CN116932780B - Astronomical knowledge graph construction method, resource searching method, device and medium - Google Patents
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Abstract
The application relates to an astronomical knowledge graph construction method, a resource searching method, equipment and a medium, and celestial body information corresponding to resources is obtained, wherein the celestial body information comprises morphological characteristics of celestial bodies and coordinates of celestial bodies; classifying celestial bodies based on morphological characteristics to obtain a plurality of celestial body categories; calculating the distance between every two celestial bodies under the same celestial body class according to the coordinates of the celestial bodies, taking each celestial body as a node, correlating every two celestial bodies belonging to the same celestial body class, and constructing to obtain a plurality of knowledge maps; and acquiring the spectral characteristics of each celestial body, and correlating celestial bodies with similar spectral characteristics between any first knowledge graph and any second knowledge graph. By the astronomical knowledge graph construction method, the accuracy of constructed knowledge graph is improved, and the astronomical knowledge graph construction method is beneficial to related personnel based on a knowledge graph search system and perfect astronomical information.
Description
Technical Field
The application relates to the technical field of knowledge graphs, in particular to an astronomical knowledge graph construction method, a resource searching method, equipment and a medium.
Background
Astronomy is a science of exploring the universe, and covers the aspects of observation, theoretical research, research on universe evolution and the like of astronomical objects. With the continuous development of astronomy technology and the change of data acquisition modes, data related to the astronomy field becomes more and more complex and huge. The astronomy research field is gradually expanded to various aspects of universe, such as astronomy, star, planet, universe and the like. In such a big data age, how to mine meaningful information from astronomical data is an important problem to be solved in astronomy.
A knowledge graph is a graphical data structure used to represent knowledge and relationships that may organize entities, attributes, and relationships into a structured network. Knowledge maps have been widely used in the fields of natural language processing, computer vision, and the like. In astronomy field, constructing a knowledge graph based on astronomical coordinates can help researchers to better understand the relationship between astronomical bodies, and can promote research on problems such as universe evolution, astronomical structure, star formation and the like.
In order to enable the entities in the knowledge-graph to be associated by spatial location information, the spatial information is generally added to the data as an element in the construction process of the knowledge-graph. Currently, studies based on spatial knowledge-graph mainly focus on information correlation in geographic aspects. In the related technology, a national science and technology resource service sharing platform of a national astronomical science data center is used for collecting, managing, reorganizing and integrating science data and journal paper related data in the astronomical field. However, this system has not yet achieved domain knowledge intelligent mining and scientific literature deep clustering. The multi-mode data in astronomical field is usually delimited by field characteristics such as celestial body classification, and a knowledge graph of coarse-granularity multiple celestial body classifications is constructed, so that the problems of messy information and low accuracy are caused.
Disclosure of Invention
Based on this, it is necessary to provide an astronomical knowledge graph construction method, a resource searching method, a computer device and a computer readable storage medium capable of structurally saving astronomical knowledge and showing it in the form of a graph.
In a first aspect, the present application provides a method for constructing an astronomical knowledge graph, the method including:
acquiring celestial body information corresponding to the resource, wherein the celestial body information comprises morphological characteristics of celestial bodies and coordinates of celestial bodies;
classifying the celestial bodies based on the morphological characteristics to obtain a plurality of celestial body categories;
calculating the distance between every two celestial bodies under the same celestial body class according to the coordinates of the celestial bodies, taking each celestial body as a node, correlating every two celestial bodies belonging to the same celestial body class, and constructing to obtain a plurality of knowledge maps;
and acquiring the spectral characteristics of each celestial body, and associating any celestial body between the first knowledge graph and the second knowledge graph according to the spectral characteristics.
In one embodiment, calculating the distance between every two celestial bodies in the same celestial body class according to the coordinates of the celestial bodies includes:
obtaining a midpoint position between a first celestial body and a second celestial body according to the coordinates of the first celestial body and the coordinates of the second celestial body, and obtaining a linear distance from the earth to the midpoint position, wherein the coordinates comprise coordinates under an equatorial coordinate system or coordinates under a yellow track coordinate system;
taking the linear distance as a sphere radius, and calculating a semi-normal vector value of a central angle of the first celestial body and the second celestial body in the sphere;
and acquiring the celestial distance between the first celestial body and the second celestial body according to the semi-normal value and the linear distance, wherein the semi-normal value is the ratio of the celestial distance to the linear distance.
In one embodiment, the spectral features of the celestial body include a light intensity and a frequency distribution.
In one embodiment, obtaining coordinates of the celestial body includes:
and judging whether the coordinate system of the celestial body is an equatorial coordinate system or not, if not, acquiring a conversion relation between the coordinate system of the celestial body and the equatorial coordinate system, and acquiring the coordinate of the celestial body under the equatorial coordinate system based on the conversion relation.
In one embodiment, after determining whether the coordinate system in which the coordinates of the celestial body are located is an equatorial coordinate system, if not, the method further includes:
and judging whether the observation time of the coordinates of the celestial body is the preset time, if not, converting the coordinates of the celestial body of the observation time in the equatorial coordinate system into the coordinates of the celestial body of the preset time in the equatorial coordinate system.
In one embodiment, constructing a first knowledge-graph according to the celestial coordinates and the distance includes:
obtaining nodes of the first knowledge graph according to the celestial coordinates, and obtaining the weights of edges in the first knowledge graph according to the distances;
and constructing the first knowledge graph according to the weights of the nodes and the edges.
In one embodiment, the constructing a first knowledge graph according to the celestial coordinates and the distance further includes:
acquiring the attribute of a celestial body, and taking the attribute of the celestial body as the attribute of a node in the first knowledge graph;
and constructing the first knowledge graph according to the node, the weight of the edge and the attribute of the node.
In a second aspect, the present application further provides a resource searching method, where the method includes:
displaying celestial body identifiers corresponding to the first resources in an interactive interface;
and responding to the access operation of the celestial body identifier, and returning to a second resource, wherein the first resource and the second resource establish an association relationship based on a knowledge graph, and the knowledge graph is generated according to the method of any embodiment of the astronomical knowledge graph construction method in the first aspect.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the astronomical knowledge graph construction method according to the first aspect when executing the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the astronomical knowledge graph construction method according to the first aspect described above.
The astronomical knowledge graph construction method, the resource searching method, the computer equipment and the storage medium acquire astronomical information corresponding to the resource, wherein the astronomical information comprises morphological characteristics of an astronomical body and coordinates of the astronomical body; classifying the celestial bodies based on the morphological characteristics to obtain a plurality of celestial body categories; calculating the distance between every two celestial bodies under the same celestial body class according to the coordinates of the celestial bodies, taking each celestial body as a node, correlating every two celestial bodies belonging to the same celestial body class, and constructing to obtain a plurality of knowledge maps; and acquiring the spectral characteristics of each celestial body, and correlating celestial bodies with similar spectral characteristics between any first knowledge graph and any second knowledge graph to classify celestial bodies according to celestial body forms. The distance between celestial bodies is calculated under each celestial body category, the accuracy of distance calculation is improved, the distance is used as the relation between nodes in the small-range knowledge graph, the small-range knowledge graph is associated based on the spectral characteristics, the accuracy of the constructed knowledge graph is improved, and the method is beneficial to related personnel to search for system and perfect celestial body information based on the knowledge graph.
Drawings
Fig. 1 is a hardware structure block diagram of a terminal of an astronomical knowledge graph construction method according to an embodiment;
FIG. 2 is a schematic flow chart of a method for constructing a astronomical knowledge graph in one embodiment;
FIG. 3 is a schematic flow diagram of astronomical knowledge graph construction based on astronomical coordinates in one embodiment;
FIG. 4 is a schematic diagram of a knowledge graph in one embodiment;
FIG. 5 is a flow diagram of a method of resource lookup in one embodiment;
FIG. 6 is an application environment diagram of a method for constructing a astronomical knowledge graph in one embodiment;
fig. 7 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The method embodiments provided in the present embodiment may be executed in a terminal, a computer, or similar computing device. For example, the method runs on a terminal, and fig. 1 is a hardware structure block diagram of the terminal of the astronomical knowledge graph construction method according to an embodiment of the present application. As shown in fig. 1, the terminal may include one or more (only one is shown in fig. 1) processors 102 and a memory 104 for storing data, wherein the processors 102 may include, but are not limited to, a microprocessor MCU, a programmable logic device FPGA, or the like. The terminal may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the structure shown in fig. 1 is merely illustrative and is not intended to limit the structure of the terminal. For example, the terminal may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1.
The memory 104 may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to the astronomical knowledge graph construction method in the present embodiment, and the processor 102 executes the computer program stored in the memory 104 to perform various functional applications and data processing, that is, to implement the above-described method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. The network includes a wireless network provided by a communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is configured to communicate with the internet wirelessly.
In one embodiment, as shown in fig. 2, an astronomical knowledge graph construction method is provided, and an application environment of the method in fig. 1 is taken as an example for explanation, and the method includes the following steps:
step S201, celestial body information corresponding to the resources is acquired, wherein the celestial body information comprises morphological characteristics of celestial bodies and coordinates of celestial bodies. The resources comprise astronomical field papers, reports, news and other multimedia resources containing astronomical information. Celestial morphological features include the size morphology, spectral features, and the like of a celestial body. Optionally, resources such as papers, reports, news and the like in the astronomical field are collected according to a search engine, astronomical observation data are collected according to the resources, and celestial body information including names, coordinates and morphological characteristics of celestial bodies is obtained.
Step S202, classifying celestial bodies based on morphological characteristics to obtain a plurality of celestial body categories.
The celestial body category includes: star, planet, system of stars, satellite, asteroid, comet, and black hole. Among them, stars are one of the most common types in celestial bodies, and they have an autonomous luminescence property, and there are a large number of papers for common stars, and separate analytical researches have been carried out. The planet is a celestial body that runs around the star, and the celestial body is relatively small in size. The asterism is a massive structure consisting of stars, interstellar gases, interstellar dust and dark matter. Satellites are celestial bodies that travel around planets and are relatively small in size. Asteroid is a celestial body of rock and metal material between the sun and the planet in the solar system, typically below hundreds of kilometers in size. Comets are celestial bodies of frozen gas, dust, and rock, which are generally small in size, but the tail length can be very long. Black holes are extremely compact objects with very strong attraction forces, which form when stars collapse or collide with large mass celestial bodies and ingest the surrounding material.
And step S203, calculating the distance between every two celestial bodies in the same celestial body category according to the coordinates of the celestial bodies, using each celestial body as a node, and correlating every two celestial bodies belonging to the same celestial body category to construct a plurality of knowledge maps.
And according to the distance between every two celestial bodies in the same celestial body category, correlating celestial bodies in the same celestial body category to obtain a plurality of small-range knowledge maps belonging to different celestial body categories. Because the celestial bodies under the same celestial body category are similar in size, the celestial body distance obtained through calculation is more accurate, and the obtained multiple small-range knowledge maps are more accurate.
Step S204, obtaining the spectral characteristics of each celestial body, and associating any celestial body between the first knowledge graph and the second knowledge graph according to the spectral characteristics.
Wherein the spectral signature includes at least one of: spectral wavelength, spectral type. The first knowledge graph and the second knowledge graph are small-range knowledge graphs, and the first knowledge graph and the second knowledge graph correspond to different celestial body categories respectively.
Optionally, correlating celestial bodies with any first knowledge graph and any second knowledge graph, wherein the difference between two or more spectrum wavelength ranges is within a set value, and/or correlating two or more celestial bodies with the same spectrum type to realize correlation of a plurality of small-range knowledge graphs, so as to construct a complete knowledge graph. The set value can be set according to the user requirement. The spectral characteristics among celestial bodies are used as the connection conditions of the knowledge graph, so that the association dimension among all entities in the knowledge graph is enriched.
In the astronomical knowledge graph construction method, fine-granularity astronomical knowledge is extracted based on celestial coordinates, astronomical knowledge graphs based on star meters are constructed based on the distance between the celestial coordinates and the celestial bodies, the problem that coarse-granularity knowledge graphs contain messy information is avoided, and scientific researchers can conveniently find and analyze astronomical knowledge based on the knowledge graphs constructed by the embodiment. Because the distance orders of different celestial body forms are greatly different, in order to improve the accuracy of searching the knowledge graph, celestial bodies are classified according to the celestial body forms, the distance between celestial bodies is calculated under each celestial body class, the accuracy of distance calculation is improved, the distance is used as the relation between nodes in a small-range knowledge graph, the small-range knowledge graph is associated based on spectral features, and the accuracy of the constructed knowledge graph is improved.
In one embodiment, obtaining coordinates of the celestial body includes: and judging whether the coordinate system of the celestial body is an equatorial coordinate system or not, if not, acquiring a conversion relation between the coordinate system of the celestial body and the equatorial coordinate system, and acquiring the coordinate of the celestial body under the equatorial coordinate system based on the conversion relation.
In the astronomical field, an equatorial coordinate system or a yellow track coordinate system is generally used for describing the position of an astronomical object, and in order to ensure the identity of coordinates and calculate the distance between nodes of a knowledge graph, data of different coordinate systems are converted. Illustratively, converting celestial coordinates in a yellow-track coordinate system and an equatorial coordinate system includes: describing the position of celestial bodies in an equatorial coordinate system using two variables, the right ascension and the right ascensionIs the projection of celestial bodies on the equator expressed in time units, declination +.>The distance of the celestial body from the equator of the celestial sphere is expressed in degrees. Describing the position of celestial body in the yellow road coordinate system using two variables of yellow warp and yellow weft, wherein yellow warp +.>Is the projection of celestial body on the yellow lane, expressed in degrees, yellow latitude +.>Is the angle of the celestial body from the yellow road, expressed in degrees. />Is a yellow-red intersection angle, namely the inclination angle of the ground axis.
In the process of converting the yellow track coordinate system into the equatorial coordinate system, the coordinate system conversion needs to simultaneously satisfy the following three formulas because of the condition of multiple solutions of the trigonometric function of the angle:
in the process of converting the equatorial coordinate system into the yellow track coordinate system, as the trigonometric function of the angle has multiple solutions, the conversion of the coordinate system needs to simultaneously satisfy the following three formulas:
because the position and the speed of the celestial body change continuously along with time, celestial body coordinates obtained from data are observed in different years. Errors may result if the distances of different years of celestial bodies are directly calculated. It is therefore necessary to convert the coordinate systems of different years to the coordinate system of the same moment. And judging whether the observation time of the celestial body coordinate is the preset time, if not, converting the coordinate of the celestial body of the observation time in the equatorial coordinate system into the coordinate of the celestial body of the preset time in the equatorial coordinate system.
The method comprises the steps of obtaining date and time of observation time of celestial coordinates, and angle and speed of rotation and revolution of celestial bodies at the observation time, roughly measuring difference between acquisition year of celestial coordinates and preset time, and converting the celestial coordinates to the same time according to measurement results.
Illustratively, the celestial coordinates are unified, taking the J2000 as a standard. Wherein, J2000 is a specific time point, which represents 12 hours (greenwich standard time) of month 1 and 1 day 1 of epoch 2000. The astronomical coordinates obtained by observing different time points are compared and analyzed by converting the astronomical coordinates into J2000 coordinates, so that more accurate and consistent astronomical observation and research are realized, and the accuracy of the knowledge graph is improved. And acquiring the angle and the speed of the celestial body movement at the observation time. The main motion of the celestial body is divided into rotation and revolution of the celestial body. The celestial bodies recorded at different times are subject to the influence of the rotation of the celestial bodies, and are different in relative positions with respect to the earth. Since the revolution change of the star is small, it can be temporarily ignored, and the revolution position change of the planet is large, it is necessary to acquire the revolution data of the planet. And acquiring the position and the speed of the earth at the observation time, such as nutation, autorotation, time lapse and the like of the earth. According to the earth movement data at the observation time, the angle and the speed of the celestial body movement, the atmospheric refraction and other effects, the difference between the observation time of the celestial body coordinates and the J2000 is roughly estimated, the coordinates of all celestial bodies are converted into the coordinates under the J2000 standard, and the coordinate can be calculated by using astronomical calculation software.
In one embodiment, calculating the distance between every two celestial bodies in the same celestial body class according to the coordinates of the celestial bodies includes: obtaining a midpoint position between the first celestial body and the second celestial body according to the coordinates of the first celestial body and the coordinates of the second celestial body, and obtaining a linear distance from the earth to the midpoint position, wherein the coordinates comprise coordinates under an equatorial coordinate system or coordinates under a yellow track coordinate system; taking the linear distance as the radius of the sphere, and calculating the semi-normal vector value of the central angles of the first celestial body and the second celestial body in the sphere; and obtaining the celestial distance between the first celestial body and the second celestial body according to the semi-normal vector value and the linear distance, wherein the semi-normal vector value is the ratio of the celestial distance to the linear distance.
The first celestial body and the second celestial body are two celestial bodies under the same celestial body category. And calculating to obtain the midpoint position according to the average value of the coordinates of the first celestial body and the second celestial body. Taking the coordinates of the first celestial body and the second celestial body as an equatorial coordinate system as an example, carrying out average calculation on the right ascension and the right ascension of the two celestial bodies to obtain the midpoint position. The linear distance from the earth to the midpoint position can be calculated by a trigonometric parallax method. And taking the straight line distance as the radius of the sphere, and calculating the celestial distance between the first celestial body and the second celestial body according to the semi-normal vector value and the straight line distance.
Illustratively, the semi-normal values of the central angles of the first celestial body and the second celestial body in the sphere are calculated:
where d is the distance between the two celestial bodies, r is the radius of the sphere, i.e. the distance of the earth calculated as described above to the center position of the two celestial bodies,is the latitude of the first celestial body, +.>Is the latitude of the second celestial body, +.>Is the longitude of the first celestial body,is the longitude of the second celestial body and hav is an abbreviation of a semi-normal function.
The calculation formula of the semi-normal function is as follows:
based on the calculation formula of the semi-normal function and the semi-normal value, the distance d between two celestial bodies can be calculated through the arcsine function, and the formula is as follows:
where d is the distance between the two celestial bodies and r is the radius of the sphere, i.e. the distance of the earth from the center of the two celestial bodies calculated as described above.
In one embodiment, constructing a first knowledge-graph from celestial coordinates and distances includes: obtaining nodes of a first knowledge graph according to the celestial coordinates, and obtaining weights of edges in the first knowledge graph according to the distances; and constructing a first knowledge graph according to the weights of the nodes and the edges. And taking different celestial bodies as nodes in the knowledge graph, and calculating the distance between the two celestial bodies as the weight of the edge between the nodes to obtain the edge of each node in the knowledge graph so as to represent the relation between the different celestial bodies. Constructing a first knowledge graph according to the celestial coordinates and the distance, and further comprising: acquiring the attribute of the celestial body, and taking the attribute of the celestial body as the attribute of the node in the first knowledge graph; and constructing a first knowledge graph according to the weights of the nodes and the edges and the attributes of the nodes. The attributes of the celestial body include the resource related to the celestial body, such as celestial body related papers, and the mass, size, density, temperature, rotation period, orbit inclination angle, orbit eccentricity, and the like of the celestial body.
After constructing a plurality of first knowledge maps, extracting spectral features of each celestial body, and linking the similar spectral features to construct the whole astronomical knowledge map. Spectral features of celestial bodies include light intensity and frequency distribution.
In one embodiment, as shown in fig. 3, a schematic flow diagram of astronomical knowledge graph construction based on astronomical coordinates is disclosed.
Step S301, data acquisition. The method comprises the steps of searching websites by using common documents, such as hundred degrees, google and the like, collecting astronomical field papers, reports, news and other resources, collecting astronomical observation data from the resources, recording names, types and coordinates of celestial bodies, and simultaneously taking the resources corresponding to the celestial bodies as attributes of the celestial bodies, so that after knowledge graph construction is facilitated, scientific researchers can inquire corresponding papers according to the celestial bodies.
In step S302, the coordinates are converted. And (3) primarily screening acquired celestial coordinates, eliminating celestial bodies marked with errors, and improving the accuracy and reliability of acquired data. For the calculation of the distance between the subsequent nodes, celestial coordinates in the equatorial coordinate system are converted into celestial coordinates in the yellow-track coordinate system.
In step S303, the coordinates are unified. In most documents, J2000 will be used to unify celestial coordinates, but there will still be some labeling of the celestial coordinates that is not standard. In this example, the literature collection is based on the J2000 standard, and nonstandard celestial coordinates are uniformly labeled.
And S304, calculating the weight of the edge in the knowledge graph. And taking different celestial bodies as nodes in the knowledge graph, calculating the distance between the two celestial bodies as the weight of the edge between the nodes, and constructing the edge between the celestial bodies. When the distance between two celestial bodies is acquired, the celestial bodies are classified according to the forms in order to improve the accuracy of searching the knowledge graph because the distance magnitude of different celestial body forms is very different, and the celestial bodies can be classified into the following categories according to the forms: star, planet, system of stars, satellite, asteroid, comet, and black hole. And respectively calculating the distance between celestial bodies under the same class according to the celestial body class by using a semi-normal formula. Optionally, calculating the distance between the two celestial bodies includes: acquiring coordinates of two celestial bodies under the same category; taking the straight line distance from the earth to the midpoint of the two celestial bodies as the radius of the sphere, and calculating the semi-normal vector value of the central angles of the two celestial bodies in the sphere; and obtaining the celestial distance according to the semi-normal vector value and the linear distance.
And S305, constructing a knowledge graph. And taking different celestial bodies as nodes in the knowledge graph, taking the distance between the two celestial bodies as the weight of the edge between the nodes of the knowledge graph, and constructing the edge between the celestial bodies to represent the relation between the different celestial bodies. And respectively constructing a plurality of small-range knowledge maps according to the celestial body category, wherein one small-range knowledge map corresponds to one celestial body category. And extracting spectral characteristics of each celestial body, and linking celestial bodies with similar spectral characteristics to realize connection between different small-range knowledge maps. Wherein the spectrum breaks down the light into beams of different wavelengths, and spectral characteristics are obtained by measuring the light intensity and frequency distribution at each wavelength. In order to facilitate scientific researchers to further know the celestial body, the attribute of the celestial body is stored as the attribute of the node, so that the celestial body can be conveniently queried to obtain a corresponding series of indexes, and content which can be researched is extracted from the indexes. The attributes of celestial bodies include, in particular, papers relating to the celestial body, mass, size, density, temperature, rotation period, orbit inclination, orbit eccentricity, etc. The constructed nodes and edges are stored in a graph database Neo4j, so that subsequent query and analysis are facilitated, the astronomical knowledge graph is drawn through visual software, and related researchers perform search analysis, and a schematic diagram is shown in fig. 4, which is a schematic diagram of a knowledge graph in the embodiment.
Step S306, updating in real time. And updating the knowledge graph periodically, including adding new celestial body nodes, constructing a new side weight calculation mode, updating attribute information and the like. When the map is updated, quality control and verification are carried out on the knowledge map, so that the accuracy and completeness of knowledge and relationship in the knowledge map are ensured. Therefore, the newly obtained celestial body is directly added into the historical graph database, the knowledge graph is updated in real time, and the construction effect of the knowledge graph can be effectively ensured.
In this embodiment, each celestial body is regarded as a node in the knowledge graph, the straight line distance between the center positions of the two celestial bodies and the earth is regarded as the radius of the sphere, and the straight line distance between the two spheres is obtained based on the calculation method of the distance between the two points of the sphere, so that the distance between the two celestial bodies is calculated more accurately, the distance between the celestial bodies is regarded as the relation between the entities in the construction of the knowledge graph, and the accuracy of the knowledge graph is improved. Based on accurate description of the celestial body position of coordinates, an astronomical knowledge graph based on celestial body coordinates is constructed, and the relation among different celestial bodies can be excavated in space, so that a large variable is provided for the property of celestial bodies in a research area. And the basic information of the celestial body and the resource related to the celestial body are used as the attribute values of the nodes in the knowledge graph, so that the retrieval efficiency of the knowledge graph is improved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a resource searching method, which comprises the following steps: displaying celestial body identifiers corresponding to the first resources in an interactive interface; and returning a second resource in response to the access operation to the celestial body identifier, wherein the first resource and the second resource establish an association relationship based on the knowledge graph, and the knowledge graph is generated according to the astronomical knowledge graph construction method in any method embodiment.
In one embodiment, a resource searching method is provided, and a flow chart of the resource searching method in the embodiment of fig. 5 is shown in fig. 5, and the flow chart includes the following steps:
step S501, obtaining an identification of a first celestial body, and returning an identification of a second celestial body associated with the first celestial body by the interactive interface in response to the identification of the first celestial body.
The first celestial body is a celestial body to be searched by a user, the first celestial body and the second celestial body establish an association relationship based on a knowledge graph, and the knowledge graph is generated according to the astronomical knowledge graph construction method of any embodiment. The identification of the first celestial body and the second celestial body may be celestial body names.
Step S502, responding to the access operation of the user to the celestial body identifier, and returning the resource corresponding to the celestial body by the interactive interface.
Resources include basic attributes of celestial bodies, papers, journals, or reports associated with celestial bodies. Wherein the basic attributes of the celestial body include coordinates, category, spectral information, and the like of the celestial body. The access operation to the keywords can be that an operation instruction issued by a user is obtained through input tools such as a touch screen, a keyboard, a mouse and the like.
The resource searching method provided by the embodiment of the application can be applied to an application environment shown in fig. 6. Wherein the terminal 602 communicates with the server 604 via a network. The data storage system may store data that the server 604 needs to process. The data storage system may be integrated on the server 604 or may be located on a cloud or other network server. The data storage system is used for storing resources containing celestial body information. And displaying the constructed knowledge graph through a terminal. The terminal 602 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 604 may be implemented as a stand-alone server or as a cluster of servers.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing data relating to celestial body information. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a method of astronomical knowledge graph construction. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 7 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, including a memory and a processor, where the memory stores a computer program that, when executed by the processor, implements the steps of the embodiments of the astronomical knowledge graph construction method described above.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the steps of the above-described embodiments of the astronomical knowledge graph construction method.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as Static Random access memory (Static Random access memory AccessMemory, SRAM) or dynamic Random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. The astronomical knowledge graph construction method is characterized by comprising the following steps of:
acquiring celestial body information corresponding to the resource, wherein the celestial body information comprises morphological characteristics of celestial bodies and coordinates of celestial bodies;
classifying the celestial bodies based on the morphological characteristics to obtain a plurality of celestial body categories;
calculating the distance between every two celestial bodies under the same celestial body class according to the coordinates of the celestial bodies, taking each celestial body as a node, correlating every two celestial bodies belonging to the same celestial body class, and constructing to obtain a plurality of knowledge maps; the constructing a plurality of knowledge maps comprises the following steps: obtaining the weight of the edge between the nodes of the knowledge graph according to the distance between every two celestial bodies under the same celestial body class, and obtaining a plurality of knowledge graphs according to the weight of the edge and the nodes;
and acquiring the spectral characteristics of each celestial body, and associating any celestial body between the first knowledge graph and the second knowledge graph according to the spectral characteristics.
2. The method of claim 1, wherein calculating a distance between each two celestial bodies in the same celestial body class from coordinates of the celestial bodies comprises:
obtaining a midpoint position between a first celestial body and a second celestial body according to the coordinates of the first celestial body and the coordinates of the second celestial body, and obtaining a linear distance from the earth to the midpoint position, wherein the coordinates comprise coordinates under an equatorial coordinate system or coordinates under a yellow track coordinate system;
taking the linear distance as a sphere radius, and calculating a semi-normal vector value of a central angle of the first celestial body and the second celestial body in the sphere;
and acquiring the celestial distance between the first celestial body and the second celestial body according to the semi-normal value and the linear distance, wherein the semi-normal value is the ratio of the celestial distance to the linear distance.
3. The method of claim 1, wherein the spectral features of the celestial body include light intensity and frequency distribution.
4. The method of claim 1, wherein obtaining coordinates of a celestial body comprises:
and judging whether the coordinate system of the celestial body is an equatorial coordinate system or not, if not, acquiring a conversion relation between the coordinate system of the celestial body and the equatorial coordinate system, and acquiring the coordinate of the celestial body under the equatorial coordinate system based on the conversion relation.
5. The method according to claim 4, wherein after determining whether the coordinate system in which the coordinates of the celestial body are located is an equatorial coordinate system, and if not, converting the coordinates of the celestial body based on the conversion relationship, the method further comprises:
and judging whether the observation time of the coordinates of the celestial body is the preset time, if not, converting the coordinates of the celestial body of the observation time in the equatorial coordinate system into the coordinates of the celestial body of the preset time in the equatorial coordinate system.
6. The method of claim 1, wherein constructing a first knowledge-graph from the celestial coordinates and distances comprises:
obtaining nodes of the first knowledge graph according to the celestial coordinates, and obtaining the weights of edges in the first knowledge graph according to the distances;
and constructing the first knowledge graph according to the weights of the nodes and the edges.
7. The method of claim 6, wherein constructing a first knowledge-graph from the celestial coordinates and distances, further comprises:
acquiring the attribute of a celestial body, and taking the attribute of the celestial body as the attribute of a node in the first knowledge graph;
and constructing the first knowledge graph according to the node, the weight of the edge and the attribute of the node.
8. A method for searching resources, the method comprising:
displaying celestial body identifiers corresponding to the first resources in an interactive interface;
and returning a second resource in response to the access operation to the celestial body identifier, wherein the first resource and the second resource establish an association relationship based on a knowledge graph, and the knowledge graph is generated according to the astronomical knowledge graph construction method of any one of claims 1 to 7.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any one of claims 1 to 7.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108959627A (en) * | 2018-07-23 | 2018-12-07 | 北京光年无限科技有限公司 | Question and answer exchange method and system based on intelligent robot |
| WO2019057190A1 (en) * | 2017-09-25 | 2019-03-28 | 腾讯科技（深圳）有限公司 | Method and apparatus for displaying knowledge graph, terminal device, and readable storage medium |
| CN110489561A (en) * | 2019-07-12 | 2019-11-22 | 平安科技（深圳）有限公司 | Knowledge mapping construction method, device, computer equipment and storage medium |
| CN110941664A (en) * | 2019-12-11 | 2020-03-31 | 北京百度网讯科技有限公司 | Knowledge graph construction method, detection method, device, equipment and storage medium |
| CN112862928A (en) * | 2021-02-24 | 2021-05-28 | 北京天文馆 | Astronomical data visualization method and device, computer equipment and readable storage medium |
| WO2021190091A1 (en) * | 2020-03-26 | 2021-09-30 | 深圳壹账通智能科技有限公司 | Knowledge map construction method and device based on knowledge node belonging degree |
| CN114282585A (en) * | 2020-09-17 | 2022-04-05 | 南京邮电大学 | Astronomical spectrum data-based outlier celestial body classification method |
| CN114546550A (en) * | 2022-01-24 | 2022-05-27 | 太原科技大学 | Automatic visual analysis method and system for celestial body spectral data |
| CN114693878A (en) * | 2020-12-31 | 2022-07-01 | 阿里巴巴集团控股有限公司 | Simulated sky image generation method and device, and map generation method and device |
| CN114863440A (en) * | 2022-04-14 | 2022-08-05 | 广州欢聚时代信息科技有限公司 | Order data processing method and device, equipment, medium and product thereof |
| CN115048536A (en) * | 2022-07-07 | 2022-09-13 | 南方电网大数据服务有限公司 | Knowledge graph generation method and device, computer equipment and storage medium |
| CN115080765A (en) * | 2022-07-20 | 2022-09-20 | 北京无线电测量研究所 | Aerospace quality knowledge map construction method, system, medium and equipment |
| CN115795061A (en) * | 2023-02-13 | 2023-03-14 | 京华信息科技股份有限公司 | Knowledge graph construction method and system based on word vectors and dependency syntax |
| WO2023065211A1 (en) * | 2021-10-21 | 2023-04-27 | 华为技术有限公司 | Information acquisition method and apparatus |
| CN116451784A (en) * | 2023-03-02 | 2023-07-18 | 杭州中奥科技有限公司 | Feature expression method and system of knowledge graph and electronic equipment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7752243B2 (en) * | 2006-06-06 | 2010-07-06 | University Of Regina | Method and apparatus for construction and use of concept knowledge base |
-
2023
- 2023-09-13 CN CN202311179452.6A patent/CN116932780B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019057190A1 (en) * | 2017-09-25 | 2019-03-28 | 腾讯科技（深圳）有限公司 | Method and apparatus for displaying knowledge graph, terminal device, and readable storage medium |
| CN108959627A (en) * | 2018-07-23 | 2018-12-07 | 北京光年无限科技有限公司 | Question and answer exchange method and system based on intelligent robot |
| CN110489561A (en) * | 2019-07-12 | 2019-11-22 | 平安科技（深圳）有限公司 | Knowledge mapping construction method, device, computer equipment and storage medium |
| CN110941664A (en) * | 2019-12-11 | 2020-03-31 | 北京百度网讯科技有限公司 | Knowledge graph construction method, detection method, device, equipment and storage medium |
| WO2021190091A1 (en) * | 2020-03-26 | 2021-09-30 | 深圳壹账通智能科技有限公司 | Knowledge map construction method and device based on knowledge node belonging degree |
| CN114282585A (en) * | 2020-09-17 | 2022-04-05 | 南京邮电大学 | Astronomical spectrum data-based outlier celestial body classification method |
| CN114693878A (en) * | 2020-12-31 | 2022-07-01 | 阿里巴巴集团控股有限公司 | Simulated sky image generation method and device, and map generation method and device |
| CN112862928A (en) * | 2021-02-24 | 2021-05-28 | 北京天文馆 | Astronomical data visualization method and device, computer equipment and readable storage medium |
| WO2023065211A1 (en) * | 2021-10-21 | 2023-04-27 | 华为技术有限公司 | Information acquisition method and apparatus |
| CN114546550A (en) * | 2022-01-24 | 2022-05-27 | 太原科技大学 | Automatic visual analysis method and system for celestial body spectral data |
| CN114863440A (en) * | 2022-04-14 | 2022-08-05 | 广州欢聚时代信息科技有限公司 | Order data processing method and device, equipment, medium and product thereof |
| CN115048536A (en) * | 2022-07-07 | 2022-09-13 | 南方电网大数据服务有限公司 | Knowledge graph generation method and device, computer equipment and storage medium |
| CN115080765A (en) * | 2022-07-20 | 2022-09-20 | 北京无线电测量研究所 | Aerospace quality knowledge map construction method, system, medium and equipment |
| CN115795061A (en) * | 2023-02-13 | 2023-03-14 | 京华信息科技股份有限公司 | Knowledge graph construction method and system based on word vectors and dependency syntax |
| CN116451784A (en) * | 2023-03-02 | 2023-07-18 | 杭州中奥科技有限公司 | Feature expression method and system of knowledge graph and electronic equipment |
Non-Patent Citations (2)
| Title |
|---|
| 基于知识图谱的信息查询系统设计与实现;杨荣;翟社平;王志文;;计算机与数字工程(第04期);138-142、175 * |
| 网格聚类分析天文光谱数据;陈淑鑫;孙伟民;王丽丽;;计算机科学(第S2期);463-466 * |
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