CN114153928A - Method, system, equipment and medium for constructing urban geographic semantic knowledge network - Google Patents
Method, system, equipment and medium for constructing urban geographic semantic knowledge network Download PDFInfo
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Abstract
The invention provides a method, a system, equipment and a storage medium for constructing a city geographic semantic knowledge network, wherein the method comprises the following steps: the server acquires geographic entity data and constructs a geographic entity data set according to the geographic entity data; the server classifies the geographic entity data set according to at least one preset characteristic to obtain classified geographic entity data; the server constructs a semantic relation model about the geographic entity; the server acquires semantic relations among the geographic entity data based on the semantic relation model and the classified geographic entity data; the server constructs a geographic knowledge network based on the graph database, the semantic relation and the geographic entity data set; the geographic knowledge network is constructed based on the graph database, and the method and the device are favorable for providing richer and more complete geographic position information for various geographic information services.
Description
Technical Field
The invention relates to the technical field of geographical entity knowledge maps, in particular to a method, a system, equipment and a medium for constructing a city geographical semantic knowledge network.
Background
The address data is used as basic application data, is closely related to daily life of people, and is ubiquitous in various industries and various fields. The geographic entity is the basis of intelligent geographic information intelligent service, constructs a geographic knowledge map, and can provide richer and more complete geographic position information for various geographic information services. How to effectively utilize the spatial and semantic relation of geographic data to realize the conversion of data-information-knowledge and further realize the construction of a geographic knowledge graph is a problem faced at present.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a method, a system, equipment and a storage medium for constructing a city geographic semantic knowledge network, which realize the construction of a geographic knowledge graph by utilizing the spatial and semantic relation of geographic data.
In order to achieve the purpose, the invention provides a method for constructing a city geographic semantic knowledge network, which comprises the following steps:
the server acquires geographic entity data and constructs a geographic entity data set according to the geographic entity data;
the server classifies the geographic entity data set according to at least one preset characteristic to obtain classified geographic entity data;
the server constructs a semantic relation model about the geographic entity;
the server acquires semantic relations among the geographic entity data based on the semantic relation model and the classified geographic entity data; and
and the server constructs a geographical knowledge network based on the graph database, the semantic relation and the geographical entity data set.
Optionally, the geographic entity data includes attribute information; the server constructs a geographic knowledge network based on the graph database, the semantic relationship and the geographic entity data set, and comprises the following steps:
and the server constructs a geographical knowledge network based on the graph database, the attribute information, the semantic relationship and the geographical entity data set.
Optionally, the geographic entity data includes latitude and longitude information; the server obtains the semantic relation between each geographic entity data based on the semantic relation model and the classified geographic entity data, and the semantic relation comprises the following steps:
and the server acquires the semantic relation among the geographic entity data based on the semantic relation model, the longitude and latitude information and the classified geographic entity data.
Optionally, the preset features comprise city administrative divisions, and the semantic relationship model comprises a hierarchical relationship model associated with the preset features; the server obtains the semantic relation between each geographic entity data based on the semantic relation model and the classified geographic entity data, and the semantic relation comprises the following steps:
and the server acquires semantic relations among the geographic entity data based on the hierarchical relation model and the classified geographic entity data.
Optionally, the method further comprises the step of:
the method comprises the steps that a server receives a first query request about a geographic entity sent by a user terminal;
the server acquires second geographic entity data matched with the first query request based on the geographic knowledge network;
the server responds to the first query request based on the second geographic entity data.
Optionally, the method further comprises the step of:
the server acquires historical search behavior data associated with geographic entity data;
the server extracts effective feedback data from the historical search behavior data;
the server determines a target geographic entity needing to be updated and a corresponding semantic relation according to the effective feedback data;
and the server updates the geographic knowledge network based on the target geographic entity and the corresponding semantic relationship.
Optionally, the method further comprises the step of:
the server receives a preset trigger signal which is generated by the user terminal and is based on the second geographic entity data;
and the server re-matches the preset trigger signal and the equivalent semantic relation in the semantic relation to obtain third geographic entity data, and recommends the third geographic entity data to the user terminal.
Optionally, the semantic relationship model includes an orientation relationship model, a distance relationship model, a topological relationship model, an attribution relationship model and an equivalence relationship model.
The invention also provides a construction system of the urban geographic semantic knowledge network, which is used for realizing the construction method of the urban geographic semantic knowledge network, and the system comprises the following steps:
the server acquires geographic entity data and constructs a geographic entity data set according to the geographic entity data;
the geographic entity data classification module is used for classifying the geographic entity data set according to at least one preset characteristic by the server to obtain classified geographic entity data;
the server constructs a semantic relation model related to the geographic entity;
the semantic relation acquisition module is used for acquiring the semantic relation among the geographic entity data by the server based on the semantic relation model and the classified geographic entity data; and
and the server constructs the geographical knowledge network based on the graph database, the semantic relationship and the geographical entity data set.
The invention also provides a construction device of the urban geographic semantic knowledge network, which comprises the following steps:
a processor;
a memory having stored therein an executable program of the processor;
wherein the processor is configured to perform the steps of any one of the above-described methods of constructing a municipal geo-semantic knowledge network via execution of the executable program.
The invention also provides a computer-readable storage medium for storing a program which, when executed by a processor, implements the steps of any one of the above-described methods for constructing a network of urban geographic semantic knowledge.
Compared with the prior art, the invention has the following advantages and prominent effects:
the method, the system, the equipment and the medium for constructing the urban geographic semantic knowledge network provided by the invention are used for constructing the semantic relation model about the geographic entity, classifying the constructed geographic entity data set, determining the semantic relation among the geographic entities according to the classification result and the semantic relation model, further realizing the construction of the geographic knowledge network based on the map database, and being beneficial to providing richer and more complete geographic position information for various geographic information services.
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Other features, objects and advantages of the present invention will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of a method for constructing a city geographic semantic knowledge network according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a classification result obtained in the method for constructing a city geographic semantic knowledge network according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a hierarchical relationship model in the method for constructing a geographic semantic knowledge network of a city according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a method for constructing a city geographic semantic knowledge network according to another embodiment of the present invention;
FIG. 5 is a schematic diagram of a geographic knowledge network constructed in the method for constructing a city geographic semantic knowledge network according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a method for constructing a city geographic semantic knowledge network according to another embodiment of the present invention;
FIG. 7 is a schematic diagram of a method for constructing a city geographic semantic knowledge network according to another embodiment of the present invention;
FIG. 8 is a schematic structural diagram of a system for constructing a city geographic semantic knowledge network according to an embodiment of the present invention;
FIG. 9 is a schematic structural diagram of a system for building a city geographic semantic knowledge network according to another embodiment of the present invention;
FIG. 10 is a schematic structural diagram of a system for building a city geographic semantic knowledge network according to another embodiment of the present invention;
FIG. 11 is a schematic structural diagram of a system for building a city geographic semantic knowledge network according to another embodiment of the present invention;
fig. 12 is a schematic structural diagram of a device for constructing a city geographic semantic knowledge network according to an embodiment of the present invention;
fig. 13 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus their repetitive description will be omitted.
As shown in fig. 1, an embodiment of the present invention discloses a method for constructing a city geographic semantic knowledge network, which includes the following steps:
s110, the server acquires geographic entity data and constructs a geographic entity data set according to the geographic entity data. Specifically, the server may obtain the geographic entity data by using the existing technology, such as extracting the geographic entity from the network information by using a natural language processing technology, or constructing the geographic entity data set according to the geographic entity data provided by different geographic entity suppliers.
And S120, classifying the geographic entity data set according to at least one preset characteristic by the server to obtain classified geographic entity data. Specifically, the preset features may include city administrative divisions or points of interest, for example. Referring to fig. 2, exemplary results classified according to administrative divisions or points of Interest (POIs) are shown. For example, the administrative division classifies the geographic entity data in the geographic entity data set according to a first layer to a sixth layer, where the first layer is, for example, a city/district/county, the second layer is, for example, a town street, the third layer is, for example, a community village, the fourth layer is, for example, a street lane, the fifth layer is, for example, a building seat, and the sixth layer is, for example, a unit level room.
With continued reference to fig. 2, the points of interest are classified according to the categories of points of interest, such as points of interest in living quarters, transportation facilities, cultural education, shopping centers, and financial places.
It should be noted that, the preset features referred to in the classification of the step in the present application are independent and unrelated to each other. For example, the reference classification preset features include two features of city administrative divisions and interest points, and the two obtained classification results are also independent of each other. The objects of each classification feature classification are the same geographic entity data. This step may be classified according to a plurality of predetermined characteristics.
S130, the server constructs a semantic relation model about the geographic entity. In this embodiment, the semantic relationship model includes a hierarchical relationship model, an orientation relationship model, a distance relationship model, a topological relationship model, an attribution relationship model, and an equivalence relationship model. The hierarchical relationship model describes a spatial upper-lower level semantic relationship, and referring to fig. 3, an exemplary hierarchical relationship model is disclosed, which discloses only the hierarchical relationship of a three-layer structure, but the present application is not limited to this structure. The first layer is Yangzhou city, the second layer is Gaoyou city, Jiangdu district and/37015and the third layer is XX town, XX community and XX street.
The orientation relation model describes a semantic relation of relative positions among geographic entity elements, and can comprise east, west, south, north, southeast, southwest, northeast, northwest and the like.
The topological relation model describes a class of semantic relations of the proximity and the correlation degree between points, lines and surfaces of geographic entities, and can comprise various relations of adjacent, side, adjacent, close, opposite, separated, equal, downstairs, underbridge, crossing and the like.
The attribution relation model is a partial/whole relation model and describes a semantic relation between a partial part of the geographic entity and the whole. For example, a certain district has a first stage, a second stage and a third stage, and a certain office building comprises a seat A, a seat B and a seat C. The above-mentioned cells and the first, second and third phases are then in a whole and in part relation.
The distance relationship model describes a semantic relationship of distance and affinity of the geographic entity, and can include various relationships such as close, moderate, far and the like.
The equivalence relation model describes equivalent and similar semantic relations among geographic entities. Illustratively, the geographic entity yellow river is equivalent to the mother river, for example. The Beijing university of transportation of the geographic entity is equivalent to the Beijing university of the geographic entity and is also equivalent to the Beijing university of the geographic entity.
And S140, the server acquires the semantic relation among the geographic entity data based on the semantic relation model and the classified geographic entity data.
In one embodiment, the semantic relationship between the semantic relationship model and the finally determined geographic entity is in one-to-one correspondence, and all the determined semantic relationships are combined to form a semantic relationship set. In the subsequent steps, a geographical knowledge network is constructed based on the semantic relation set.
In this embodiment, for a part of semantic relationships, such as a topological relationship and an equivalence relationship, prior knowledge may be introduced, and the semantic relationship corresponding to each geographic entity and the part of the model is determined by combining the established corresponding semantic relationship model.
In an embodiment of the application, the geographic entity data includes latitude and longitude information. In step S140, based on the latitude and longitude information and the orientation relationship model and distance relationship model established in the above steps, the orientation relationship and distance relationship between the corresponding geographic entities are determined.
For example, in an embodiment, the semantic relationship model constructed in step S130 is a hierarchical relationship model, the preset features in step S120 are city administrative divisions, and step S120 classifies the geographic entity data according to the administrative divisions, so that the classified geographic entity data is mapped to the semantic relationship model in this step, and the hierarchical semantic relationship between the geographic entities is obtained. The present application is not limited thereto.
And S150, the server constructs a geographic knowledge network based on the graph database, the semantic relationship and the geographic entity data set. Specifically, in the step, the server stores and constructs the geographical knowledge network in the form of a graph database based on the semantic relationship and the geographical entity data set.
In another embodiment of the application, another method for constructing a city geographic semantic knowledge network is disclosed. As shown in fig. 4, in this embodiment, the geographic entity data includes attribute information and latitude and longitude information. The method further includes steps S160 and S170 in addition to steps S110, S120, and S130. Wherein, step S160 is:
and the server acquires the semantic relationship among the geographic entities based on the semantic relationship model, the longitude and latitude information and the classified geographic entity data.
Step S170 is:
and the server constructs a geographical knowledge network based on the graph database, the attribute information, the semantic relationship and the geographical entity data set.
Illustratively, the Beijing university of transportation is taken as an example, and has an academic department attribute, a 211 university attribute, an office university attribute, a university of the department of education, and the like. In this embodiment, as shown in fig. 5, the finally constructed geographic knowledge network shows attribute information of each geographic entity and semantic relationships between the geographic entities. The 4 attributes that the geographic entity "Beijing university of transportation" contains are shown in FIG. 5: science of science, 211 university, office university and department of education directly. In fig. 5, the hai lake area, the west directoral street and the beijing university of transportation form a hierarchical relationship of a three-layer structure. The hai lake region and the western city region have adjacent topological semantic relations and a close distance semantic relation. The Beijing university of transportation is at the east of the west kingdom subway station, so the Beijing university of transportation and the west kingmen subway station form a relevant bearing relation. Because the new street crossing subway station comprises a new street crossing subway station port A and a new street crossing subway station port B, a partial/whole semantic relation is formed among the new street crossing subway station port A, the new street crossing subway station port B and the new street crossing subway station port A.
In another embodiment of the application, another method for constructing a city geographic semantic knowledge network is disclosed. As shown in fig. 6, the method further includes, on the basis of the above embodiment, the steps of:
s180, the server receives a first query request about the geographic entity sent by the user terminal.
And S190, the server acquires second geographic entity data matched with the first query request based on the geographic knowledge network.
And S200, the server responds to the first query request based on the second geographic entity data.
Therefore, geographic information service can be provided for users according to the constructed geographic knowledge network. Illustratively, step S120 classifies the geo-entity data set also based on the points of interest in this embodiment. For example, the classified interest points include transportation facilities, the sub-classes of subway stations are included below the transportation facilities, and when a user queries nearby subway stations based on a geographic entity, the user can directly query according to the interest points, so that the query matching efficiency is improved.
In another embodiment of the application, another method for constructing a city geographic semantic knowledge network is disclosed. As shown in fig. 7, the method further includes, on the basis of the above embodiment, the steps of:
s210, the server acquires historical search behavior data associated with geographic entity data.
S220, the server searches the behavior data from the history and extracts effective feedback data.
And S230, the server determines the target geographic entity needing to be updated and the corresponding semantic relationship according to the effective feedback data.
S240, the server updates the geographic knowledge network based on the target geographic entity and the corresponding semantic relationship.
Illustratively, when the effective feedback data of the user is greater than a preset threshold, it indicates that the user generally reflects that a certain geographic entity is matched with a mistake, such as: the geographic entity "hua shi" is not equivalent to the geographic entity "master university of east china", but is equivalent to the geographic entity "master university of china". Then the equivalent semantic relationship between the geographic entity "hua shi university" and "huadong scholar university" needs to be deleted, and the equivalent semantic relationship between the geographic entity "hua shi university" and "huazhong scholar university" needs to be established.
In another embodiment of the application, another method for constructing a city geographic semantic knowledge network is disclosed. On the basis of the above embodiment, the method further comprises the steps of:
and the server receives a preset trigger signal which is generated by the user terminal and is based on the second geographic entity data. And
and the server re-matches the preset trigger signal and the equivalent semantic relation in the semantic relation to obtain third geographic entity data, and recommends the third geographic entity data to the user terminal.
For example, the preset trigger signal is a trigger signal corresponding to the return button, the geographic entity corresponding to the first query request sent by the user terminal may be "hua", and there may be an equivalent semantic relationship between the "hua" and the two geographic entities, and the second geographic entity data obtained by matching according to the geographic knowledge network is "university of east china". When the return operation is received, which indicates that the user considers that the matching result is not appropriate, the result is recommended to the user according to another geographic entity data 'university in china' matched with the 'master'.
It should be noted that all the above embodiments disclosed in the present application can be freely combined, and the technical solutions obtained by combining them are also within the scope of the present application.
As shown in fig. 8, an embodiment of the present invention further discloses a system 8 for constructing a city geographic semantic knowledge network, which includes:
the geographic entity data obtaining module 81 obtains geographic entity data from the server, and constructs a geographic entity data set according to the geographic entity data.
The geographic entity data classification module 82 classifies the geographic entity data set according to at least one preset feature by the server to obtain classified geographic entity data.
And a semantic relation model building module 83, wherein the server builds a semantic relation model about the geographic entity.
The semantic relation obtaining module 84 obtains the semantic relation between the geographic entity data based on the semantic relation model and the classified geographic entity data. And
and a geographic knowledge network construction module 85, which constructs a geographic knowledge network based on the graph database, the semantic relationship and the geographic entity data set.
As shown in fig. 9, another embodiment of the present invention further discloses a building system 9 of a city geographic semantic knowledge network, which includes, on the basis of the geographic entity data obtaining module 81, the geographic entity data classifying module 82 and the semantic relationship model building module 83, the system further includes:
the second semantic relation module 91, the server constructs the geographic knowledge network based on the graph database, the attribute information, the semantic relation and the geographic entity data set.
The second geographic knowledge network construction module 92, the server constructs the geographic knowledge network based on the graph database, the semantic relationship and the geographic entity data set.
As shown in fig. 10, another embodiment of the present invention further discloses a system 10 for constructing a city geographic semantic knowledge network, which on the basis of the above embodiment, further includes:
and the query request receiving module 86 is used for receiving a first query request about the geographic entity sent by the user terminal.
And a second geographic entity obtaining module 87, configured to obtain, by the server, second geographic entity data matched with the first query request based on the geographic knowledge network.
The request response module 88, the server, based on the second geographic entity data, responds to the first query request.
As shown in fig. 11, another embodiment of the present invention further discloses a system 11 for constructing a city geographic semantic knowledge network, which on the basis of the above embodiment further includes:
the search behavior data obtaining module 93 obtains historical search behavior data associated with geographic entity data.
And a feedback data extraction module 94, wherein the server extracts effective feedback data from the historical search behavior data.
And a semantic relationship updating module 95, which determines the target geographic entity to be updated and the corresponding semantic relationship according to the effective feedback data.
And a knowledge network updating module 96, wherein the server updates the geographical knowledge network based on the target geographical entity and the corresponding semantic relationship.
It can be understood that the construction system of the urban geographic semantic knowledge network further comprises other existing functional modules for supporting the operation of the construction system of the urban geographic semantic knowledge network. The system for constructing the urban geographic semantic knowledge network shown in fig. 8 to 11 is only an example, and should not bring any limitation to the functions and the scope of the embodiments of the present invention.
The construction system of the urban geographic semantic knowledge network in this embodiment is used to implement the above method for constructing the urban geographic semantic knowledge network, so for the specific implementation steps of the construction system of the urban geographic semantic knowledge network, reference may be made to the description of the method for constructing the urban geographic semantic knowledge network, which is not described herein again.
The embodiment of the invention also discloses construction equipment of the urban geographic semantic knowledge network, which comprises a processor and a memory, wherein the memory stores an executable program of the processor; the processor is configured to execute the steps in the method for building a city geographical semantic knowledge network described above via execution of an executable program. FIG. 12 is a schematic structural diagram of a building device of the urban geographic semantic knowledge network disclosed by the invention. An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 12. The electronic device 600 shown in fig. 12 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 12, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting the different platform components (including the memory unit 620 and the processing unit 610), a display unit 640, etc.
Wherein the storage unit stores program code which can be executed by the processing unit 610 to cause the processing unit 610 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned method for constructing a network of urban geographical semantic knowledge. For example, processing unit 610 may perform the steps as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, to name a few.
The invention also discloses a computer readable storage medium for storing a program, wherein the program realizes the steps in the construction method of the urban geographic semantic knowledge network when being executed. In some possible embodiments, the aspects of the present invention may also be implemented in the form of a program product, which includes program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned construction method of a city geographic semantic knowledge network of this specification, when the program product is run on the terminal device.
As described above, when the program of the computer-readable storage medium of this embodiment is executed, the semantic relationship model about the geographic entity is constructed, the constructed geographic entity data set is classified, the semantic relationship between the geographic entities is determined according to the classification result and the semantic relationship model, and then the geographic knowledge network is constructed based on the graph database, which is beneficial to providing richer and more complete geographic location information for various geographic information services.
Fig. 13 is a schematic structural diagram of a computer-readable storage medium of the present invention. Referring to fig. 13, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
According to the method, the system, the equipment and the storage medium for constructing the urban geographic semantic knowledge network, provided by the embodiment of the invention, through constructing the semantic relation model about the geographic entities, classifying the constructed geographic entity data set, determining the semantic relation among the geographic entities according to the classification result and the semantic relation model, further realizing the construction of the geographic knowledge network based on the map database, and being beneficial to providing richer and more complete geographic position information for various geographic information services.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (11)
1. A construction method of a city geographic semantic knowledge network is characterized by comprising the following steps:
the server acquires geographic entity data and constructs a geographic entity data set according to the geographic entity data;
the server classifies the geographic entity data set according to at least one preset characteristic to obtain classified geographic entity data;
the server constructs a semantic relation model about the geographic entity;
the server acquires semantic relations among the geographic entity data based on the semantic relation model and the classified geographic entity data; and
and the server constructs a geographical knowledge network based on the graph database, the semantic relation and the geographical entity data set.
2. The method for constructing the urban geographic semantic knowledge network according to claim 1, wherein the geographic entity data comprises attribute information; the server constructs a geographic knowledge network based on the graph database, the semantic relationship and the geographic entity data set, and comprises the following steps:
and the server constructs a geographical knowledge network based on the graph database, the attribute information, the semantic relationship and the geographical entity data set.
3. The method for constructing the urban geographic semantic knowledge network according to claim 1, wherein the geographic entity data comprises longitude and latitude information; the server obtains the semantic relation between each geographic entity data based on the semantic relation model and the classified geographic entity data, and the semantic relation comprises the following steps:
and the server acquires the semantic relation among the geographic entity data based on the semantic relation model, the longitude and latitude information and the classified geographic entity data.
4. The method for constructing the urban geographic semantic knowledge network according to claim 1, wherein the preset features comprise urban administrative divisions, and the semantic relationship model comprises a hierarchical relationship model associated with the preset features; the server obtains the semantic relation between each geographic entity data based on the semantic relation model and the classified geographic entity data, and the semantic relation comprises the following steps:
and the server acquires semantic relations among the geographic entity data based on the hierarchical relation model and the classified geographic entity data.
5. The method for constructing the urban geographic semantic knowledge network according to claim 1, further comprising the steps of:
the method comprises the steps that a server receives a first query request about a geographic entity sent by a user terminal;
the server acquires second geographic entity data matched with the first query request based on the geographic knowledge network;
the server responds to the first query request based on the second geographic entity data.
6. The method for constructing the urban geographic semantic knowledge network according to claim 1, further comprising the steps of:
the server acquires historical search behavior data associated with geographic entity data;
the server extracts effective feedback data from the historical search behavior data;
the server determines a target geographic entity needing to be updated and a corresponding semantic relation according to the effective feedback data;
and the server updates the geographic knowledge network based on the target geographic entity and the corresponding semantic relationship.
7. The method for constructing the urban geographic semantic knowledge network according to claim 5, wherein the method further comprises the steps of:
the server receives a preset trigger signal which is generated by the user terminal and is based on the second geographic entity data;
and the server re-matches the preset trigger signal and the equivalent semantic relation in the semantic relation to obtain third geographic entity data, and recommends the third geographic entity data to the user terminal.
8. The method for building the urban geographic semantic knowledge network according to claim 1, wherein the semantic relationship model comprises an orientation relationship model, a distance relationship model, a topological relationship model, an attribution relationship model and an equivalence relationship model.
9. A construction system of a city geographical semantic knowledge network, for implementing the construction method of the city geographical semantic knowledge network according to claim 1, wherein the system comprises:
the server acquires geographic entity data and constructs a geographic entity data set according to the geographic entity data;
the geographic entity data classification module is used for classifying the geographic entity data set according to at least one preset characteristic by the server to obtain classified geographic entity data;
the server constructs a semantic relation model related to the geographic entity;
the semantic relation acquisition module is used for acquiring the semantic relation among the geographic entity data by the server based on the semantic relation model and the classified geographic entity data; and
and the server constructs the geographical knowledge network based on the graph database, the semantic relationship and the geographical entity data set.
10. An apparatus for constructing a city geographic semantic knowledge network, comprising:
a processor;
a memory having stored therein an executable program of the processor;
wherein the processor is configured to execute the steps of the method for constructing a city geographical semantic knowledge network according to any one of claims 1 to 8 via execution of the executable program.
11. A computer-readable storage medium for storing a program, wherein the program, when executed by a processor, implements the steps of the method for constructing a city geographical semantic knowledge network according to any one of claims 1 to 8.
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