CN115292516A - Block chain-based distributed knowledge graph construction method, device and system - Google Patents
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Abstract
The disclosure provides a distributed knowledge graph construction method, device, system, electronic equipment and storage medium based on a block chain, wherein the method comprises the following steps: storing knowledge data provided by a plurality of knowledge data providing systems into a blockchain network, wherein the blockchain network comprises: and each block chain storage node corresponds to one knowledge data providing system, and a distributed knowledge graph is constructed based on the knowledge data stored in the block chain network. According to the method, the distributed knowledge graph is established in a public, transparent and traceable construction environment, authenticity of knowledge data uploaded by each node can be guaranteed, meanwhile, the stored knowledge data can be traced through a block chain network, all operation processes from production to storage can be traced, once the data are changed in the process, the data can be rapidly and accurately identified, and authenticity and stability of the data are further guaranteed.
Description
Technical Field
The present disclosure relates to the field of blockchain technologies, and in particular, to a method, an apparatus, a system, an electronic device, and a storage medium for building a distributed knowledge graph based on blockchain.
Background
With the continuous evolution and rapid development of network technology, the human society is advancing into the "web3.0" era based on knowledge interconnection. The goal of knowledge networking is to build a world wide web that is understood by both humans and machines. However, knowledge interconnection in a distributed big data environment presents a huge challenge due to the multiple sources, heterogeneous data content and loose organizational structure on the world wide web.
The knowledge graph lays a foundation for the knowledge organization and intelligent application of the Internet era by using the strong semantic processing capability and open organizational capability of the knowledge graph, and the goal of the knowledge graph is to use a structured graph to model world knowledge and record the relationship among things in the world.
In the existing knowledge graph construction scheme, data of a knowledge graph is often stored in a database in an importing manner, if a piece of data which is tampered or counterfeited in other manners exists, significant misleading or damage may be caused to the whole query or analysis result, or even if all results are not deviated, if a certain user finds that one piece of data is counterfeited, the authenticity of the whole knowledge graph database is doubtful to all users, and huge loss is brought to an application user of the database.
It is noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure and therefore may include information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The present disclosure provides a method, an apparatus, a system, an electronic device, and a storage medium for building a block chain-based distributed knowledge graph, which at least to some extent overcome the problem in the related art that data is easily tampered in the process of building a knowledge graph.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to one aspect of the disclosure, a block chain-based distributed knowledge graph construction method is provided, which includes: storing knowledge data provided by a plurality of knowledge data providing systems into a blockchain network, wherein the blockchain network comprises: the system comprises a plurality of block chain storage nodes, a plurality of storage nodes and a plurality of data processing units, wherein each block chain storage node corresponds to a knowledge data providing system; and constructing a distributed knowledge graph based on knowledge data stored in the blockchain network.
In one embodiment of the present disclosure, process data generated by the distributed knowledge-graph during a construction process is obtained; and storing the process data generated in the construction process of the distributed knowledge graph into the blockchain network.
In one embodiment of the present disclosure, value quantitative evaluation is performed on knowledge data provided by each knowledge data providing system from a plurality of preset dimensions; and determining the profit information of each knowledge data providing system according to the value quantitative evaluation result of the knowledge data provided by each knowledge data providing system.
In one embodiment of the present disclosure, performing quantitative evaluation of value on knowledge data provided by each knowledge data providing system from a plurality of preset dimensions includes: acquiring weight information corresponding to each preset dimension; obtaining value quantitative evaluation indexes corresponding to all preset dimensions; and performing value quantitative evaluation on the knowledge data provided by each knowledge data providing system according to the weight information and the value quantitative evaluation index corresponding to each preset dimension.
In one embodiment of the present disclosure, before storing knowledge data provided by a plurality of knowledge data providing systems into a blockchain network, the method further comprises: authenticating the identity information of each knowledge data providing system; the storing the knowledge data provided by the plurality of knowledge data providing systems into the blockchain network comprises the following steps: and storing the knowledge data provided by the authenticated one or more knowledge data providing systems into the blockchain network.
In one embodiment of the present disclosure, after storing the knowledge data provided by the plurality of knowledge data providing systems into the blockchain network, the method further includes: performing data preprocessing on knowledge data provided by a plurality of knowledge data providing systems, wherein the data preprocessing comprises entity disambiguation and/or reference resolution.
In one embodiment of the present disclosure, constructing a distributed knowledge graph based on knowledge data stored in the blockchain network includes: uniformly converting the knowledge data into knowledge data in a Resource Description Framework (RDF) format; and constructing a distributed knowledge graph based on knowledge data in an RDF format.
In one embodiment of the present disclosure, the method further comprises: receiving a data acquisition request sent by a knowledge graph consumption system, wherein the data acquisition request is used for requesting to acquire target data in the distributed knowledge graph; in response to the data acquisition request, determining target data corresponding to the data acquisition request from the distributed knowledge graph; returning the target data to the knowledge-graph consumption system.
According to another aspect of the present disclosure, there is provided a block chain-based distributed knowledge graph building apparatus, including: a data storage module, configured to store knowledge data provided by a plurality of knowledge data providing systems into a blockchain network, where the blockchain network includes: the system comprises a plurality of block chain storage nodes, a plurality of storage nodes and a plurality of data processing units, wherein each block chain storage node corresponds to a knowledge data providing system; and the knowledge graph building module is used for building a distributed knowledge graph based on the knowledge data stored in the block chain network.
In an embodiment of the present disclosure, the data storage module is further configured to acquire process data generated by the distributed knowledge graph in a construction process; and storing the process data generated in the construction process of the distributed knowledge graph into the block chain network.
In an embodiment of the present disclosure, the apparatus further includes a value quantitative evaluation module, where the value quantitative evaluation module is configured to perform value quantitative evaluation on the knowledge data provided by each knowledge data providing system from a plurality of preset dimensions; the device also comprises a profit information determining module, wherein the profit information determining module is used for determining profit information of each knowledge data providing system according to the value quantitative evaluation result of the knowledge data provided by each knowledge data providing system.
In an embodiment of the present disclosure, the value quantitative evaluation module is further configured to obtain weight information corresponding to each preset dimension; obtaining value quantitative evaluation indexes corresponding to all preset dimensions; and performing value quantitative evaluation on the knowledge data provided by each knowledge data providing system according to the weight information and the value quantitative evaluation index corresponding to each preset dimension.
In an embodiment of the present disclosure, the apparatus further includes an identity authentication module, configured to authenticate identity information of each knowledge data providing system; the data storage module is further configured to store the knowledge data provided by the authenticated one or more knowledge data providing systems into the blockchain network.
In an embodiment of the present disclosure, the apparatus further includes a data preprocessing module, configured to perform data preprocessing on knowledge data provided by a plurality of knowledge data providing systems, where the data preprocessing includes entity disambiguation and/or reference resolution.
In an embodiment of the present disclosure, the knowledge graph building module is further configured to uniformly convert the knowledge data into knowledge data in an RDF format; and constructing a distributed knowledge graph based on knowledge data in the RDF format.
In an embodiment of the present disclosure, the apparatus further includes a request receiving module, configured to receive a data obtaining request sent by a knowledge graph consumption system, where the data obtaining request is used to request to obtain target data in the distributed knowledge graph; the device further comprises a target data determining module, wherein the target data determining module is used for responding to the data acquisition request and determining target data corresponding to the data acquisition request from the distributed knowledge graph; the device also comprises a target data sending module, and the target data returning module is used for returning the target data to the knowledge graph consumption system.
According to still another aspect of the present disclosure, there is provided a distributed knowledge graph building system based on block chains, including: the system comprises a plurality of knowledge graph providers, a knowledge graph service platform, a block chain network and a knowledge graph consumption system; wherein the knowledge graph provider is used for providing knowledge data; the knowledge graph service platform is used for storing knowledge data provided by a plurality of knowledge data providing systems into a block chain network and constructing a distributed knowledge graph based on the knowledge data stored in the block chain network; the blockchain network is configured to store knowledge data provided by a plurality of knowledge data providing systems, wherein the blockchain network includes: the system comprises a plurality of block chain storage nodes, a plurality of storage nodes and a plurality of data processing units, wherein each block chain storage node corresponds to a knowledge data providing system; the knowledge graph consumption system is used for sending a data acquisition request to the knowledge graph service platform, wherein the data acquisition request is used for requesting to acquire target data in the distributed knowledge graph; the knowledge graph service platform is further used for responding to the data acquisition request, determining target data corresponding to the data acquisition request from the distributed knowledge graph, and returning the target data to the knowledge graph consumption system; the knowledge graph consumption system is also used for receiving the target data returned by the knowledge graph service platform.
According to still another aspect of the present disclosure, there is provided an electronic device including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the above block chain based distributed knowledge graph construction method via execution of the executable instructions.
According to yet another aspect of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the above-mentioned block chain-based distributed knowledge graph construction method.
The embodiment of the disclosure provides a distributed knowledge graph construction method, device, system, electronic device and storage medium based on a block chain, wherein the method comprises the following steps: storing knowledge data provided by a plurality of knowledge data providing systems into a blockchain network, wherein the blockchain network comprises: and each block chain storage node corresponds to one knowledge data providing system, and a distributed knowledge graph is constructed based on the knowledge data stored in the block chain network. According to the method, the distributed knowledge graph is established in a public, transparent and traceable construction environment, authenticity of knowledge data uploaded by each node can be guaranteed, meanwhile, the stored knowledge data can be traced through a block chain network, namely all operation processes from generation to storage can be traced, once the data are changed in the process, the data can be rapidly and accurately identified, and authenticity and stability of the data are further guaranteed.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 is a schematic structural diagram of a distributed knowledge graph building system according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of a distributed knowledge graph building method based on blockchains in an embodiment of the present disclosure;
FIG. 3 is a flow chart of another block chain-based distributed knowledge graph building method according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating a block chain-based distributed knowledge graph building method according to an embodiment of the present disclosure;
FIG. 5 is a flow chart of another block chain-based distributed knowledge graph building method according to an embodiment of the present disclosure;
FIG. 6 is a diagram illustrating a quantitative assessment of value in accordance with an embodiment of the present disclosure;
FIG. 7 is a flow chart of another block chain-based distributed knowledge graph building method according to an embodiment of the present disclosure;
FIG. 8 is a flow diagram illustrating another method for building a distributed knowledge graph based on blockchains in accordance with an embodiment of the present disclosure;
FIG. 9 is a flow chart of another block chain-based distributed knowledge graph building method according to an embodiment of the present disclosure;
FIG. 10 is a schematic diagram illustrating a block chain-based distributed knowledge graph building apparatus according to an embodiment of the present disclosure;
FIG. 11 is a block chain-based distributed knowledge graph building system according to an embodiment of the present disclosure; and
fig. 12 shows a block diagram of an electronic device in an embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
As mentioned in the background art, since the internet is an open and decentralized structure, the construction of a multi-party distributed knowledge graph becomes the mainstream of current research, but many problems and challenges still exist, such as the establishment of a publicly transparent and traceable distributed knowledge graph construction environment to meet the requirements of distributed knowledge recording, storage and updating; realizing quantification and excitation of multi-party knowledge contribution, and the like.
The block chain technology is a technical scheme which does not depend on a third party and carries out storage, verification, transmission and communication of network data through self distributed nodes, and has the characteristics of decentralization, no tampering, traceability, transparency, no need of third party endorsement and the like. Therefore, a decentralized distributed knowledge graph construction system can be realized by using a block chain technology, and the problems of multi-party collaboration and trust are solved. Meanwhile, the chain linking storage can be carried out in the construction process of the whole knowledge graph, and the safety compliance and traceability of the process are guaranteed.
Based on the above, the distributed knowledge graph construction method, device, system, electronic device and storage medium based on the block chain are provided, the distributed knowledge graph is established in a publicly transparent and traceable construction environment, authenticity of knowledge data uploaded by each node can be guaranteed, meanwhile, the stored knowledge data can be traced through the block chain network, namely all operation processes from generation to storage can be traced, once the data is changed in the process, the data can be quickly and accurately identified, and authenticity and stability of the data are further guaranteed.
FIG. 1 shows a schematic structural diagram of a distributed knowledge graph building system that can be applied to embodiments of the present disclosure.
As shown in FIG. 1, the functional components of the system may be divided into four layers, including an infrastructure layer 110, a middleware layer 120, an access layer 130, and an application layer 140.
The infrastructure layer 110 includes a data receiving unit, a data storage unit, and a block chain network, and provides a basis for constructing a distributed knowledge graph:
the data receiving unit is used for receiving the knowledge data uploaded by a plurality of knowledge data providing systems;
the data storage unit is used for uniformly and normally storing the knowledge data provided by the knowledge data providing system into the block chain network;
block chain network: the system is used for storing knowledge data provided by a plurality of knowledge data providing systems and providing identity authentication, consensus and evidence storage traceability functions of knowledge data providing systems/knowledge map consumers;
the middleware layer 120 is configured to construct knowledge data of multiple knowledge data providing systems into a distributed knowledge graph, and mainly includes some conventional knowledge graph constructing technologies, such as knowledge fusion, computation, processing, and the like.
The knowledge fusion unit is used for carrying out knowledge fusion on knowledge data provided by the multi-party knowledge data providing system, and comprises entity disambiguation, reference resolution and the like;
the knowledge calculation unit is used for linking and predicting the relation between different entities in the distributed knowledge graph to realize simple reasoning of the knowledge graph;
the value quantification unit is used for evaluating the quality of the constructed distributed knowledge graph, and comprises quantification indexes such as the accuracy and the coverage rate of the distributed knowledge graph;
the access layer 130 is configured to provide a unified API (Application Programming Interface) access Interface to the outside.
The application layer 140 is used for interfacing with a knowledge graph consumption system, and can provide a variety of knowledge graph applications such as knowledge question and answer, semantic search, root cause analysis, and personalized recommendation.
The present exemplary embodiment will be described in detail below with reference to the drawings and examples.
First, a block chain-based distributed knowledge graph construction method is provided in the embodiments of the present disclosure, and the method may be executed by any electronic device with computing processing capability.
Fig. 2 is a flowchart illustrating a block chain-based distributed knowledge graph building method in an embodiment of the present disclosure, and as shown in fig. 1, the block chain-based distributed knowledge graph building method provided in the embodiment of the present disclosure includes the following steps:
s202, storing the knowledge data provided by the knowledge data providing systems into a blockchain network, wherein the blockchain network includes: the system comprises a plurality of block chain storage nodes, a plurality of data acquisition units and a plurality of data transmission units, wherein each block chain storage node corresponds to a knowledge data providing system;
it should be noted that the knowledge data may be data relating to various technical fields, the technical fields may be fields of electronics, power, communication, computers, automation, medical drugs, chemical engineering, materials, and the like, the knowledge data may also be triple knowledge data, the format of the triple knowledge data may be [ resource, attribute value ], and after receiving the knowledge data uploaded by a plurality of knowledge data providing systems, the triple knowledge data are uniformly and normatively stored in the block chain network. Here, the knowledge data providing system serves as a knowledge data provider for providing knowledge data required for constructing the distributed knowledge graph, and each blockchain storage node corresponds to one knowledge data providing system, which may be that each knowledge data providing system is a blockchain storage node, or that each knowledge data providing system connects one blockchain storage node in a blockchain network through a blockchain client.
S204, constructing a distributed knowledge graph based on knowledge data stored in the block chain network.
It should be noted that the construction of the distributed knowledge graph requires that a plurality of knowledge data providing systems participate and cooperate together, and a plurality of knowledge data stored in the block chain network can be imported into the Neo4j graph database to construct the distributed knowledge graph. Before importing a plurality of knowledge data into a Neo4j graph database, deduplication processing can be further performed on information in the knowledge data, the Neo4j graph database stores entities and relations respectively by adopting nodes and edges, the nodes are used for representing entity objects, the edges are directed lines connecting the nodes in a graph and used for representing relations among different nodes, the nodes correspond to attribute values, the nodes are associated with the nodes through the edges, and the attributes are used for describing characteristics of the nodes or the edges.
The method for constructing a distributed knowledge graph based on a blockchain provided by the embodiment of the disclosure stores knowledge data provided by a plurality of knowledge data providing systems into a blockchain network, wherein the blockchain network comprises: and each block chain storage node corresponds to one knowledge data providing system, and a distributed knowledge graph is constructed based on the knowledge data stored in the block chain network. According to the method, the distributed knowledge graph is established in a public, transparent and traceable construction environment, authenticity of knowledge data uploaded by each node can be guaranteed, meanwhile, the stored knowledge data can be traced through a block chain network, namely all operation processes from generation to storage can be traced, once the data are changed in the process, the data can be rapidly and accurately identified, and authenticity and stability of the data are further guaranteed.
In an embodiment of the present disclosure, the method may further include the steps disclosed in fig. 3, referring to a flowchart of another block chain-based distributed knowledge graph building method shown in fig. 3, and the method may include the following steps:
s302, acquiring process data generated in the construction process of the distributed knowledge graph;
s304, storing the process data generated in the construction process of the distributed knowledge graph in the block chain network.
It should be noted that, by using the distributed account book technology of the block chain, the generation and expansion of the distributed knowledge graph and the full life cycle of the application are uplink stored, and based on the characteristic that the block chain has traceability, the process data generated in the distributed knowledge graph building process is uplink certified, so as to ensure that the building process is traceable and auditable, for example, each new block generated by the node should include the block information and the knowledge data stored in the block, the block information should at least include the identifier of the last block to be linked, the identifier of the block, and the traceable characteristic values and the traceable relationships between the characteristic values of all blocks to be linked, the identifier of the last block is a numerical value and/or a text for indicating the identity and/or the characteristic of the last block, in the specific implementation, the name and/or the hash value of the last block can be used, and in the specific implementation, the name and/or the hash value of the block can be used.
The traceable characteristic value refers to a value for indicating the independent identity and/or characteristic of all the linked blocks before the new block, and the traceable relationship between the characteristic values refers to a logic and/or link relationship for indicating the development of the characteristic values according to time and/or link sequence.
Referring to fig. 4, a schematic diagram of a block chain-based distributed knowledge graph construction method may be shown, and the application of the distributed knowledge graph may include intelligent question and answer, semantic search, decision analysis, and the like of a knowledge graph consumption system based on the distributed knowledge graph.
In an embodiment of the present disclosure, the method may further include the steps disclosed in fig. 5, referring to a flowchart of another block chain-based distributed knowledge graph building method shown in fig. 5, and the method may include the following steps:
s502, performing value quantitative evaluation on knowledge data provided by each knowledge data providing system from a plurality of preset dimensions;
s504, determining the income information of each knowledge data providing system according to the value quantitative evaluation result of the knowledge data provided by each knowledge data providing system.
It should be noted that the preset dimensions may be a cost dimension, a quality dimension, an application dimension, and the like of the knowledge data, and refer to a value quantitative evaluation schematic diagram shown in fig. 6, where the cost dimension refers to a construction cost, an operation and maintenance cost, and a management cost of constructing the knowledge data of the knowledge data providing system, and assuming that the value of the knowledge data is in direct proportion to the cost, that is, the higher the cost, the higher the value of the knowledge is, the kg of the ith knowledge data providing system can be calculated by the following formula i Cost of supplied knowledge data cost(kg i )：
cost(kg i )＝cost c (kg i )+cost o (kg i )+cost a (kg i ) (1)
Wherein, cost c (kg i ) Provide the system kg for the ith knowledge data i Cost of construction of the knowledge data provided, cost o (kg i ) System kg is provided for ith knowledge data i Cost of operation and maintenance of the provided knowledge data, cost a (kg i ) Provide the system kg for the ith knowledge data i Management cost of the provided knowledge data.
The quality dimension refers to the knowledge range covered by the knowledge data provided by the knowledge data providing system and the accuracy of expression, the more entities and relations contained in the distributed knowledge graph, the greater the knowledge coverage rate, but the accuracy rate gradually decreases with the enlargement of the scale of the distributed knowledge graph, and the kg of the ith knowledge data providing system can be calculated through the following formula i The quality of the provided knowledge data;
quality(kg i )＝scale(kg i )*accuracy(kg i ) (2)
wherein, scale (kg) i ) System kg is provided for ith knowledge data i Coverage of the knowledge data provided, accuracy (kg) i ) System kg is provided for ith knowledge data i Accuracy of the provided knowledge data.
The application dimension refers to the activity and the multidimensional property of the knowledge data, the activity represents the frequency of acquiring or using the knowledge data in unit time, and the multidimensional property represents the number of applications which can be applied by the knowledge data; assuming that knowledge data can be applied to m applications, the average frame rate of access or use is used to represent the value of the application dimension, and thus the application dimension over its given time period t can be represented by the following formula:
wherein app (kg) i ) System kg is provided for ith knowledge data i An application value of the supplied knowledge data, m is the number of applications to which the knowledge data can be applied, activy i (kg i ) The frequency used for a given time period t for the knowledge data.
According to the weight information and the value quantitative evaluation index corresponding to each preset dimension, the value quantitative evaluation is carried out on the knowledge data provided by each knowledge data providing system, and the value quantitative evaluation result of the knowledge data can be obtained through the following formula:
Value(kg i )＝α*cost(kg i )+β*quality(kg i )+γ*app(kg i ) (4)
α+β+γ＝1 (5)
wherein α, β, γ respectively represent weights corresponding to the cost dimension, the quality dimension, and the application dimension, and when setting the weights, the sum of the weights corresponding to the cost dimension, the quality dimension, and the application dimension may be set to 1, and a value quantitative evaluation result of a distributed knowledge graph formed by knowledge data provided by n knowledge data providing systems may be calculated by the following formula:
wherein Value (KG) represents a Value quantitative evaluation result of the distributed knowledge graph, and n is n knowledge data providing systems.
Assuming a distributed knowledge graph composed of knowledge data provided by n knowledge data providing systems, weights of a cost dimension, a quality dimension and an application dimension can be set according to actual requirements.
Based on the mode, the value quantitative evaluation can be performed on the knowledge data provided by each knowledge data providing system from a plurality of preset dimensions to obtain the value quantitative evaluation result of the knowledge data, and the value quantitative evaluation result of the distributed knowledge graph can be calculated by comprehensively considering the value quantitative evaluation results of the knowledge data.
Distributed knowledge graph provides various applications by externalThe benefits of the distributed knowledge graph can be expressed by R (KG), each knowledge data providing system providing knowledge data can obtain a part of the benefits of the knowledge graph to stimulate the knowledge sharing enthusiasm of the knowledge data providing system, and the KG of the ith knowledge data providing system can be calculated through the following formula i The profit information of (2):
wherein, R (KP) i ) Provide the system kg for the ith knowledge data i The revenue information of (1).
The incentive mechanism aims to fully mobilize the enthusiasm of the knowledge data providing system and achieve benefit maximization, the benefit of each knowledge data providing system depends on the contribution of the provided knowledge data to the knowledge graph, and the greater the contribution, the higher the benefit; the incentive scheme can realize automatic distribution of benefits through the intelligent contracts so as to reduce the complexity of benefit distribution, recorded transactions are irreversible and cannot be tampered, each transaction can trace the source layer by layer, whether the transactions are in compliance or not is judged, and the problems of rights of knowledge assets and economic disputes are solved.
In an embodiment of the present disclosure, performing quantitative evaluation on knowledge data provided by each knowledge data providing system from multiple preset dimensions may be implemented by the steps disclosed in fig. 7, and referring to a flowchart of another block chain-based distributed knowledge graph building method shown in fig. 7, the method may include the following steps:
s702, acquiring weight information corresponding to each preset dimension;
s704, obtaining value quantitative evaluation indexes corresponding to all preset dimensions;
and S706, performing value quantitative evaluation on the knowledge data provided by each knowledge data providing system according to the weight information and the value quantitative evaluation index corresponding to each preset dimension.
It should be noted that the preset dimension may be a cost dimension, a quality dimension, an application dimension, and the like, the sum of the weight information corresponding to all the preset dimensions may be set to 1, and the sum of the product of the weight corresponding to all the preset dimensions and the value quantitative evaluation index is used as the value quantitative evaluation result.
In one embodiment of the present disclosure, before storing the knowledge data provided by the plurality of knowledge data providing systems in the blockchain network, the method further comprises: authenticating the identity information of each knowledge data providing system; storing knowledge data provided by a plurality of knowledge data providing systems into a blockchain network, comprising: and storing the knowledge data provided by the authenticated knowledge data providing system or systems into the blockchain network.
It should be noted that, the process of authenticating the identity information of each knowledge data providing system may be: receiving identity information and public key information uploaded by a knowledge data providing system, verifying whether the knowledge data providing system is registered according to the identity information and the public key information uploaded by the knowledge data providing system, and if so, determining that the knowledge data providing system passes authentication; the identity information of the credible knowledge data providing system can be stored in the block chain network, whether the identity information of the knowledge data providing system is stored in the block chain network or not is judged by matching the identity information uploaded by the knowledge data providing system with the identity information stored in the block chain network, and if yes, the knowledge data providing system is determined to pass the authentication.
In one embodiment of the present disclosure, after storing the knowledge data provided by the plurality of knowledge data providing systems into the blockchain network, the method further comprises: and performing data preprocessing on knowledge data provided by a plurality of knowledge data providing systems, wherein the data preprocessing comprises entity disambiguation and/or reference resolution.
It should be noted that entity disambiguation refers to resolving to identify entities in a plurality of knowledge data that refer to the same object,
the entity disambiguation system may perform entity disambiguation on knowledge data provided by a plurality of knowledge data providing systems based on clustering or entity linking, wherein the clustering based entity system disambiguates entity referents in a clustering manner if a target entity list is not given. All the designated items pointing to the same target entity are clustered to the same category by the disambiguation system, and each category in the clustering result corresponds to one target entity. The method comprises the steps of firstly obtaining an entity reference set to be disambiguated from a plurality of knowledge data, extracting the characteristics of each entity reference item in the entity reference set to be disambiguated, representing the characteristics as a characteristic vector, calculating the similarity between the entity reference items, and finally clustering the entity reference items by adopting a clustering algorithm so that each category in a clustering result corresponds to a target entity.
The entity disambiguation system based on entity link is used for realizing disambiguation by linking an entity nominal item with a corresponding entity in a target entity list under the condition that the target entity list is given, and comprises two steps of link candidate filtering and entity linking, wherein the link candidate filtering is used for filtering entities which most nominal items in a plurality of knowledge data cannot point to according to rules or knowledge, only a small number of link entity candidates are reserved, the entity linking is the given nominal item and the link candidate thereof, and the target entity which the entity nominal item finally points to is determined.
The knowledge data provided by the knowledge data providing systems can be subjected to reference resolution through a clustering reference resolution algorithm, specifically, an entity Mention with reference object meaning in the knowledge data can be obtained first, and whether two mentions refer to the same object or not can be judged by using a feature vector of the Mention.
In an embodiment of the present disclosure, the step disclosed in fig. 8 may be implemented to construct a distributed knowledge graph based on knowledge data stored in a blockchain network, where reference is made to a flowchart of another method for constructing a blockchain-based distributed knowledge graph shown in fig. 8, where the method may include:
s802, uniformly converting the knowledge data into the knowledge data in the resource description framework RDF format;
s804, constructing a distributed knowledge graph based on knowledge data in an RDF format.
It should be noted that the base structure of the RDF format is a triple (triple) including a resource (subject) -attribute (predicate) -attribute value (object), which is also called a declaration (statement), where the attribute value may also be a resource or a literal (literal), and the literal can only be an atomic value, such as: numbers, dates, etc.; an attribute describes a relationship between a resource and an attribute value. RDF is an XML (subset of standard universal markup language) application that handles metadata, i.e. "data describing data" or "information describing information". The resource and attribute values in the knowledge data in the RDF format can be a question and an answer respectively, and a distributed knowledge graph can be constructed according to the knowledge data in the plurality of RDF formats.
In an embodiment of the present disclosure, the method may further include the steps disclosed in fig. 9, referring to a flowchart of another block chain-based distributed knowledge graph building method shown in fig. 9, where the method may include:
s902, receiving a data acquisition request sent by the knowledge graph consumption system, wherein the data acquisition request is used for requesting to acquire target data in the distributed knowledge graph;
s904, responding to the data acquisition request, and determining target data corresponding to the data acquisition request from the distributed knowledge graph;
and S906, returning the target data to the knowledge graph consumption system.
It should be noted that the data acquisition request may include one of the resource or attribute values in the triple, and after the data acquisition request is received, the target data including the corresponding resource or attribute value is determined according to the relationship between the resource or attribute value in the data acquisition request and the resource and attribute value described by the attribute, and the target data is returned to the knowledge graph consumption system. In addition, the knowledge graph consumption system can acquire the required knowledge data in a searching or question-and-answer mode, and knowledge consumption is the most direct mode for measuring the knowledge value. The more knowledge data in the distributed knowledge graph is acquired by the knowledge graph consumption system, the larger the numerical value of the value evaluation result of the distributed knowledge graph.
Based on the same inventive concept, the embodiment of the present disclosure further provides a device for building a distributed knowledge graph based on a block chain, as in the following embodiments. Because the principle of the embodiment of the apparatus for solving the problem is similar to that of the embodiment of the method, the embodiment of the apparatus can be implemented by referring to the implementation of the embodiment of the method, and repeated details are not described again.
Fig. 10 is a schematic diagram illustrating an apparatus for building a distributed knowledge graph based on a blockchain according to an embodiment of the present disclosure, as shown in fig. 10, the apparatus includes:
a data storage module 1010, configured to store knowledge data provided by a plurality of knowledge data providing systems into a blockchain network, where the blockchain network includes: the system comprises a plurality of block chain storage nodes, a plurality of storage nodes and a plurality of data processing units, wherein each block chain storage node corresponds to a knowledge data providing system;
and a knowledge graph constructing module 1020 for constructing a distributed knowledge graph based on the knowledge data stored in the blockchain network.
In an embodiment of the present disclosure, the data storage module 1010 is further configured to acquire process data generated in a construction process of the distributed knowledge graph; and storing the process data generated in the construction process of the distributed knowledge graph into the block chain network.
In an embodiment of the present disclosure, the apparatus further includes a value quantitative evaluation module, where the value quantitative evaluation module is configured to perform value quantitative evaluation on the knowledge data provided by each knowledge data providing system from a plurality of preset dimensions; the device also comprises a profit information determining module, wherein the profit information determining module is used for determining profit information of each knowledge data providing system according to the value quantitative evaluation result of the knowledge data provided by each knowledge data providing system.
In an embodiment of the present disclosure, the value quantitative evaluation module is further configured to obtain weight information corresponding to each preset dimension; obtaining value quantitative evaluation indexes corresponding to all preset dimensions; and performing value quantitative evaluation on the knowledge data provided by each knowledge data providing system according to the weight information and the value quantitative evaluation index corresponding to each preset dimension.
In an embodiment of the present disclosure, the apparatus further includes an identity authentication module, configured to authenticate identity information of each knowledge data providing system; the data storage module is further configured to store the knowledge data provided by the authenticated one or more knowledge data providing systems into the blockchain network.
In one embodiment of the present disclosure, the apparatus further includes a data preprocessing module, which is configured to perform data preprocessing on knowledge data provided by the knowledge data providing systems, where the data preprocessing includes entity disambiguation and/or reference resolution.
In an embodiment of the present disclosure, the knowledge graph building module 1020 is further configured to uniformly convert the knowledge data into knowledge data in an RDF format; and constructing a distributed knowledge graph based on knowledge data in an RDF format.
In an embodiment of the present disclosure, the apparatus further includes a request receiving module, configured to receive a data obtaining request sent by the knowledge-graph consumption system, where the data obtaining request is used to request to obtain target data in the distributed knowledge graph; the device also comprises a target data determining module, wherein the target data determining module is used for responding to the data acquisition request and determining target data corresponding to the data acquisition request from the distributed knowledge graph; the device also comprises a target data sending module, and the target data returning module is used for returning the target data to the knowledge graph consumption system.
Based on the same inventive concept, the embodiment of the present disclosure further provides a distributed knowledge graph building system based on a block chain, as in the following embodiments. Because the principle of solving the problem of the system embodiment is similar to that of the method embodiment, reference may be made to the implementation of the method embodiment for implementation of the system embodiment, and repeated descriptions are omitted.
Fig. 11 is a schematic diagram illustrating a block chain-based distributed knowledge graph building system according to an embodiment of the present disclosure, where as shown in fig. 11, the system includes: a plurality of knowledge-graph providers 1110, knowledge-graph service platforms 1120, blockchain networks 1130, and knowledge-graph consumption systems 1140;
the knowledge graph provider 1110 is configured to provide knowledge data;
the knowledge graph service platform 1120 is used for storing knowledge data provided by a plurality of knowledge data providing systems into a block chain network and constructing a distributed knowledge graph based on the knowledge data stored in the block chain network;
a blockchain network 1130 for storing knowledge data provided by a plurality of knowledge data providing systems, wherein the blockchain network includes: the system comprises a plurality of block chain storage nodes, a plurality of data acquisition units and a plurality of data transmission units, wherein each block chain storage node corresponds to a knowledge data providing system;
the knowledge graph consumption system 1140 is configured to send a data acquisition request to the knowledge graph service platform, where the data acquisition request is used to request to acquire target data in the distributed knowledge graph;
the knowledge graph service platform 1120 is further used for responding to the data acquisition request, determining target data corresponding to the data acquisition request from the distributed knowledge graph, and returning the target data to the knowledge graph consumption system;
the knowledge-graph consumption system 1140 is further configured to receive target data returned by the knowledge-graph service platform.
As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 1200 according to this embodiment of the disclosure is described below with reference to fig. 12. The electronic device 1200 shown in fig. 12 is only an example and should not bring any limitation to the function and the scope of use of the embodiments of the present disclosure.
As shown in fig. 12, the electronic device 1200 is embodied in the form of a general purpose computing device. The components of the electronic device 1200 may include, but are not limited to: the at least one processing unit 1210, the at least one memory unit 1220, and a bus 1230 connecting various system components including the memory unit 1220 and the processing unit 1210.
Where the memory unit stores program code, the program code may be executed by the processing unit 1210 such that the processing unit 1210 performs the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned "exemplary methods" section of this specification. For example, the processing unit 1210 may perform the following steps of the above-described method embodiments: storing knowledge data provided by a plurality of knowledge data providing systems into a blockchain network, wherein the blockchain network comprises: the system comprises a plurality of block chain storage nodes, a plurality of storage nodes and a plurality of data processing units, wherein each block chain storage node corresponds to a knowledge data providing system; and constructing a distributed knowledge graph based on knowledge data stored in the block chain network.
The storage unit 1220 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM) 12201 and/or a cache memory unit 12202, and may further include a read only memory unit (ROM) 12203.
The electronic device 1200 may also communicate with one or more second devices 1240 (e.g., keyboard, pointing device, bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 1200, and/or may communicate with any devices (e.g., router, modem, etc.) that enable the electronic device 1200 to communicate with one or more other computing devices. Such communication may occur over input/output (I/O) interfaces 1250. Also, the electronic device 1200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 1260. As shown, the network adapter 1260 communicates with the other modules of the electronic device 1200 via the bus 1230. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 1200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, to name a few.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, and may also be implemented by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium, which may be a readable signal medium or a readable storage medium. On which a program product capable of implementing the above-described method of the present disclosure is stored. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the disclosure described in the "exemplary methods" section above of this specification, when the program product is run on the terminal device.
More specific examples of the computer-readable storage medium in the present disclosure may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In the present disclosure, a computer readable storage medium may include a propagated data signal with readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Alternatively, program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
In particular implementations, program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to external computing devices (e.g., through the internet using an internet service provider).
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the description of the above embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, and may also be implemented by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (12)
1. A distributed knowledge graph construction method based on a block chain is characterized by comprising the following steps:
storing knowledge data provided by a plurality of knowledge data providing systems into a blockchain network, wherein the blockchain network comprises: the system comprises a plurality of block chain storage nodes, a plurality of storage nodes and a plurality of data processing units, wherein each block chain storage node corresponds to a knowledge data providing system;
and constructing a distributed knowledge graph based on knowledge data stored in the blockchain network.
2. The method of building a blockchain-based distributed knowledge graph according to claim 1, further comprising:
acquiring process data generated in the construction process of the distributed knowledge graph;
and storing the process data generated in the construction process of the distributed knowledge graph into the block chain network.
3. The method of building a blockchain-based distributed knowledge graph according to claim 1, further comprising:
performing value quantitative evaluation on knowledge data provided by each knowledge data providing system from a plurality of preset dimensions;
and determining the income information of each knowledge data providing system according to the value quantitative evaluation result of the knowledge data provided by each knowledge data providing system.
4. The method for building the distributed knowledge graph based on the block chains according to claim 3, wherein the quantitative evaluation of the value of the knowledge data provided by each knowledge data providing system is performed from a plurality of preset dimensions, and comprises the following steps:
acquiring weight information corresponding to each preset dimension;
obtaining value quantitative evaluation indexes corresponding to all preset dimensions;
and performing value quantitative evaluation on the knowledge data provided by each knowledge data providing system according to the weight information and the value quantitative evaluation index corresponding to each preset dimension.
5. The blockchain-based distributed knowledge graph building method according to claim 1, wherein before storing the knowledge data provided by the plurality of knowledge data providing systems into the blockchain network, the method further comprises:
authenticating the identity information of each knowledge data providing system;
the storing the knowledge data provided by the plurality of knowledge data providing systems into the blockchain network comprises the following steps: and storing the knowledge data provided by the authenticated knowledge data providing system or systems into the blockchain network.
6. The blockchain-based distributed knowledge graph building method according to claim 1, wherein after storing knowledge data provided by a plurality of knowledge data providing systems into a blockchain network, the method further comprises:
performing data preprocessing on knowledge data provided by a plurality of knowledge data providing systems, wherein the data preprocessing comprises entity disambiguation and/or reference resolution.
7. The method for building the blockchain-based distributed knowledge graph according to claim 1, wherein building the distributed knowledge graph based on knowledge data stored in the blockchain network comprises:
uniformly converting the knowledge data into knowledge data in a Resource Description Framework (RDF) format;
and constructing a distributed knowledge graph based on knowledge data in the RDF format.
8. The method of building a blockchain-based distributed knowledge graph according to claim 1, further comprising:
receiving a data acquisition request sent by a knowledge graph consumption system, wherein the data acquisition request is used for requesting to acquire target data in the distributed knowledge graph;
in response to the data acquisition request, determining target data corresponding to the data acquisition request from the distributed knowledge graph;
returning the target data to the knowledge-graph consumption system.
9. A distributed knowledge graph building device based on block chains is characterized by comprising the following components:
a data storage module, configured to store knowledge data provided by a plurality of knowledge data providing systems into a blockchain network, where the blockchain network includes: the system comprises a plurality of block chain storage nodes, a plurality of storage nodes and a plurality of data processing units, wherein each block chain storage node corresponds to a knowledge data providing system;
and the knowledge graph building module is used for building a distributed knowledge graph based on the knowledge data stored in the block chain network.
10. A distributed knowledge graph building system based on a block chain is characterized by comprising: the system comprises a plurality of knowledge graph providers, a knowledge graph service platform, a block chain network and a knowledge graph consumption system;
wherein the knowledge graph provider is used for providing knowledge data;
the knowledge graph service platform is used for storing knowledge data provided by a plurality of knowledge data providing systems into a block chain network and constructing a distributed knowledge graph based on the knowledge data stored in the block chain network;
the blockchain network is configured to store knowledge data provided by a plurality of knowledge data providing systems, wherein the blockchain network includes: the system comprises a plurality of block chain storage nodes, a plurality of storage nodes and a plurality of data processing units, wherein each block chain storage node corresponds to a knowledge data providing system;
the knowledge graph consumption system is used for sending a data acquisition request to the knowledge graph service platform, wherein the data acquisition request is used for requesting to acquire target data in the distributed knowledge graph;
the knowledge graph service platform is further used for responding to the data acquisition request, determining target data corresponding to the data acquisition request from the distributed knowledge graph, and returning the target data to the knowledge graph consumption system;
the knowledge graph consumption system is further used for receiving the target data returned by the knowledge graph service platform.
11. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the block chain-based distributed knowledge graph construction method of any one of claims 1 to 8 via execution of the executable instructions.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the block chain-based distributed knowledge graph construction method according to any one of claims 1 to 8.
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