CN115391545A - Knowledge graph construction method and device for multi-platform collaborative observation task - Google Patents
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Abstract
The invention provides a knowledge graph construction method and a device for a multi-platform collaborative observation task, which relate to the technical field of data processing and comprise the following steps: determining an entity corresponding to a knowledge graph of the cooperative observation task, and acquiring knowledge of the cooperative observation task based on the field and range to which the entity belongs, wherein the entity comprises: the remote sensing platform corresponding to the collaborative observation task, the sensor corresponding to the remote sensing platform, the related event of the collaborative observation task and the ground object target of the collaborative observation task; determining characteristic information between the entities based on the entities and knowledge, wherein the characteristic information comprises: intrinsic characteristics, extrinsic characteristics, components, and spatiotemporal relationships; converting the characteristic information into triples, and constructing an ontology base based on the triples; constructing an entity alignment model of the semantic knowledge graph and an entity alignment model of the structural knowledge graph based on the ontology base; the knowledge graph is constructed based on the entity alignment model of the semantic knowledge graph and the entity alignment model of the structural knowledge graph, and the technical problem that the intellectualization and automation level of multi-platform collaborative observation task management and resource scheduling in the prior art is low is solved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a knowledge graph construction method and device for a multi-platform collaborative observation task.
Background
The space-based earth observation information acquisition means is developed rapidly, and the resolution and the real-time performance are improved. In addition, the observation technology of the near space platform is gradually mature, and compared with a satellite platform and an aviation platform, the near space platform has the advantages of high precision, continuous data and the like. At present, the management and control capabilities of platform observation tasks such as space bases, adjacent spaces and the like are relatively backward, and the automation and intelligence levels are not high. The management control capability of the platform observation task needs to match the observation requirements. The conventional task planning method mainly aims at a single type of observation platform, and does not consider the cooperative demand of simultaneously developing observation tasks by a multi-level platform in the sky, the sky and the sky. Meanwhile, when the observation platform task analyst carries out the relevant work of demand planning, long-term knowledge accumulation and continuous knowledge updating are needed. Converting personnel experience and multi-platform historical experience tasks into digital and intelligent information means is also an important challenge, and important consideration needs to be given: converting unstructured text information related to a historical observation task into structured knowledge; the space-to-air multi-platform resources have respective observation advantages, and when target observation is carried out together, problems such as task allocation and planning need to be carried out according to the characteristics of the platforms.
No effective solution has been proposed to the above problems.
Disclosure of Invention
In view of this, the present invention aims to provide a knowledge graph construction method and apparatus for a multi-platform collaborative observation task, so as to alleviate the technical problem in the prior art that the intellectualization and automation level of multi-platform collaborative observation task management and resource scheduling are low.
In a first aspect, an embodiment of the present invention provides a knowledge graph construction method for a multi-platform collaborative observation task, including: determining an entity corresponding to a knowledge graph of a collaborative observation task, and acquiring knowledge of the collaborative observation task based on the field and range of the entity, wherein the entity comprises: the remote sensing platform corresponding to the collaborative observation task, the sensor corresponding to the remote sensing platform, the related event of the collaborative observation task and the ground object target of the collaborative observation task; determining feature information between the entities based on the entities and the knowledge, wherein the feature information comprises: intrinsic characteristics, extrinsic characteristics, components, and spatiotemporal relationships; converting the characteristic information into a triple, and constructing an ontology base based on the triple; based on the ontology base, constructing an entity alignment model of a semantic knowledge graph and an entity alignment model of a structural knowledge graph; and constructing the knowledge graph based on the entity alignment model of the semantic knowledge graph and the entity alignment model of the structural knowledge graph.
Further, the remote sensing platform comprises: the remote sensing system comprises a space-based remote sensing platform, a space-based remote sensing platform and a near space remote sensing platform; the knowledge includes: resource information of the remote sensing platform, attribute data of the sensor, different scene event data of the collaborative observation task, related application of the collaborative observation task, prior knowledge of the collaborative observation task and historical collaborative observation task; wherein the attribute data of the sensor comprises: sensor parameters, imaging mechanism, timeliness and coverage area.
Further, determining feature information between the entities based on the entities and the knowledge, comprising: performing entity extraction on the knowledge based on the entity and the knowledge to obtain a target entity; extracting the relation of the knowledge to obtain a relation set between the entities; and extracting the attributes of the knowledge based on the target entities and the relation set to obtain the characteristic information between the entities.
Further, based on the ontology base, an entity alignment model of the semantic knowledge graph and an entity alignment model of the structural knowledge graph are constructed, and the method comprises the following steps: classifying the knowledge based on feature information between entities in the ontology library to obtain semantic knowledge features and structural knowledge features; building an entity alignment model of the semantic knowledge graph based on the semantic knowledge features; and constructing an entity alignment model of the structural knowledge graph based on the structural knowledge characteristics.
Further, constructing the knowledge-graph based on the entity alignment model of the semantic graph and the entity alignment model of the structural knowledge-graph, including: performing iterative training on the entity alignment model of the semantic graph and the entity alignment model of the structural knowledge graph to obtain a sub-knowledge graph; and constructing the knowledge graph based on the sub knowledge graph.
Further, the method further comprises: and updating the knowledge graph based on a forward rule matching algorithm.
In a second aspect, an embodiment of the present invention provides a knowledge graph constructing apparatus for a multi-platform collaborative observation task, including: the system comprises a first determining unit, a second determining unit, a converting unit, a first constructing unit and a second constructing unit, wherein the first determining unit is used for determining an entity corresponding to a knowledge graph of a collaborative observation task and acquiring knowledge of the collaborative observation task based on the domain and range of the entity, and the entity comprises: the remote sensing platform corresponding to the collaborative observation task, the sensor corresponding to the remote sensing platform, the related event of the collaborative observation task and the ground object target of the collaborative observation task; the second determining unit is configured to determine feature information between the entities based on the entities and the knowledge, where the feature information includes: intrinsic characteristics, extrinsic characteristics, components, and spatiotemporal relationships; the conversion unit is used for converting the characteristic information into triples and constructing an ontology library based on the triples; the first construction unit is used for constructing an entity alignment model of a semantic knowledge graph and an entity alignment model of a structural knowledge graph based on the ontology base; the second construction unit is used for constructing the knowledge graph based on the entity alignment model of the semantic knowledge graph and the entity alignment model of the structural knowledge graph. .
Further, the remote sensing platform includes: the system comprises a space-based remote sensing platform, an air-based remote sensing platform and a near space remote sensing platform; the knowledge includes: resource information of the remote sensing platform, attribute data of the sensor, different scene event data of the collaborative observation task, related application of the collaborative observation task, prior knowledge of the collaborative observation task and historical collaborative observation task; wherein the attribute data of the sensor comprises: sensor parameters, imaging mechanism, timeliness and coverage area.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory and a processor, where the memory is used to store a program that supports the processor to execute the method in the first aspect, and the processor is configured to execute the program stored in the memory.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium.
In the embodiment of the invention, an entity corresponding to a knowledge graph of a collaborative observation task is determined, and knowledge of the collaborative observation task is acquired based on the field and range of the entity, wherein the entity comprises: the remote sensing platform corresponding to the collaborative observation task, the sensor corresponding to the remote sensing platform, the related event of the collaborative observation task and the ground object target of the collaborative observation task; determining feature information between the entities based on the entities and the knowledge, wherein the feature information comprises: intrinsic characteristics, extrinsic characteristics, components, and spatiotemporal relationships; converting the characteristic information into a triple, and constructing an ontology base based on the triple; based on the ontology base, constructing an entity alignment model of a semantic knowledge graph and an entity alignment model of a structural knowledge graph; and constructing the knowledge graph based on the entity alignment model of the semantic knowledge graph and the entity alignment model of the structural knowledge graph. By applying the knowledge graph to the management and resource scheduling of the multi-platform collaborative observation task, the purposes of intellectualization and automation resource scheduling and management of the collaborative observation task are achieved, and the technical problem that the intellectualization and automation level of the management and resource scheduling of the multi-platform collaborative observation task is low is solved, so that the technical effect of improving the efficiency of the platform collaborative observation task is achieved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a knowledge graph construction method for a multi-platform collaborative observation task according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a knowledge graph constructing apparatus for a multi-platform collaborative observation task according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
according to an embodiment of the present invention, there is provided an embodiment of a knowledge graph construction method for multi-platform collaborative observation tasks, it is noted that the steps illustrated in the flowchart of the drawings may be executed in a computer system such as a set of computer-executable instructions, and that although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be executed in an order different from that herein.
Fig. 1 is a flowchart of a knowledge graph construction method for a multi-platform collaborative observation task according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, determining an entity corresponding to a knowledge graph of a collaborative observation task, and acquiring knowledge of the collaborative observation task based on the domain and range to which the entity belongs, wherein the entity comprises: the remote sensing platform corresponding to the collaborative observation task, the sensor corresponding to the remote sensing platform, the related event of the collaborative observation task and the ground object target of the collaborative observation task;
it should be noted that the remote sensing platform includes: the remote sensing system comprises a space-based remote sensing platform, a space-based remote sensing platform and a near space remote sensing platform;
the knowledge includes: resource information of the remote sensing platform, attribute data of the sensor, different scene event data of the collaborative observation task, related application of the collaborative observation task, prior knowledge of the collaborative observation task and historical collaborative observation task;
wherein the attribute data of the sensor comprises: sensor parameters, imaging mechanism, timeliness and coverage area.
Specifically, firstly, the ontology field and the ontology range constructed by the knowledge graph for the multi-platform collaborative observation task facing the sky and the sky are determined, and knowledge collection is carried out within the ontology field range.
Step S104, determining characteristic information between the entities based on the entities and the knowledge, wherein the characteristic information comprises: intrinsic characteristics, extrinsic characteristics, components, and spatiotemporal relationships;
step S106, converting the characteristic information into triples, and constructing an ontology library based on the triples
Step S108, constructing an entity alignment model of the semantic knowledge graph and an entity alignment model of the structural knowledge graph based on the ontology base;
step S110, constructing the knowledge graph based on the entity alignment model of the semantic knowledge graph and the entity alignment model of the structural knowledge graph.
In the embodiment of the invention, an entity corresponding to a knowledge graph of a collaborative observation task is determined, and knowledge of the collaborative observation task is acquired based on the field and range of the entity, wherein the entity comprises: the remote sensing platform corresponding to the collaborative observation task, the sensor corresponding to the remote sensing platform, the related event of the collaborative observation task and the ground object target of the collaborative observation task; determining feature information between the entities based on the entities and the knowledge, wherein the feature information comprises: intrinsic characteristics, extrinsic characteristics, components, and spatiotemporal relationships; converting the characteristic information into a triple, and constructing an ontology base based on the triple; based on the ontology base, constructing an entity alignment model of a semantic knowledge graph and an entity alignment model of a structural knowledge graph; and constructing the knowledge graph based on the entity alignment model of the semantic knowledge graph and the entity alignment model of the structural knowledge graph. By applying the knowledge graph to the multi-platform collaborative observation task management and resource scheduling, the purposes of intelligentizing and automatically scheduling and managing the collaborative observation task are achieved, and the technical problem that the intelligentization and automation level of the multi-platform collaborative observation task management and resource scheduling is low is solved, so that the technical effect of improving the efficiency of the platform collaborative observation task is achieved.
In addition, it should be noted that, in the present application, after obtaining knowledge, the method further includes:
specifically, the acquired collaborative observation task and knowledge define a basic cognitive framework of a knowledge graph facing the space-air multi-platform collaborative observation task, make clear basic concepts and semantic management among the concepts, and provide basic frameworks and data structures for machine cognition.
In addition, knowledge management can be performed on the acquired knowledge of the collaborative observation task, different index relationships are established for different data and the knowledge is efficiently inquired.
In the embodiment of the present invention, step S104 includes:
s11, performing entity extraction on the knowledge based on the entity and the knowledge to obtain a target entity;
s12, extracting the relation of the knowledge to obtain a relation set between the entities;
and S13, extracting the attributes of the knowledge based on the target entities and the relation set to obtain the characteristic information among the entities.
In the embodiment of the invention, the collaborative observation task knowledge extraction comprises entity extraction, relation extraction and attribute extraction, and the ontology base construction facing the space-in-the-sky multi-platform collaborative observation task is completed.
Firstly, extracting knowledge entities of the collaborative observation task: carrying out comprehensive analysis based on collected and sorted multi-platform collaborative observation task knowledge, and defining important entities in an ontology library aiming at the structured data of a remote sensing platform knowledge base comprising space base, space base and adjacent space, unstructured event data comprising text, internet knowledge, image knowledge and the like, and unstructured information comprising historical observation tasks and the like;
secondly, extracting knowledge relation of the collaborative observation task: aiming at the multi-source heterogeneous characteristics of the collaborative observation task knowledge, a method for performing structured expression of multi-source heterogeneous data in a unified triple data form is provided, aiming at different data, a targeted method is adopted to realize triple relation extraction, so that unified structured expression of heterogeneous data relation is realized, and a relation set between entities is constructed, thereby obtaining a body library;
and finally, extracting knowledge attributes of the collaborative observation task: the attribute extraction is to make clear the internal structure of the entity on the basis of the entity extraction and the relation extraction, and comprises the essential characteristics, the external characteristics, the components, the space-time relation and the like of the entity.
The method comprises the steps of constructing a body of a knowledge graph facing the space-air multi-platform collaborative observation task, extracting important concepts in the collaborative observation task as entities, extracting relationships among the entities, realizing triple structure expression of multi-source heterogeneous data, refining and defining attributes of the entities, and defining internal structures of the entities. Taking a remote sensing platform as an example, the remote sensing platform is to face three platforms of an sky base, a space base and a near space, and is refined into an sky base visible light remote sensing observation platform, an sky base SAR remote sensing observation platform, an sky base electronic signal remote sensing observation platform, a space base visible light remote sensing observation platform and the like, and the system diagram is shown in the following table.
Entity extraction and attribute extraction example table of remote sensing observation platform
The method is used for multi-platform collaborative observation task, the knowledge relation construction of multi-source heterogeneous data is achieved based on triple relation extraction, knowledge association is conducted on a remote sensing observation platform, a collaborative observation event and a collaborative observation target, and the association relation between bodies is established, and is shown in the following table.
Example table of incidence relation between ontologies
In the embodiment of the present invention, step S106 includes:
s21, classifying the knowledge based on the characteristic information among the entities in the ontology library to obtain semantic knowledge characteristics and structural knowledge characteristics;
s22, constructing an entity alignment model of the semantic knowledge graph based on the semantic knowledge characteristics;
and S23, constructing an entity alignment model of the structural knowledge graph based on the structural knowledge characteristics.
In the embodiment of the invention, a method for multi-source heterogeneous knowledge fusion based on representation learning is provided, which comprises the following steps:
aiming at multi-source heterogeneous data, on the basis of entity extraction, a collaborative training frame is used to divide entity features into a semantic view and a structural view, an entity alignment model which is based on two maps joint representation learning is trained under the two views respectively, and a credible entity alignment result is continuously selected to assist in training of the model under the other view, so that fusion of semantic and structural information is realized, and entity repetition and redundancy in the maps are cleaned and combined; and integrating the sub-knowledge maps generated after the steps of entity alignment and the like.
Model iterative training is carried out by establishing an entity alignment model based on a semantic knowledge graph and an entity alignment model based on a structural knowledge graph, fusion of semantic and structural information is realized, and entity repetition and redundancy in the graph are cleaned and combined.
In the embodiment of the present invention, step S108 includes the following steps:
step S31, carrying out iterative training on the entity alignment model of the semantic graph and the entity alignment model of the structural knowledge graph to obtain a sub-knowledge graph;
and S32, cleaning or combining target entities in the sub-maps based on the sub-knowledge maps to obtain the knowledge maps, wherein the target entities are repeated entities and redundant entities.
In the embodiment of the invention, a method for multi-source heterogeneous knowledge fusion based on representation learning is provided, which comprises the following steps:
aiming at multi-source heterogeneous data, on the basis of entity extraction, a collaborative training frame is used to divide entity features into a semantic view and a structural view, an entity alignment model which is based on two maps joint representation learning is trained under the two views respectively, and a credible entity alignment result is continuously selected to assist in training of the model under the other view, so that fusion of semantic and structural information is realized, and entity repetition and redundancy in the maps are cleaned and combined; and integrating the sub-knowledge maps generated after the steps of entity alignment and the like.
In an embodiment of the present invention, the method further comprises:
and step S110, filling, complementing and updating the knowledge graph based on a forward rule matching algorithm.
In the embodiment of the invention, in order to face the completeness and updating capability of the knowledge map of the space-air multi-platform cooperative observation task, the mining of map hidden knowledge is realized by a forward rule matching inference algorithm, and based on an inference machine model of the forward rule matching inference algorithm, the mining of the potential relationship of the map is realized by the inference machine model facing the space-air multi-platform cooperative observation task. Firstly, establishing a knowledge graph relation rule set, combining the existing knowledge graph, performing knowledge relation reasoning calculation to obtain new knowledge, and on the basis, extracting a triple relation to realize filling and updating of the knowledge graph.
The method comprises the following specific steps:
constructing a rule set of a knowledge inference machine, expressing a format by using a rule in Jena, namely an IF condition THEN conclusion form, and obtaining a corresponding conclusion IF the condition is met;
analyzing the input of a knowledge graph and a rule set based on a Rete forward rule matching inference engine algorithm and oriented to a forward rule to realize the mining of potential knowledge;
and carrying out triple structure expression based on the mined new knowledge to realize the updating of the knowledge graph.
The embodiment of the invention provides a knowledge graph construction method for an sky-near-sky multi-platform cooperative observation task, which is used for realizing ontology construction, knowledge expression, knowledge association, knowledge fusion and the like of unstructured data of remote sensing resource information such as a sky base, a space base, an adjacent space and the like, event data, priori knowledge, historical observation task information and the like, constructing a set of targeted knowledge graph, and realizing automatic management and resource scheduling of a multi-platform cooperative observation task.
Example two:
the embodiment of the invention also provides a knowledge graph construction device for the multi-platform collaborative observation task, and the cultivated land change detection device is used for executing the knowledge graph construction method for the multi-platform collaborative observation task, which is provided by the embodiment of the invention.
As shown in fig. 2, fig. 2 is a schematic diagram of the above-mentioned knowledge graph construction for the multi-platform collaborative observation task, where the knowledge graph construction for the multi-platform collaborative observation task includes: the first determining unit 10 is a second determining unit 20, a converting unit 30, a first building unit 40 and a second building unit 50.
The first determining unit 10 is configured to determine an entity corresponding to a knowledge graph of a collaborative observation task, and acquire knowledge of the collaborative observation task based on a domain and a range to which the entity belongs, where the entity includes: the remote sensing platform corresponding to the collaborative observation task, the sensor corresponding to the remote sensing platform, the related event of the collaborative observation task and the ground object target of the collaborative observation task;
the second determining unit 20 is configured to determine feature information between the entities based on the entities and the knowledge, where the feature information includes: intrinsic characteristics, extrinsic characteristics, components, and spatiotemporal relationships;
the converting unit 30 is configured to convert the feature information into a triple, and construct an ontology library based on the triple;
the first construction unit 40 is configured to construct an entity alignment model of a semantic knowledge graph and an entity alignment model of a structural knowledge graph based on the ontology library;
the second construction unit 50 is configured to construct the knowledge graph based on the entity alignment model of the semantic knowledge graph and the entity alignment model of the structural knowledge graph.
In the embodiment of the invention, an entity corresponding to a knowledge graph of a collaborative observation task is determined, and knowledge of the collaborative observation task is acquired based on the field and range of the entity, wherein the entity comprises: the remote sensing platform corresponding to the collaborative observation task, the sensor corresponding to the remote sensing platform, the related event of the collaborative observation task and the ground object target of the collaborative observation task; determining feature information between the entities based on the entities and the knowledge, wherein the feature information comprises: intrinsic characteristics, extrinsic characteristics, components, and spatiotemporal relationships; converting the characteristic information into a triple, and constructing an ontology base based on the triple; based on the ontology base, constructing an entity alignment model of the semantic knowledge graph and an entity alignment model of the structural knowledge graph; and constructing the knowledge graph based on the entity alignment model of the semantic knowledge graph and the entity alignment model of the structural knowledge graph. By applying the knowledge graph to the multi-platform collaborative observation task management and resource scheduling, the purposes of intelligentizing and automatically scheduling and managing the collaborative observation task are achieved, and the technical problem that the intelligentization and automation level of the multi-platform collaborative observation task management and resource scheduling is low is solved, so that the technical effect of improving the efficiency of the platform collaborative observation task is achieved.
Example three:
an embodiment of the present invention further provides an electronic device, which includes a memory and a processor, where the memory is used to store a program that supports the processor to execute the method in the first embodiment, and the processor is configured to execute the program stored in the memory.
Referring to fig. 3, an embodiment of the present invention further provides an electronic device 100, including: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is arranged to execute executable modules, such as computer programs, stored in the memory 61.
The Memory 61 may include a Random Access Memory (RAM) and a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like may be used.
The bus 62 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus.
The memory 61 is configured to store a program, and the processor 60 executes the program after receiving an execution instruction, where the method performed by the apparatus defined by the flow program disclosed in any embodiment of the present invention may be applied to the processor 60, or implemented by the processor 60.
The processor 60 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 60. The Processor 60 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in a memory 61, and the processor 60 reads the information in the memory 61 and completes the steps of the method in combination with the hardware.
Example four:
the embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the method in the first embodiment.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on 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.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: those skilled in the art can still make modifications or changes to the embodiments described in the foregoing embodiments, or make equivalent substitutions for some features, within the scope of the disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A knowledge graph construction method for a multi-platform collaborative observation task is characterized by comprising the following steps:
determining an entity corresponding to a knowledge graph of a collaborative observation task, and acquiring knowledge of the collaborative observation task based on the field and range of the entity, wherein the entity comprises: the remote sensing platform corresponding to the collaborative observation task, the sensor corresponding to the remote sensing platform, the related event of the collaborative observation task and the ground object target of the collaborative observation task;
determining feature information between the entities based on the entities and the knowledge, wherein the feature information comprises: intrinsic characteristics, extrinsic characteristics, components, and spatiotemporal relationships;
converting the feature information into triples, and constructing an ontology library based on the triples;
based on the ontology base, constructing an entity alignment model of the semantic knowledge graph and an entity alignment model of the structural knowledge graph;
and constructing the knowledge graph based on the entity alignment model of the semantic knowledge graph and the entity alignment model of the structural knowledge graph.
2. The method of claim 1,
the remote sensing platform comprises: the remote sensing system comprises a space-based remote sensing platform, a space-based remote sensing platform and a near space remote sensing platform;
the knowledge includes: resource information of the remote sensing platform, attribute data of the sensor, different scene event data of the collaborative observation task, related application of the collaborative observation task, prior knowledge of the collaborative observation task and historical collaborative observation task;
wherein the attribute data of the sensor comprises: sensor parameters, imaging mechanism, timeliness and coverage area.
3. The method of claim 2, wherein determining feature information between the entities based on the entities and the knowledge comprises:
performing entity extraction on the knowledge based on the entity and the knowledge to obtain a target entity;
extracting the relation of the knowledge to obtain a relation set between the entities;
and extracting the attributes of the knowledge based on the target entities and the relation set to obtain the characteristic information between the entities.
4. The method of claim 1, wherein constructing an entity alignment model of a semantic knowledge graph and an entity alignment model of a structural knowledge graph based on the ontology library comprises:
classifying the knowledge based on feature information between entities in the ontology library to obtain semantic knowledge features and structural knowledge features;
building an entity alignment model of the semantic knowledge graph based on the semantic knowledge features;
and constructing an entity alignment model of the structural knowledge graph based on the structural knowledge characteristics.
5. The method of claim 1, wherein constructing the knowledge-graph based on the entity-aligned model of the semantic graph and the entity-aligned model of the structural knowledge-graph comprises:
performing iterative training on the entity alignment model of the semantic graph and the entity alignment model of the structural knowledge graph to obtain a sub-knowledge graph;
and cleaning or combining target entities in the sub-maps based on the sub-knowledge maps to obtain the knowledge maps, wherein the target entities are repeated entities and redundant entities.
6. The method of claim 1, further comprising:
and filling, complementing and updating the knowledge graph based on a forward rule matching algorithm.
7. A knowledge graph construction device for a multi-platform collaborative observation task is characterized by comprising the following steps: a first determining unit, a second determining unit, a converting unit, a first constructing unit and a second constructing unit, wherein,
the first determining unit is configured to determine an entity corresponding to a knowledge graph of a collaborative observation task, and acquire knowledge of the collaborative observation task based on a domain and a range to which the entity belongs, where the entity includes: the remote sensing platform corresponding to the collaborative observation task, the sensor corresponding to the remote sensing platform, the related event of the collaborative observation task and the ground object target of the collaborative observation task;
the second determining unit is configured to determine feature information between the entities based on the entities and the knowledge, where the feature information includes: intrinsic characteristics, extrinsic characteristics, components, and spatiotemporal relationships;
the conversion unit is used for converting the feature information into triples and constructing an ontology library based on the triples;
the first construction unit is used for constructing an entity alignment model of the semantic knowledge graph and an entity alignment model of the structural knowledge graph based on the ontology base;
the second construction unit is used for constructing the knowledge graph based on the entity alignment model of the semantic knowledge graph and the entity alignment model of the structural knowledge graph.
8. The apparatus of claim 7,
the remote sensing platform includes: the system comprises a space-based remote sensing platform, an air-based remote sensing platform and a near space remote sensing platform;
the knowledge includes: resource information of the remote sensing platform, attribute data of the sensor, different scene event data of the collaborative observation task, related application of the collaborative observation task, prior knowledge of the collaborative observation task and historical collaborative observation task;
wherein the attribute data of the sensor comprises: sensor parameters, imaging mechanism, timeliness and coverage area.
9. An electronic device comprising a memory for storing a program that enables a processor to perform the method of any of claims 1 to 6 and a processor configured to execute the program stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1 to 6.
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