CN112818261A - Navigation method and device based on POI (Point of interest) knowledge graph and electronic equipment - Google Patents
Navigation method and device based on POI (Point of interest) knowledge graph and electronic equipment Download PDFInfo
- Publication number
- CN112818261A CN112818261A CN202110111132.1A CN202110111132A CN112818261A CN 112818261 A CN112818261 A CN 112818261A CN 202110111132 A CN202110111132 A CN 202110111132A CN 112818261 A CN112818261 A CN 112818261A
- Authority
- CN
- China
- Prior art keywords
- poi
- knowledge
- graph
- knowledge graph
- entity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000012545 processing Methods 0.000 claims description 27
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 238000004422 calculation algorithm Methods 0.000 claims description 5
- 238000011835 investigation Methods 0.000 claims description 2
- 238000004140 cleaning Methods 0.000 abstract 1
- 238000007405 data analysis Methods 0.000 abstract 1
- 238000011176 pooling Methods 0.000 description 5
- 239000011159 matrix material Substances 0.000 description 4
- 239000013598 vector Substances 0.000 description 4
- 238000013527 convolutional neural network Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 230000009193 crawling Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000004087 circulation Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/288—Entity relationship models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
Abstract
The embodiment of the invention discloses a navigation method, a navigation device and electronic equipment based on a POI (point of interest) knowledge graph, wherein the navigation method comprises the following steps: obtaining POI basic data and POI related information; carrying out data cleaning and data analysis on the POI related information to obtain a POI characteristic entity; establishing an exclusive knowledge graph of each POI according to the POI characteristic entity and the POI basic data; constructing a POI knowledge map library based on the exclusive knowledge map of each POI; and navigating based on the POI knowledge map library. The invention can still perform navigation when the user forgets the name and address of the POI.
Description
Technical Field
The embodiment of the invention relates to the technical field of geographic information, in particular to a navigation method and device based on a POI (point of interest) knowledge graph and electronic equipment.
Background
Currently, when navigating, a user must know a name or an address of a Point of Interest (POI) of a destination first to search using navigation. However, the user sometimes forgets or does not know the name and address of the destination POI, only knows some scenes or features of the destination POI, and cannot navigate.
It is a problem to be solved urgently if navigation can still be performed when the user forgets the name and address of the destination POI.
Disclosure of Invention
The embodiment of the invention aims to provide a navigation method, a navigation device and electronic equipment of a POI (point of interest) knowledge graph, which are used for solving the problem that navigation cannot be carried out if the name or address of a destination is unknown in the conventional navigation process.
In order to achieve the above object, the embodiments of the present invention mainly provide the following technical solutions:
in a first aspect, an embodiment of the present invention provides a navigation method based on a POI knowledge graph, including:
obtaining POI basic data and POI related information;
extracting a POI characteristic entity according to the POI related information;
establishing an exclusive knowledge graph of each POI according to the POI characteristic entity and the POI basic data;
constructing a POI knowledge map library based on the exclusive knowledge map of each POI;
and navigating based on the POI knowledge map library.
According to an embodiment of the present invention, extracting a POI feature entity according to the POI related information includes:
and carrying out data duplication removal and text processing on the POI related information to obtain the POI characteristic entity.
According to an embodiment of the present invention, establishing a specific knowledge graph of each POI according to the POI feature entities and the POI base data includes:
unifying names of POI characteristic entities representing the same entity;
and establishing an exclusive knowledge graph of each POI according to each POI and the associated characteristic entity.
According to one embodiment of the invention, a deep neural network algorithm is used in the specific knowledge graph of each POI to calculate the correlation degree between each POI and the associated feature entity.
According to an embodiment of the invention, the constructing of the POI knowledge map library based on the specific knowledge map of each POI comprises:
and removing the duplication of the same POI characteristic entities in the POI knowledge map library, and classifying the POI characteristic entities of the same category.
According to one embodiment of the invention, navigating based on a POI knowledge map library comprises:
acquiring a keyword for navigation search;
obtaining the sequence of the multiple POI basic data according to the relevancy of the feature entities corresponding to the key words;
determining target POI basic data based on the sorting result;
and navigating according to the current position of the navigation terminal and the basic data of the target POI.
According to an embodiment of the present invention, the POI-related information is obtained through a network or manually edited after being investigated by an entity.
In a second aspect, an embodiment of the present invention further provides a navigation device based on a POI knowledge graph, including:
the acquisition module is used for acquiring POI basic data and POI related information;
a storage module;
the control processing module is used for extracting a POI characteristic entity according to the POI related information; the control processing module is further used for establishing an exclusive knowledge map of each POI according to the POI characteristic entity and the POI basic data; the control processing module is also used for constructing a POI knowledge map library based on the exclusive knowledge map of each POI through the storage module;
and the navigation module is used for navigating based on the POI knowledge map library.
According to an embodiment of the present invention, the control processing module is configured to perform data deduplication and text processing on the POI related information to obtain the POI feature entity.
According to an embodiment of the present invention, the control processing module is configured to perform data deduplication and text processing on the POI related information to obtain the POI feature entity.
According to an embodiment of the present invention, the control processing module is further configured to unify names of POI feature entities representing the same entity, and establish an exclusive knowledge graph of each POI according to each POI and an associated feature entity.
According to an embodiment of the present invention, the control processing module is further configured to calculate a degree of correlation between each POI and the associated feature entity using a deep neural network algorithm in the dedicated knowledge graph of each POI.
According to an embodiment of the invention, the control processing module is configured to perform deduplication on the same POI feature entities in the POI knowledge map library, and classify POI feature entities of the same category.
According to one embodiment of the invention, the navigation module is used for acquiring a keyword for navigation search; the navigation module is further used for obtaining the ranking of the multiple POI basic data according to the relevancy of the feature entities corresponding to the key words; the navigation module is further used for determining target POI basic data based on the sorting result; and the navigation module is also used for navigating according to the current position of the navigation terminal and the basic data of the target POI.
According to an embodiment of the present invention, the POI-related information is obtained through a network or manually edited after being investigated by an entity.
In a third aspect, an embodiment of the present invention further provides an electronic device, including: at least one processor and at least one memory; the memory is to store one or more program instructions; the processor is configured to execute one or more program instructions to perform the POI knowledge-graph based navigation method according to the first aspect.
In a fourth aspect, embodiments of the present invention further provide a computer-readable storage medium containing one or more program instructions for executing the POI knowledge-graph based navigation method according to the first aspect.
The technical scheme provided by the embodiment of the invention at least has the following advantages:
the navigation method, the navigation device and the electronic equipment based on the POI knowledge graph provided by the embodiment of the invention can establish the exclusive knowledge graph of each POI, and then can establish the POI knowledge graph library based on the exclusive knowledge graph of each POI. When a user conducts navigation search, according to input key words, ranking of the multiple POI basic data is obtained according to the relevance of the key words in the corresponding feature entities of the POI knowledge map library, further target POI basic data is determined according to ranking results, and then navigation can be conducted according to the current position of the navigation terminal and the target POI basic data. The invention can still perform navigation when the user forgets the name and address of the POI.
Drawings
Fig. 1 is a flowchart of a navigation method based on a POI knowledge graph according to an embodiment of the present invention.
Fig. 2 is a block diagram of a navigation device based on a POI knowledge graph according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, interfaces, techniques, etc. in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In the description of the present invention, it is to be understood that the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Fig. 1 is a flowchart of a navigation method based on a POI knowledge graph according to an embodiment of the present invention. As shown in fig. 1, the navigation method based on the POI knowledge graph according to the embodiment of the present invention includes:
s1: and acquiring basic data of the POI and POI related information.
In this embodiment, the POI basic data may be obtained through preset navigation software, for example, through commonly used navigation software. The basic data of the POI comprises a POI name, a POI address, longitude and latitude information of the POI and the like.
In the present embodiment, the POI-related information is information related to a POI, and for example, for a mozzarella, the POI-related information includes an introduction about the mozzarella, a recommended sightseeing spot included in the mozzarella, facility information in the mozzarella, and the like. The POI related information is acquired through a network or manually edited after being examined by an entity. The method for acquiring the relevant information of the POI through the network comprises text crawling by using the name or the address of the POI as a key word through a network crawling tool. The POI related information can be edited manually after physical examination, for example, by recording feature entities contained in a moan mountain, such as a luminous runway, by a person on site in the moan mountain.
S2: and extracting POI characteristic entities according to the POI related information.
In one embodiment of the present invention, step S2 includes:
s2-1: and carrying out data deduplication on the POI related information.
Specifically, if the sources of data acquisition are different and the acquired data formats are different, the data formats need to be unified first. If the collected data formats are the same, the data formats are not required to be unified. And carrying out data deduplication on the POI relevant information, wherein the data deduplication is to remove completely same data and only reserve one piece of data. For example, reports (same article introduction) related to the same POI are published or reprinted on different websites, the same article is collected N times when the reports are collected, and the same article is deleted and only one article is reserved when data is deduplicated.
S2-2: and performing text processing and semantic analysis on the POI relevant information after data duplication removal to obtain all feature information in the POI relevant information, and establishing a POI feature entity according to all feature information.
S3: and establishing an exclusive knowledge graph of each POI according to the POI characteristic entity and the POI basic data.
In one embodiment of the present invention, step S3 includes:
s3-1: and unifying names of POI characteristic entities representing the same entity.
In particular, POI feature entities that represent the same entity may be name unified by a given named entity dictionary and then according to the named entity dictionary.
In an example of the present invention, the feature entities extracted from the three reports for the same POI are american technology, shenyang american technology, and in fact, the three feature entities are the same feature entity and are collected together into shenyang american technology limited according to a naming dictionary.
S3-2: and establishing an exclusive knowledge graph of each POI according to each POI and the associated POI characteristic entity.
S3-3: after the knowledge map library of each POI is established, the correlation degree between each POI and the associated characteristic entities is calculated.
In one embodiment of the invention, a deep neural network algorithm is used in the proprietary knowledge-graph of each POI to calculate the degree of correlation between each POI and the associated feature entity.
For example, X denotes a certain POI basic information (POI name or address), and Y denotes a certain feature entity. KR (X, Y) represents the degree of correlation between X and Y. KR (X, Y) is more than or equal to 0 and less than or equal to 1.
More specifically, Word2vec, a tool for converting Word tokens into real-valued vectors, which is open source of Google, is used for realizing the representation of Word vectors of POI and feature entities and forming a matrix data structure. And taking the matrix as input, and predicting by using the model trained by the convolutional neural network so as to obtain a correlation result. The convolutional neural network model can be trained through the pre-labeled linguistic data, so that the probability of word relevancy can be learned by the model.
In this embodiment, the convolutional neural network for correlation calculation is divided into four layers, i.e., an input layer, a convolutional layer, a pooling layer, and an output layer.
The input layer realizes the construction of Word vectors by using Word2vec, and can use a realization module of a sketch-gram Word vector model for Word2vec of Google in python. The corresponding text is entered and the model automatically models the text.
The convolutional layer mainly realizes the selection of characteristics, and the matrix of the input layer is scanned through a convolutional kernel.
The pooling layer can be implemented by a max-posing method, similar to the convolutional layer, except that the pooling layer only operates on the matrix itself.
The output layer mainly realizes the connection of the convolution layer and the pooling layer, and a value representing the correlation degree can be obtained through a plurality of circulations of the convolution layer and the pooling layer.
S4: and constructing a POI knowledge map library based on the exclusive knowledge map of each POI.
In one embodiment of the invention, identical POI feature entities are deduplicated and classified in the POI knowledge map library.
Specifically, the method for removing duplicate features in the POI knowledge map library may be: and carrying out duplication removal on the same characteristic entities contained in the exclusive knowledge maps of the POIs, and only reserving a unique item to establish an association relation between the exclusive knowledge maps of the POIs and the unique reserved characteristic entities.
In one example of the invention, the knowledge graph library includes two POI knowledge graphs, a POI1Knowledge graph and POI2And (4) knowledge mapping. POI1The characteristic entities associated with the knowledge graph comprise A, B, C and D, POI2The feature entities associated with the knowledge-graph include A, E, F and G.
The embodiment is used for removing the duplication of the entity object in the knowledge graph library and aiming at the POI1Knowledge graph and POI2The same characteristic entities A contained in the knowledge graph are subjected to duplication removal, only one item is reserved, and at the moment, the characteristic entities in the knowledge graph library comprise A, B, C, D, E, F and G, namely, the POI is established1Knowledge graph and POI2The association relationship between the knowledge graph and the unique reserved characteristic entity A. And when searching, according to the key words input by the user, when the key words are matched with the characteristic entity A, the POI is given1Underlying data and POI2And (4) recommending basic data.
In this embodiment, the classification method for POI feature entities of the same category may be, for example, as follows:
the knowledge map library comprises two POI knowledge maps: POIAKnowledge graph and POIBAnd (4) knowledge mapping. Wherein, POIAThe characteristic entities of the knowledge graph comprise an archness castle and a baby king. POIBThe characteristic entities of the knowledge-graph include: happy baby. The characteristic entities in the knowledge map library also comprise playgrounds, namely the classification of the naughty fort, the baby king and the happy baby.
When the user navigates and the input key word is naughty castle or baby king, POI can be recommendedAWhen the key word input by the user is happy baby, the POI is recommendedBWhen the user inputs the keyword as the amusement park, the POI is recommended at the same timeAAnd POIBAnd sorting according to the degree of correlation.
When the keyword searched by the user during navigation is the amusement park, the POI can be recommendedAAnd POIBIs the basic data (i.e. POI)AAnd POIBName and address of). POIAAnd POIBBase data of (1) the physical playground relative to the POI according to the characteristicsAAnd POIBThe relevance rank of (2). For example: naughty castle to POIAHas a correlation of 0.7, and the baby king is opposite to the POIAHas a correlation of 0.6, Happy baby to POIBHas a relevance of 0.5, and when the navigation is recommended, the POIAWill be ranked at POIBIn front of the underlying data.
S5: and navigating based on the POI knowledge map library.
In one embodiment of the present invention, step S5 includes:
s5-1: and acquiring a keyword for navigation search.
S5-2: and obtaining the sequence of the basic data of the POI according to the relevance of the characteristic entity corresponding to the key word.
In one example of the invention, e.g., the keyword is "night runway", then at each POI knowledge map library a POI feature entity is matched that includes "night runway". If N POI-specific knowledge maps including a 'luminous runway' are matched, the embodiment sorts according to the relevance between each POI and the characteristic entity 'luminous runway'.
S5-3: and determining target POI basic data based on the sorting result. For example, the POI with the highest relevance may be used as the target POI by default, or the user may select the POI as the target POI according to the own requirement, and then obtain the basic data of the target POI, that is, the name and address of the target POI.
S5-4: and navigating according to the current position of the navigation terminal and the basic data of the target POI.
The navigation method based on the POI knowledge map library provided by the embodiment of the invention can establish the exclusive knowledge map of each POI, and then can establish the POI knowledge map library based on the exclusive knowledge map of each POI. When a user conducts navigation search, according to input key words, ranking of the multiple POI basic data is obtained according to the relevance of the key words in the corresponding feature entities of the POI knowledge map library, further target POI basic data is determined according to ranking results, and then navigation can be conducted according to the current position of the navigation terminal and the target POI basic data.
Fig. 2 is a block diagram of a navigation device based on a POI knowledge graph according to an embodiment of the present invention. As shown in fig. 2, the navigation apparatus based on POI knowledge graph according to the embodiment of the present invention includes: an acquisition module 100, a storage module 200, a control processing module 300 and a navigation module 400.
The obtaining module 100 is configured to obtain basic data of a point of interest (POI) and POI related information. The control processing module 300 is configured to extract a POI feature entity according to the POI related information. The control processing module 300 is further configured to establish a dedicated knowledge graph of each POI according to the POI feature entities and the POI base data. The control processing module 300 is further configured to construct, through the storage module, a POI knowledge map library based on the specific knowledge map of each POI. The navigation module 400 is used to navigate based on the POI knowledge map library.
In an embodiment of the present invention, the control processing module 300 is configured to perform data deduplication and text processing on the POI related information to obtain the POI feature entity.
In one embodiment of the invention, the control processing module 300 performs name unification on POI feature entities representing the same entity. The control processing module 300 is further configured to establish a dedicated knowledge graph of each POI according to each POI and the associated feature entity.
In one embodiment of the present invention, the control processing module 300 is further configured to calculate a degree of correlation between each POI and the associated feature entity using a deep neural network algorithm in the dedicated knowledge-graph of each POI.
In an embodiment of the present invention, the control processing module 300 is further configured to perform deduplication on the same POI feature entities in the POI knowledge map library, and classify POI feature entities of the same category.
In one embodiment of the invention, the navigation module 400 is used to obtain keywords for performing a navigation search. The navigation module 400 is further configured to obtain a ranking of the multiple POI base data according to the relevance of the feature entity corresponding to the keyword. The navigation module 400 is further configured to determine target POI basis data based on the ranking results; the navigation module 400 is further configured to navigate according to the current location of the navigation terminal and the basic data of the target POI.
In one embodiment of the invention, the POI base data is acquired from map data provided by the navigation terminal.
In one embodiment of the invention, the POI-related information is obtained via a network or entered by a user after physical investigation.
It should be noted that, the specific implementation of the navigation apparatus based on the POI knowledge graph in the embodiment of the present invention is similar to the specific implementation of the navigation method based on the POI knowledge graph in the embodiment of the present invention, and specific reference is specifically made to the description of the navigation method based on the POI knowledge graph, and no further description is made for reducing redundancy.
In addition, other configurations and functions of the navigation device based on the POI knowledge graph according to the embodiment of the present invention are known to those skilled in the art, and are not described in detail for reducing redundancy.
An embodiment of the present invention further provides an electronic device, including: at least one processor and at least one memory; the memory is to store one or more program instructions; the processor is configured to execute one or more program instructions to perform the POI knowledge-graph based navigation method according to the first aspect.
The disclosed embodiments of the present invention provide a computer-readable storage medium having stored therein computer program instructions, which, when run on a computer, cause the computer to perform the above-mentioned POI knowledge-graph-based navigation method.
In an embodiment of the invention, the processor may be an integrated circuit chip having signal processing capability. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.
The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or which may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory.
The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (ddr Data Rate SDRAM), Enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).
The storage media described in connection with the embodiments of the invention are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will appreciate that the functionality described in the present invention may be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.
Claims (10)
1. A navigation method based on a POI knowledge graph is characterized by comprising the following steps:
obtaining POI basic data and POI related information;
extracting a POI characteristic entity according to the POI related information;
establishing an exclusive knowledge graph of each POI according to the POI characteristic entity and the POI basic data;
constructing a POI knowledge map library based on the exclusive knowledge map of each POI;
and navigating based on the POI knowledge map library.
2. The POI knowledge graph-based navigation method according to claim 1, wherein the POI feature entity is extracted according to the POI related information, and the method comprises the following steps:
and carrying out data duplication removal and text processing on the POI related information to obtain the POI characteristic entity.
3. The POI-knowledge-graph-based navigation method according to claim 1, wherein establishing the specific knowledge graph of each POI according to the POI feature entities and the POI base data comprises:
unifying names of POI characteristic entities representing the same entity;
and establishing an exclusive knowledge graph of each POI according to each POI and the associated characteristic entity.
4. The POI knowledge graph-based navigation method of claim 3, wherein a deep neural network algorithm is used in the proprietary knowledge graph of each POI to calculate the degree of correlation between each POI and the associated feature entity.
5. The POI knowledge graph-based navigation method according to claim 4, wherein the building of the POI knowledge graph library based on the specific knowledge graph of each POI comprises:
and removing the duplication of the same POI characteristic entities in the POI knowledge map library, and classifying the POI characteristic entities of the same category.
6. The POI knowledge graph-based navigation method according to claim 5, wherein navigating based on the POI knowledge graph library comprises:
acquiring a keyword for navigation search;
obtaining the sequence of the multiple POI basic data according to the relevancy of the feature entities corresponding to the key words;
determining target POI basic data based on the sorting result;
and navigating according to the current position of the navigation terminal and the basic data of the target POI.
7. The POI knowledge-graph-based navigation method according to claim 1, wherein the POI-related information is obtained through a network or manually edited after physical investigation.
8. A POI knowledge-graph-based navigation apparatus, comprising:
the acquisition module is used for acquiring POI basic data and POI related information;
a storage module;
the control processing module is used for extracting a POI characteristic entity according to the POI related information; the control processing module is further used for establishing an exclusive knowledge map of each POI according to the POI characteristic entity and the POI basic data; the control processing module is also used for constructing a POI knowledge map library based on the exclusive knowledge map of each POI through the storage module;
and the navigation module is used for navigating based on the POI knowledge map library.
9. An electronic device, characterized in that the electronic device comprises: at least one processor and at least one memory;
the memory is to store one or more program instructions;
the processor configured to execute one or more program instructions to perform the POI knowledge-graph based navigation method of any one of claims 1-7.
10. A computer readable storage medium having one or more program instructions embodied therein for performing the POI knowledge-graph based navigation method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110111132.1A CN112818261A (en) | 2021-01-27 | 2021-01-27 | Navigation method and device based on POI (Point of interest) knowledge graph and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110111132.1A CN112818261A (en) | 2021-01-27 | 2021-01-27 | Navigation method and device based on POI (Point of interest) knowledge graph and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112818261A true CN112818261A (en) | 2021-05-18 |
Family
ID=75859822
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110111132.1A Pending CN112818261A (en) | 2021-01-27 | 2021-01-27 | Navigation method and device based on POI (Point of interest) knowledge graph and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112818261A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100125406A1 (en) * | 2008-11-19 | 2010-05-20 | Nokia Corporation | Methods, apparatuses, and computer program products for providing point of interest navigation services |
| CN108153901A (en) * | 2018-01-16 | 2018-06-12 | 北京百度网讯科技有限公司 | The information-pushing method and device of knowledge based collection of illustrative plates |
| CN110737774A (en) * | 2018-07-03 | 2020-01-31 | 百度在线网络技术（北京）有限公司 | Book knowledge graph construction method, book recommendation method, device, equipment and medium |
| CN111475625A (en) * | 2020-05-09 | 2020-07-31 | 山东舜网传媒股份有限公司 | News manuscript generation method and system based on knowledge graph |
| CN112084383A (en) * | 2020-09-07 | 2020-12-15 | 中国平安财产保险股份有限公司 | Information recommendation method, device and equipment based on knowledge graph and storage medium |
| CN112131275A (en) * | 2020-09-23 | 2020-12-25 | 中国科学技术大学智慧城市研究院(芜湖) | Enterprise portrait construction method of holographic city big data model and knowledge graph |
-
2021
- 2021-01-27 CN CN202110111132.1A patent/CN112818261A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100125406A1 (en) * | 2008-11-19 | 2010-05-20 | Nokia Corporation | Methods, apparatuses, and computer program products for providing point of interest navigation services |
| CN108153901A (en) * | 2018-01-16 | 2018-06-12 | 北京百度网讯科技有限公司 | The information-pushing method and device of knowledge based collection of illustrative plates |
| CN110737774A (en) * | 2018-07-03 | 2020-01-31 | 百度在线网络技术（北京）有限公司 | Book knowledge graph construction method, book recommendation method, device, equipment and medium |
| CN111475625A (en) * | 2020-05-09 | 2020-07-31 | 山东舜网传媒股份有限公司 | News manuscript generation method and system based on knowledge graph |
| CN112084383A (en) * | 2020-09-07 | 2020-12-15 | 中国平安财产保险股份有限公司 | Information recommendation method, device and equipment based on knowledge graph and storage medium |
| CN112131275A (en) * | 2020-09-23 | 2020-12-25 | 中国科学技术大学智慧城市研究院(芜湖) | Enterprise portrait construction method of holographic city big data model and knowledge graph |
Non-Patent Citations (2)
| Title |
|---|
| 史忠植，王文杰，马慧芳: "旅游管理专业本科生论文集 第一辑", 北京：机械工业出版社 , pages: 239 - 193 * |
| 曾旺红: "基于知识图谱的旅游景点个性化推荐方法研究", 中国优秀硕士学位论文全文数据库信息科技辑, no. 07, pages 12 - 51 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9424529B2 (en) | Systems and methods to facilitate local searches via location disambiguation | |
| CN110765368B (en) | Artificial intelligence system and method for semantic retrieval | |
| CN108287816B (en) | Interest point online detection and machine learning classifier training method and device | |
| CN110795515B (en) | Point of interest (POI) processing method and device, electronic equipment and computer storage medium | |
| CN109002499B (en) | Discipline correlation knowledge point base construction method and system | |
| Abdolmajidi et al. | Matching authority and VGI road networks using an extended node-based matching algorithm | |
| CN113722478A (en) | Multi-dimensional feature fusion similar event calculation method and system and electronic equipment | |
| CN109766447A (en) | A kind of method and apparatus of determining sensitive information | |
| Hall et al. | Generating geographical location descriptions with spatial templates: a salient toponym driven approach | |
| CN110688434B (en) | Method, device, equipment and medium for processing interest points | |
| KR101747532B1 (en) | Method and system for recommending course for travel related query | |
| Mata-Rivera et al. | A collaborative learning approach for geographic information retrieval based on social networks | |
| Kilic et al. | Investigating the quality of reverse geocoding services using text similarity techniques and logistic regression analysis | |
| CN112818261A (en) | Navigation method and device based on POI (Point of interest) knowledge graph and electronic equipment | |
| CN116431746A (en) | Address mapping method and device based on coding library, electronic equipment and storage medium | |
| CN114513550B (en) | Geographic position information processing method and device and electronic equipment | |
| Qian et al. | Data cleaning approaches in Web2. 0 VGI application | |
| CN115577190A (en) | Tourist behavior data extraction method | |
| CN116244426A (en) | Geographic function area identification method, device, equipment and storage medium | |
| TW202146850A (en) | Processing apparatus and method for determining road names | |
| JPH1166078A (en) | Retrieval request embodiment method/device and storage medium storing retrieval, request embodiment program | |
| CN110688492B (en) | Knowledge graph query method based on lightweight index | |
| Luberg et al. | Information extraction for a tourist recommender system | |
| CN115358379B (en) | Neural network processing method, neural network processing device, information processing method, information processing device and computer equipment | |
| CN110083679B (en) | Search request processing method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: 110000 No. 861-6, shangshengou village, Hunnan District, Shenyang City, Liaoning Province Applicant after: Shenyang Meihang Technology Co.,Ltd. Address before: 110000 12th and 13th floors, No.1 Jinhui street, Hunnan New District, Shenyang City, Liaoning Province Applicant before: SHENYANG MXNAVI Co.,Ltd. |
|
| CB02 | Change of applicant information |