, they specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, numbers, steps, operations, elements, components, and/or groups thereof.
It should also be noted that, in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed substantially concurrently, or the figures may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
It should be understood that specific details are provided in the following description to facilitate a thorough understanding of example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. For example, systems may be shown in block diagrams in order not to obscure the examples in unnecessary detail. In other instances, well-known processes, structures and techniques may be shown without unnecessary detail in order to avoid obscuring example embodiments.
Example 1
The knowledge map is a series of different graphs displaying the relationship between the knowledge development process and the structure, and is used for describing knowledge resources and carriers thereof by using a visualization technology, mining, analyzing, constructing, drawing and displaying knowledge and the mutual relation between the knowledge resources and the carriers. The knowledge graph is a modern theory which achieves the aim of multi-discipline fusion by combining theories and methods of applying subjects such as mathematics, graphics, information visualization technology, information science and the like with methods such as metrology introduction analysis, co-occurrence analysis and the like and utilizing a visualized graph to vividly display the core structure, development history, frontier field and overall knowledge framework of the subjects. It can provide practical and valuable reference for subject research.
The knowledge graph is released by Google at the earliest, and in order to improve the quality of answers returned by a search engine and the efficiency of user query, the search engine can gain insight into semantic information behind the user query under the assistance of the knowledge graph and then return more accurate structured information, so that the query requirement of the user can be met more possibly. When we perform the search, the associations on the right side of the search results come from the application of knowledge-graph techniques. We receive a variety of recommended information almost every day, from news, shopping to eating, entertainment. Personalized recommendation is used as an important means for information filtering, and can recommend proper services according to habits and hobbies of people and come from application of knowledge graph technology. More and more application scenes of searching, maps, personalized recommendation, internet, wind control and banks … … are more and more dependent on the knowledge map, so that the knowledge map is everywhere.
An entity correlation obtaining method based on a dynamic graph, as shown in fig. 1, includes the following steps:
s1: establishing an initial knowledge graph, wherein the specific method comprises the following steps: acquiring original data information, and storing and processing the original data information to obtain an initial knowledge graph;
the original data information comprises webpage data information on each large website, not only comprises each large official website, but also relates to a plurality of small private websites, and aims to collect a large amount of data information, the more the collected data information is, the more perfect the constructed initial knowledge graph is, and the more accurate the correlation analysis result is; knowledge-graph can provide high-quality structured data, and is widely applied to a plurality of fields of artificial intelligence, such as automatic question and answer, search engine and information extraction, a typical knowledge-graph is usually represented in a form of triples (head entities, relations and tail entities), most of the existing knowledge-graphs are constructed in a semi-automatic or manual mode, and therefore, two problems exist: incomplete, potential relationships between many entities in the knowledge graph are not mined; the expansibility is poor, and a new entity cannot be automatically added into the knowledge graph, so that in order to solve the problems, the technical scheme of the dynamic knowledge graph is introduced;
s2: updating real-time information of the initial knowledge graph to obtain a dynamic knowledge graph, and specifically comprising the following steps:
s2-1: acquiring latest data information in real time, and taking the latest data information as a reference entity;
s2-2: extracting the existing entity in the initial knowledge graph, and comparing the reference entity with the existing entity to obtain a comparison result;
s2-3: if the comparison result shows that the comparison is wrong, manually judging and checking the reference entity and the comparison entity to select a final standard entity;
in a real scene, the coincidence degree between the extra information of the new entity and the extra information of the entity in the knowledge graph is not particularly high, for example, the description information of the entity, and many words in the description information of the new entity do not appear in the description of the entity in the knowledge graph, so the scheme compares the entity with the existing entity according to the reference, and performs necessary manual judgment and audit, thereby solving the problems;
if the comparison result shows no error, the existing entity is taken as the final standard entity;
s2-4: repeating the steps S2-1 to S2-3, and obtaining a dynamic knowledge graph according to the final standard entity;
the method is a real-time dynamic updating process, realizes the sustainable updating of the dynamic knowledge graph, and updates the analysis basis in real time when correlation analysis is carried out;
s3: acquiring search word information input by a user, and extracting a search entity in the search word information;
s4: based on the dynamic knowledge map, performing graph calculation according to the searched entity, and performing desalination treatment on other entities irrelevant to the searched entity to obtain all target entities relevant to the searched entity;
the specific method of the desalination treatment comprises the following steps: deleting other entities irrelevant to the search entity and the relation between the other entities and the search entity in the dynamic knowledge map;
in order to ensure the accuracy of the correlation analysis, other unrelated entities and the relations between the other entities and the search entity are deleted, namely the simplification and the noise reduction of the dynamic knowledge graph are realized, the processing efficiency of the correlation analysis is improved, and the efficiency is improved more obviously before a large amount of data;
s4.5: representing all target entities and search entities as entity vectors;
the formula for the entity vector is:
Ii＝(E1i,E2i,...,Edi)T
in the formula IiAn entity vector that is an entity; i is an entity indicator quantity; ediIs the value of the d dimension of the ith entity; d is the total dimension;
s5: acquiring the similarity between each target entity and the search entity;
according to the cosine theorem, the similarity between each target entity and the search entity is obtained;
the formula of the cosine theorem is:
wherein cos (θ) is a cosine value; a. theiIs a search entity; b isiIs a related entity; i is an indicating quantity; n is the total number of related entities;
s6: obtaining entity correlation results according to the similarity between all target entities and the search entity and the similarity between all target entities and the search entity, wherein the entity correlation results are entity correlation statistical graphs obtained by combining the similarity between all target entities and the search entity and the similarity between all target entities and the search entity;
the entity correlation result visualization method has the advantages that the entity correlation result visualization method achieves visualization of entity correlation results, the representation forms of the entity correlation results are various, the entity correlation results can be histograms, curve graphs or other representation forms, the entity correlation statistical graphs in broken line forms are selected in the embodiment, and the correlation analysis results can be visually displayed.
The invention realizes the dynamization and visualization of the knowledge map, improves the practicability of the knowledge map and the adaptability in correlation analysis, combines the dynamic map with the statistical map, realizes the purposes of updating data in real time and reducing the hysteresis of the knowledge map, realizes the noise reduction treatment of a large amount of data, and visually reflects the change and the relation of the correlation degree between entities.
Example 2
The embodiment is improved based on the technical scheme of the embodiment 1, and compared with the technical scheme of the embodiment 1, the embodiment is characterized in that: preferably, in step S4, the specific method of the desalination process is: graph transparency is increased for other entities in the dynamic knowledge-graph that are not related to the search entity and for relationships between the other entities and the search entity.
In order to ensure the accuracy of correlation analysis, the graph transparency of other entities irrelevant to the search entity and the relationship between the other entities and the search entity in the dynamic knowledge graph is increased, namely, the simplification and noise reduction processing of the dynamic knowledge graph are realized, the processing efficiency of the correlation analysis is improved, and the efficiency is improved more obviously before a large amount of data.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may alternatively be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present invention is not limited to any specific combination of hardware and software.
The embodiments described above are merely illustrative, and may or may not be physically separate, if referring to units illustrated as separate components; if a component displayed as a unit is referred to, it may or may not be a physical unit, and may be located in one place or distributed over a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment. Can be understood and implemented by those skilled in the art without inventive effort.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: modifications of the technical solutions described in the embodiments or equivalent replacements of some technical features may still be made. And such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
The present invention is not limited to the above-described alternative embodiments, and various other forms of products can be obtained by anyone in light of the present invention. The above detailed description should not be taken as limiting the scope of the invention, which is defined in the claims, and which the description is intended to be interpreted accordingly.
Claims (10)
1. An entity correlation obtaining method based on a dynamic map is characterized in that: the method comprises the following steps:
s1: establishing an initial knowledge graph;
s2: updating real-time information of the initial knowledge graph to obtain a dynamic knowledge graph;
s3: acquiring search word information input by a user, and extracting a search entity in the search word information;
s4: based on the dynamic knowledge map, performing graph calculation according to the searched entity, and performing desalination treatment on other entities irrelevant to the searched entity to obtain all target entities relevant to the searched entity;
s5: acquiring the similarity between each target entity and the search entity;
s6: and obtaining an entity correlation result according to all the target entities and the search entity and the similarity of all the target entities and the search entity.
2. The method for obtaining entity correlation based on dynamic graph as claimed in claim 1, wherein: the specific method of step S1 is as follows: and acquiring original data information, and storing and processing the original data information to obtain an initial knowledge graph.
3. The method for obtaining entity correlation based on dynamic graph as claimed in claim 1, wherein: the specific steps of step S2 are as follows:
s2-1: acquiring latest data information in real time, and taking the latest data information as a reference entity;
s2-2: extracting the existing entity in the initial knowledge graph, and comparing the reference entity with the existing entity to obtain a comparison result;
s2-3: if the comparison result shows that the comparison is wrong, manually judging and checking the reference entity and the comparison entity to select a final standard entity;
if the comparison result shows no error, the existing entity is taken as the final standard entity;
s2-4: and repeating the steps S2-1 to S2-3 to obtain the dynamic knowledge graph according to the final standard entity.
4. The method for obtaining entity correlation based on dynamic graph as claimed in claim 1, wherein: in step S4, the specific method of the desalination process is: and deleting other entities irrelevant to the search entity and the relation between the other entities and the search entity in the dynamic knowledge map.
5. The method for obtaining entity correlation based on dynamic graph as claimed in claim 1, wherein: in step S4, the specific method of the desalination process is: graph transparency is increased for other entities in the dynamic knowledge-graph that are not related to the search entity and for relationships between the other entities and the search entity.
6. The method for obtaining entity correlation based on dynamic graph as claimed in claim 1, wherein: step S4.5 is also included before step S5: all target entities and search entities are represented as entity vectors.
7. The method according to claim 6, wherein the method comprises: the formula of the entity vector is as follows:
Ii＝(E1i,E2i,...,Edi)T
in the formula IiAn entity vector that is an entity; i is an entity indicator quantity; ediIs the value of the d dimension of the ith entity; d is the total number of dimensions.
8. The method for obtaining entity correlation based on dynamic graph as claimed in claim 1, wherein: in step S5, the similarity between each target entity and the search entity is obtained according to the cosine theorem.
9. The method according to claim 8, wherein the method comprises: the formula of the cosine theorem is as follows:
wherein cos (θ) is a cosine value; a. theiFor searchingA cable entity; b isiIs a related entity; i is an indicating quantity; n is the total number of related entities.
10. The method for obtaining entity correlation based on dynamic graph as claimed in claim 1, wherein: in step S6, the entity correlation result is an entity correlation statistical graph obtained by combining all the target entities and the search entity and the similarity between all the target entities and the search entity.
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