CN111782748A - Map retrieval method, and information point POI semantic vector calculation method and device - Google Patents

Map retrieval method, and information point POI semantic vector calculation method and device Download PDF

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CN111782748A
CN111782748A CN202010597660.8A CN202010597660A CN111782748A CN 111782748 A CN111782748 A CN 111782748A CN 202010597660 A CN202010597660 A CN 202010597660A CN 111782748 A CN111782748 A CN 111782748A
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CN111782748B (en
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臧文华
范淼
卓安
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Beijing Baidu Netcom Science and Technology Co Ltd
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Abstract

The application discloses a map retrieval method, and a method and a device for calculating information point POI semantic vectors, relates to the technical field of map retrieval, and can be applied to the fields of cloud and deep learning. The map retrieval method comprises the following steps: calculating a semantic vector of the map retrieval information; searching a target POI semantic vector matched with the semantic vector of the map retrieval information according to a pre-established information point POI semantic vector index library; the semantic vector of the target POI is obtained by calculation according to the graph semantic features of the target POI, and the graph semantic features of the target POI aggregate the semantic features between the target POI and the N neighbor nodes. According to the method and the device, the generation of the POI semantic vector is optimized, the semantic vector expression of the POI is richer, the accuracy and the confidence coefficient of the POI semantic vector can be improved, the retrieval prompt can be recalled accurately, the basic recall rate is effectively improved, the recall effect is improved, and the problems in the prior art are solved.

Description

Map retrieval method, and information point POI semantic vector calculation method and device
Technical Field
The application relates to a data processing technology, in particular to the technical field of map retrieval, and specifically relates to a map retrieval method, and a method and a device for calculating a semantic vector of an information point POI.
Background
In the map retrieval technology, retrieval prompt (sug for short) can be recalled in a semantic recall mode. However, in the current semantic recall method, a POI vector index is usually established according to a POI name, and for the map retrieval information input by the user, only the similarity between the semantic vector of the map retrieval information and the semantic vector of the POI name is considered, which may result in poor recall effect when there is a difference in the map retrieval information.
Disclosure of Invention
The application provides a map retrieval method, and a method and a device for calculating a semantic vector of an information point POI.
According to a first aspect, the present application provides a map retrieval method, the method comprising:
calculating a semantic vector of the map retrieval information;
searching a target POI semantic vector matched with the semantic vector of the map retrieval information according to a pre-established information point POI semantic vector index library; the semantic vector of the target POI is obtained by calculation according to the image semantic features of the target POI, the image semantic features of the target POI aggregate the semantic features between the target POI and N neighbor nodes, and N is an integer greater than 1.
According to a second aspect, the present application provides a method for calculating a semantic vector of an information point POI, the method including:
acquiring the image meaning characteristics of a target POI, wherein the image meaning characteristics of the target POI aggregate the semantic characteristics between the target POI and N neighbor nodes, and N is an integer greater than 1;
and calculating the semantic vector of the target POI according to the image semantic features of the target POI.
According to a third aspect, the present application provides a map retrieval apparatus comprising:
the first calculation module is used for calculating semantic vectors of the map retrieval information;
the searching module is used for searching a semantic vector of a target POI matched with the semantic vector of the map retrieval information according to a pre-established information point POI semantic vector index library; the semantic vector of the target POI is obtained by calculation according to the image semantic features of the target POI, the image semantic features of the target POI aggregate the semantic features between the target POI and N neighbor nodes, and N is an integer greater than 1.
According to a fourth aspect, the present application provides an apparatus for calculating a semantic vector of a POI, comprising:
the system comprises a first acquisition module, a first search module and a second search module, wherein the first acquisition module is used for acquiring the image semantic features of a target POI, the image semantic features of the target POI aggregate the semantic features between the target POI and N neighbor nodes, and N is an integer greater than 1;
and the calculation module is used for calculating the semantic vector of the target POI according to the image semantic features of the target POI.
According to a fifth aspect, the present application provides an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any of the methods of the first aspect or to enable the at least one processor to perform any of the methods of the second aspect.
According to a sixth aspect, the present application provides a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of the first aspects or the second aspect.
According to the technology of the application, the semantic vector of the POI is obtained through calculation according to the image semantic features of the POI, and the image semantic features of the POI aggregate the semantic features between the POI and the N neighbor nodes, so that the generation of the semantic vector of the POI is optimized, the semantic vector expression of the POI is richer, the accuracy and the confidence coefficient of the semantic vector of the POI can be improved, and a more accurate and efficient POI semantic vector index library can be constructed. Therefore, even if the map retrieval information input by the user is different, the retrieval prompt can be recalled accurately, the basic recall rate is effectively improved, the recall effect is improved, and the problems in the prior art are solved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
fig. 1 is a schematic flow chart of a map retrieval method according to a first embodiment of the present application;
FIG. 2 is a POI-query graph according to a first embodiment of the present application;
FIG. 3 is a POI-POI diagram in accordance with a first embodiment of the present application;
FIG. 4 is a schematic diagram of obtaining a semantic vector of a target POI according to a first embodiment of the present application;
fig. 5 is a schematic diagram of obtaining semantic vectors of map retrieval information according to the first embodiment of the present application;
FIG. 6 is a schematic diagram of a map retrieval process according to a first embodiment of the present application;
fig. 7 is a flowchart illustrating a method for calculating a POI semantic vector according to a second embodiment of the present application;
fig. 8 is a schematic structural diagram of a map retrieval apparatus according to a third embodiment of the present application;
fig. 9 is a schematic structural diagram of a POI semantic vector calculation apparatus according to a third embodiment of the present application;
FIG. 10 is a block diagram of an electronic device used to implement an embodiment of the application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
First embodiment
As shown in fig. 1, the present application provides a map retrieval method, including the steps of:
step 101: and calculating a semantic vector of the map retrieval information.
The map search information may be understood as map search information input by a user, and an english expression of the map search information may be "query".
In this step, a semantic vector of the map retrieval information may be calculated through a pre-trained semantic model, and specifically, after receiving the map retrieval information input by the user, the map retrieval information is input into the pre-trained semantic model for calculation, so as to obtain the semantic vector of the map retrieval information. The semantic vector of the map retrieval information may be, for example, but not limited to, an embedded representation.
In the present application, the semantic vector of the map retrieval information may be calculated by using the existing semantic model, or may be calculated by using a new semantic model provided in the present application, which will be described later.
Step 102: searching a target POI semantic vector matched with the semantic vector of the map retrieval information according to a pre-established POI semantic vector index library; the semantic vector of the target POI is obtained by calculation according to the graph semantic features of the target POI, and the graph semantic features of the target POI aggregate the semantic features between the target POI and the N neighbor nodes.
Wherein N is an integer greater than 1.
POI is an abbreviation of "Point of Information" and Chinese can be translated into "Information points" (and also into "points of interest"). In the technical fields of geographic information systems and map retrieval, a POI may be a house, a shop, a mailbox, a bus station, etc. The traditional geographic information acquisition method requires a mapping staff to acquire the longitude and latitude of a POI by using a precise mapping instrument and then mark the POI. Each POI can contain information of the four aspects of name, category, coordinate and classification, can remind a user of detailed information of branches of road conditions and surrounding buildings, can also facilitate navigation to find each place required by the user, and can select the most convenient and unobstructed road for path planning, and the like.
The POI semantic vector index library refers to an index library which is created in advance and used for storing a plurality of POIs and semantic vectors corresponding to the POIs, and the POIs stored in the POI semantic vector index library can be full-scale POIs. Any POI semantic vector stored in the POI semantic vector index database can be calculated through a pre-trained semantic model, specifically, taking a target POI as an example, the image semantic features of the target POI can be obtained first, and the image semantic features of the target POI are input into the pre-trained semantic model for calculation, so that the semantic vector of the target POI is obtained. After the semantic vector of the target POI is obtained, the target POI and the semantic vector corresponding to the target POI can be stored in a POI semantic vector index database. The semantic vector of the target POI may be, for example, but not limited to, an embed representation.
In the present application, the existing semantic model may be used to calculate the semantic vector of the POI, or a new semantic model provided in the present application, which will be described later, may be used to calculate the semantic vector of the POI.
It should be noted that, in order to improve the map retrieval efficiency, the POI semantic vector index library may be created in advance in an offline manner, that is, the image semantic features of the POI and the semantic vectors of the POI may be calculated in advance in an offline manner. Of course, it is not excluded to implement the above process in an online manner. The processes of calculating the semantic vector of the map retrieval information in step 101 and searching the target POI semantic vector matched with the semantic vector of the map retrieval information according to the POI semantic vector index library in step 102 can be realized in an online manner.
The following provides a manner for acquiring the semantic features of the target POI, that is, the semantic features of the target POI can be obtained through the following steps:
acquiring N neighbor nodes of the target POI;
respectively acquiring semantic association between the target POI and each neighbor node to obtain N semantic features;
and aggregating the N semantic features to obtain the image semantic features of the target POI.
In the application, after N neighbor nodes of a target POI are obtained, a graph model of the target POI and the N neighbor nodes is constructed by using a GCN (graph convolutional Network), semantic features between the target POI and each neighbor node are respectively obtained, and the semantic features are aggregated by using a propagation characteristic of the graph model, so that the graph semantic features of the target POI are obtained.
The neighbor nodes of the target POI can be determined according to the historical click data of the user within the preset time, and the sources of the historical click data of the user are rich and diverse, so that the neighbor nodes of the target POI are enriched, and the semantic vector expression of the POI is enriched.
In summary, the semantic vector of the POI is calculated according to the image semantic features of the POI, and the image semantic features of the POI aggregate the semantic features between the POI and the plurality of neighbor nodes, so that the generation of the POI semantic vector is optimized, the expression of the POI semantic vector is richer, the accuracy and the confidence of the POI semantic vector can be improved, and a more accurate and efficient POI semantic vector index library can be constructed.
In practical application, in the step, a target POI semantic vector matched with the semantic vector of the map retrieval information can be searched according to the POI semantic vector index library, so that recall of retrieval prompts can be realized, and a POI corresponding to the map retrieval information is recommended to a user.
In the application, at least one POI most relevant to the map retrieval information can be recalled from the total POIs by utilizing a K nearest neighbor algorithm, an hnsw search algorithm and the like.
Because the semantic vector expression of the POI is richer, and the accuracy and the confidence coefficient of the POI semantic vector are higher, the retrieval prompt can be recalled accurately even if the map retrieval information input by the user is different, so that the basic recall rate is effectively improved, the recall effect is improved, and the problems in the prior art are solved. From the perspective of a user, the method and the device can reduce the user input step length, improve the user input efficiency, better meet the user requirements, and improve the user experience.
Optionally, the N neighbor nodes are N pieces of map retrieval information, and the N pieces of map retrieval information are selected by a user in response to the target POI; alternatively, the first and second electrodes may be,
the N neighbor nodes are N POIs sequentially selected by a user within preset time, and the target POI and the N POIs belong to the same type.
The embodiment provides two kinds of graph models of the target POI and N neighbor nodes, or provides two kinds of composition schemes of the POI and the neighbor nodes.
First, a POI-query graph may be formed by counting map retrieval information (i.e., query) input by a user clicking a target POI within a preset time, as shown in fig. 2. Secondly, other POIs of the same type as the target POI can be clicked in sequence by the statistical user within a preset time to form a POI-POI graph, as shown in fig. 3.
Considering that the application scene of the application is based on semantic association between the POI and the map retrieval information, the POI-query graph can be preferentially selected and constructed, so that the semantic information of the historical map retrieval information of the target POI can be aggregated, and richer cross features are provided for association between the POI and the map retrieval information in online map retrieval.
In the embodiment, the selection operation of the target POI is responded by the user and the N POIs are sequentially selected by the user within the preset time, both the N POIs can be understood as historical click data of the user, the neighbor nodes of the target POI are determined according to the historical click data of the user, and the sources of the historical click data of the user are rich and diverse, so that the neighbor nodes of the target POI are enriched, and the semantic vector expression of the POI is enriched.
Optionally, the semantic vector of the target POI is obtained by calculation according to the image semantic features of the target POI and the basic information of the target POI; wherein the basic information of the target POI comprises at least one of the name of the target POI, the address of the target POI, the tag to which the target POI belongs and the geographical position of the target POI.
In this embodiment, the semantic vector of the target POI further considers basic information of the target POI, such as a name (POI name), an address (address), a tag (tag), a geographic location (e.g., city), and the like, in addition to the image semantic features of the target POI.
The basic information of the target POI also plays an important role in representing the semantic vector of the target POI. For example, the tags of the POIs are divided into hundreds of categories such as local communities, hospitals, banks, buildings and the like, when a user searches a certain definite POI, a tag tendency is implied, if the relationship between the query and the POI name and tag can be established, the POIs of the tag can be definite, the retrieval range is reduced, and other tag impurities are reduced. Assuming that ". x. residence commission" is searched, tag of POI is of residence commission class, not of traffic class. For example, the geographic location information of the POI is input into the same query at different geographic locations (such as different cities), the user requirements are greatly different, and if "chinese geology university" is searched in beijing, the user's main requirement is likely to be "chinese geology university (beijing)", whereas if "chinese geology university" is searched in wuhan, the user's main requirement is likely to be "chinese geology university (wuhan)".
Therefore, the embodiment can realize multi-source fusion by fusing the vector representation of multiple domains of the POI, and can enable the semantic vector expression of the POI to be more accurate and richer.
Optionally, the semantic vector of the target POI is calculated by the following steps:
calculating a first vector representation of the target POI according to the graphic meaning characteristics of the target POI;
calculating a second vector representation of the target POI according to the basic information of the target POI;
and calculating a semantic vector of the target POI according to the first vector representation and the second vector representation.
In this embodiment, when a plurality of types of basic information of the target POI are comprehensively considered, corresponding vector representations, such as tag vector representation, city vector representation, POI name vector representation, address vector representation, and the like, may be respectively calculated from different pieces of basic information.
In this embodiment, the first vector representation of the target POI may be calculated by GCN, and the poinamevector representation may be calculated by the semantic pre-training model ERNIE-TINY.
As shown in fig. 4, fig. 4 is a schematic diagram illustrating that vector representations of four domains including three basic information (POI name, tag, and city) and a graph semantic feature are fused to obtain a semantic vector of a target POI.
According to the method and the device, a log (namely historical click data of a user) can be clicked by the user, the tag of the POI clicked by the query is mapped into a tag vector representation, and the tag vector representation is spliced to the vector representation of the POI name by using the click relation between the query and the POI.
Optionally, the calculating a semantic vector of the map retrieval information includes:
calculating a first vector representation of the map retrieval information according to the map retrieval information;
after receiving the map retrieval information, calculating a second vector representation of the map retrieval information according to the current geographic position information;
calculating a semantic vector of the map retrieval information from the first vector representation and the second vector representation.
In this embodiment, the first vector representation of the map search information may be calculated by CNN (convolutional neural Networks).
In this embodiment, the semantic vector of the map retrieval information further considers current geographic location (e.g., city) information in addition to the map retrieval information itself. The current geographical position information can well reflect the main requirements of the user, so that the current geographical position plays an important role in representing the semantic vector of the map retrieval information. The current geographical position information may be understood as scene information (side information) of the map retrieval information, and the map retrieval information itself and the current geographical position information may be regarded as a plurality of fields of the map retrieval information.
As shown in fig. 5, fig. 5 is a schematic diagram illustrating that vector representations of two domains, namely query and city, are fused to obtain a semantic vector of map retrieval information.
According to the embodiment, multi-source fusion can be realized by fusing vector representation of multiple domains of map retrieval information, and the semantic vector expression of the POI can be more accurate and richer.
Optionally, the semantic vector of the target POI and the semantic vector of the map retrieval information are calculated through a pre-trained semantic model;
the training function of the semantic model is as follows:
Figure BDA0002557646750000091
wherein Q represents a feature matrix of a map retrieval information sample, P represents a feature matrix of a POI sample,
Figure BDA0002557646750000092
represents the kronecker product, RposRepresenting a positive sample feature matrix, RnegRepresenting a negative sample feature matrix, m representing the spacing between the positive and negative sample feature matrices, and l representing a loss function.
The loss function used by the semantic model is different from the conventional loss function, and the training data used by the loss function is that positive and negative samples are constructed in each batch of training data, wherein in each batch of training data, the positive sample is the kronecker product of the vector of query and the vector of the POI clicked by query, and the negative sample is the inner product of the vector of query and the vector of the POI not clicked by query. The loss function does not need labeled label, and the distance between the positive sample and the negative sample is increased by using m during calculation, so that the positive sample and the negative sample have discrimination. The loss function used by the semantic model has better robustness, so that the robustness of the semantic model can be improved.
For example, in the training process, it is assumed that a batch of training data includes two pieces of data, which are:
query: hundredth big (denoted as Q1), the POI clicked by the user is: hundredth buildings (P1);
query: west two flags (denoted as Q2), the POI clicked by the user is: west two flag subway station (noted as P2).
Then in this batch of training data, the inner product of Q1 with the P1 vector corresponds to a positive sample feature matrix and the inner product of Q1 with the P2 vector corresponds to a negative sample feature matrix. The loss function is applied to enable the similarity between the query of the positive sample and the POI to be high, the similarity between the query of the negative sample and the POI to be low, the difference between the query of the positive sample and the POI is not larger than m, and the positive sample and the negative sample have discrimination.
After the semantic model is obtained by using the training function, the calculation of the POI semantic vector and the query semantic vector can be realized through the semantic model.
As shown in fig. 6, fig. 6 illustrates a map retrieval process using a semantic model to implement computation of POI semantic vectors and query semantic vectors and semantic recalls.
It should be noted that various optional embodiments of the map retrieval method in the present application may be implemented in combination with each other or implemented separately, and the present application is not limited thereto.
The above embodiments of the present application have at least the following advantages or benefits:
in the method, the semantic vector of the POI is obtained by calculation according to the image semantic features of the POI, and the image semantic features of the POI aggregate the semantic features between the POI and the N neighbor nodes, so that the generation of the POI semantic vector is optimized, the semantic vector expression of the POI is richer, the accuracy and the confidence coefficient of the POI semantic vector can be improved, and a more accurate and efficient POI semantic vector index library can be constructed. Therefore, even if the map retrieval information input by the user is different, the retrieval prompt can be recalled accurately, the basic recall rate is effectively improved, the recall effect is improved, and the problems in the prior art are solved. The method and the system can be applied to the cloud field, such as cloud service or cloud platform, and can also be applied to the deep learning field.
Second embodiment
As shown in fig. 7, the present application provides a method for calculating a POI semantic vector, which includes the following steps:
step 201: acquiring the image meaning characteristics of a target POI, wherein the image meaning characteristics of the target POI aggregate the semantic characteristics between the target POI and N neighbor nodes, and N is an integer greater than 1;
step 202: and calculating the semantic vector of the target POI according to the image semantic features of the target POI.
Optionally, the obtaining of the graph semantic features of the target POI includes:
acquiring N neighbor nodes of the target POI;
respectively acquiring semantic association between the target POI and each neighbor node to obtain N semantic features;
and aggregating the N semantic features to obtain the image semantic features of the target POI.
Optionally, the N neighbor nodes are N pieces of map retrieval information, and the N pieces of map retrieval information are selected by a user in response to the target POI; alternatively, the first and second electrodes may be,
the N neighbor nodes are N POIs sequentially selected by a user within preset time, and the target POI and the N POIs belong to the same type.
Optionally, the method further includes:
acquiring basic information of the target POI, wherein the basic information of the target POI comprises at least one of the name of the target POI, the address of the target POI, a tag to which the target POI belongs and the geographical position of the target POI;
the calculating the semantic vector of the target POI according to the image semantic features of the target POI comprises the following steps:
and calculating the semantic vector of the target POI according to the image semantic features of the target POI and the basic information of the target POI.
Optionally, the calculating a semantic vector of the target POI according to the graph semantic features of the target POI and the basic information of the target POI includes:
calculating a first vector representation of the target POI according to the graphic meaning characteristics of the target POI;
calculating a second vector representation of the target POI according to the basic information of the target POI;
and calculating a semantic vector of the target POI according to the first vector representation and the second vector representation.
Optionally, the semantic vector of the target POI is calculated through a pre-trained semantic model;
the training function of the semantic model is as follows:
Figure BDA0002557646750000111
wherein Q represents a feature matrix of a map retrieval information sample, P represents a feature matrix of a POI sample,
Figure BDA0002557646750000112
represents the kronecker product, RposRepresenting a positive sample feature matrix, RnegRepresenting a negative sample feature matrix, m representing the spacing between the positive and negative sample feature matrices, and l representing a loss function.
In this application, any implementation manner related to the calculation of the POI semantic vector in the first embodiment may be referred to in the related implementation manner of the second embodiment, and any implementation manner related to the calculation of the POI semantic vector in the first embodiment is applicable to the second embodiment, and can achieve the same beneficial effects, and is not described here again to avoid repetition.
Third embodiment
As shown in fig. 8, the present application provides a map retrieval apparatus 300 including:
a first calculation module 301, configured to calculate a semantic vector of the map retrieval information;
the searching module 302 is configured to search a semantic vector of a target POI matched with the semantic vector of the map retrieval information according to a pre-created information point POI semantic vector index library; the semantic vector of the target POI is obtained by calculation according to the image semantic features of the target POI, the image semantic features of the target POI aggregate the semantic features between the target POI and N neighbor nodes, and N is an integer greater than 1.
Optionally, the map retrieval apparatus 300 further includes:
the first acquisition module is used for acquiring N neighbor nodes of the target POI;
the second acquisition module is used for respectively acquiring semantic association between the target POI and each neighbor node to obtain N semantic features;
and the aggregation module is used for aggregating the N semantic features to obtain the image semantic features of the target POI.
Optionally, the N neighbor nodes are N pieces of map retrieval information, and the N pieces of map retrieval information are selected by a user in response to the target POI; alternatively, the first and second electrodes may be,
the N neighbor nodes are N POIs sequentially selected by a user within preset time, and the target POI and the N POIs belong to the same type.
Optionally, the semantic vector of the target POI is obtained by calculation according to the image semantic features of the target POI and the basic information of the target POI; wherein the basic information of the target POI comprises at least one of the name of the target POI, the address of the target POI, the tag to which the target POI belongs and the geographical position of the target POI.
Optionally, the map retrieving apparatus 300 further includes a second calculating module, configured to:
calculating a first vector representation of the target POI according to the graphic meaning characteristics of the target POI;
calculating a second vector representation of the target POI according to the basic information of the target POI;
and calculating a semantic vector of the target POI according to the first vector representation and the second vector representation.
Optionally, the first calculating module 301 is specifically configured to:
calculating a first vector representation of the map retrieval information according to the map retrieval information;
after receiving the map retrieval information, calculating a second vector representation of the map retrieval information according to the current geographic position information;
calculating a semantic vector of the map retrieval information from the first vector representation and the second vector representation.
Optionally, the semantic vector of the target POI is calculated through a pre-trained semantic model;
the training function of the semantic model is as follows:
Figure BDA0002557646750000131
wherein Q represents a feature matrix of a map retrieval information sample, P represents a feature matrix of a POI sample,
Figure BDA0002557646750000132
represents the kronecker product, RposRepresenting a positive sample feature matrix, RnegRepresenting a negative sample feature matrix, m representing the spacing between the positive and negative sample feature matrices, and l representing a loss function.
The map retrieval device 300 provided by the present application can implement each process in the above map retrieval method embodiments, and can achieve the same beneficial effects, and for avoiding repetition, the details are not repeated here.
Fourth embodiment
As shown in fig. 9, the present application provides a device 400 for calculating a POI semantic vector, comprising:
a first obtaining module 401, configured to obtain a semantic feature of a target POI, where the semantic feature of the target POI aggregates semantic features between the target POI and N neighbor nodes, and N is an integer greater than 1;
a calculating module 402, configured to calculate a semantic vector of a target POI according to a graph semantic feature of the target POI.
Optionally, the first obtaining module 401 includes:
the first obtaining sub-module is used for obtaining N neighbor nodes of the target POI;
the second obtaining submodule is used for respectively obtaining semantic association between the target POI and each neighbor node so as to obtain N semantic features;
and the aggregation sub-module is used for aggregating the N semantic features to obtain the image semantic features of the target POI.
Optionally, the N neighbor nodes are N pieces of map retrieval information, and the N pieces of map retrieval information are selected by a user in response to the target POI; alternatively, the first and second electrodes may be,
the N neighbor nodes are N POIs sequentially selected by a user within preset time, and the target POI and the N POIs belong to the same type.
Optionally, the POI semantic vector calculation apparatus 400 further includes:
the second obtaining module is used for obtaining basic information of the target POI, wherein the basic information of the target POI comprises at least one of the name of the target POI, the label of the target POI and the geographic position of the target POI;
the calculation module 402 is specifically configured to:
and calculating the semantic vector of the target POI according to the image semantic features of the target POI and the basic information of the target POI.
Optionally, the calculating module 402 is specifically configured to:
calculating a first vector representation of the target POI according to the graphic meaning characteristics of the target POI;
calculating a second vector representation of the target POI according to the basic information of the target POI;
and calculating a semantic vector of the target POI according to the first vector representation and the second vector representation.
Optionally, the semantic vector of the target POI is calculated through a pre-trained semantic model;
the training function of the semantic model is as follows:
Figure BDA0002557646750000141
wherein Q represents a feature matrix of a map retrieval information sample, P represents a feature matrix of a POI sample,
Figure BDA0002557646750000142
represents the kronecker product, RposRepresenting a positive sample feature matrix, RnegRepresenting a negative sample feature matrix, m representing the spacing between the positive and negative sample feature matrices, and l representing a loss function.
The POI semantic vector calculation apparatus 400 provided in the present application can implement each process in the above map retrieval method embodiments, and can achieve the same beneficial effects, and for avoiding repetition, the details are not repeated here.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
Fig. 10 is a block diagram of an electronic device according to an embodiment of the method of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 10, the electronic apparatus includes: one or more processors 701, a memory 702, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 10 illustrates an example of a processor 701.
The memory 702 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform a map retrieval method or a POI semantic vector calculation method provided herein. A non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to execute a map retrieval method or a POI semantic vector calculation method provided by the present application.
The memory 702 is a non-transitory computer-readable storage medium, and can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the map retrieval method in the embodiment of the present application (for example, the first module 301 and the search module 302 shown in fig. 8), and program instructions/modules corresponding to the POI semantic vector calculation method in the embodiment of the present application (for example, the first obtaining module 401 and the calculation module 402 shown in fig. 9). The processor 701 executes various functional applications and data processing of the problem analysis apparatus by running the non-transitory software programs, instructions, and modules stored in the memory 702, that is, implementing the map retrieval method or the POI semantic vector calculation method in the above method embodiments.
The memory 702 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by use of the electronic device, and the like. Further, the memory 702 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 702 may optionally include memory located remotely from the processor 701, which may be connected to the electronic device 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 electronic device may further include: an input device 703 and an output device 704. The processor 701, the memory 702, the input device 703 and the output device 704 may be connected by a bus or other means, and fig. 10 illustrates an example of a connection by a bus.
The input device 703 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic apparatus, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or other input devices. The output devices 704 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, the semantic vector of the POI is obtained by calculation according to the image semantic features of the POI, and the image semantic features of the POI aggregate the semantic features between the POI and the N neighbor nodes, so that the generation of the POI semantic vector is optimized, the expression of the POI semantic vector is richer, the accuracy and the confidence of the POI semantic vector can be improved, and a more accurate and efficient POI semantic vector index database can be constructed. Therefore, even if the map retrieval information input by the user is different, the retrieval prompt can be recalled accurately, the basic recall rate is effectively improved, the recall effect is improved, and the problems in the prior art are solved.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (17)

1. A map retrieval method, the method comprising:
calculating a semantic vector of the map retrieval information;
searching a target POI semantic vector matched with the semantic vector of the map retrieval information according to a pre-established information point POI semantic vector index library; the semantic vector of the target POI is obtained by calculation according to the image semantic features of the target POI, the image semantic features of the target POI aggregate the semantic features between the target POI and N neighbor nodes, and N is an integer greater than 1.
2. The method of claim 1, wherein the graphical semantic features of the target POI are obtained by:
acquiring N neighbor nodes of the target POI;
respectively acquiring semantic association between the target POI and each neighbor node to obtain N semantic features;
and aggregating the N semantic features to obtain the image semantic features of the target POI.
3. The method according to claim 1, wherein the N neighbor nodes retrieve information for N maps, the N map retrieval information being responded to by a user in response to a selection operation of the target POI; alternatively, the first and second electrodes may be,
the N neighbor nodes are N POIs sequentially selected by a user within preset time, and the target POI and the N POIs belong to the same type.
4. The method according to any one of claims 1 to 3, wherein the semantic vector of the target POI is calculated according to the graph semantic features of the target POI and the basic information of the target POI; wherein the basic information of the target POI comprises at least one of the name of the target POI, the address of the target POI, the tag to which the target POI belongs and the geographical position of the target POI.
5. The method of claim 4, wherein the semantic vector of the target POI is calculated by:
calculating a first vector representation of the target POI according to the graphic meaning characteristics of the target POI;
calculating a second vector representation of the target POI according to the basic information of the target POI;
and calculating a semantic vector of the target POI according to the first vector representation and the second vector representation.
6. The method according to any one of claims 1 to 3, wherein the calculating a semantic vector of the map retrieval information comprises:
calculating a first vector representation of the map retrieval information according to the map retrieval information;
after receiving the map retrieval information, calculating a second vector representation of the map retrieval information according to the current geographic position information;
calculating a semantic vector of the map retrieval information from the first vector representation and the second vector representation.
7. The method according to any one of claims 1 to 3, wherein the semantic vector of the target POI and the semantic vector of the map retrieval information are calculated by a pre-trained semantic model;
the training function of the semantic model is as follows:
Figure FDA0002557646740000021
Rneg＝Q·PT
l＝max(0,m-Rpos+Rneg)
wherein Q represents a feature matrix of a map retrieval information sample, P represents a feature matrix of a POI sample,
Figure FDA0002557646740000022
represents the kronecker product, RposRepresenting a positive sample feature matrix, RnegRepresenting a negative sample feature matrix, m representing the spacing between the positive and negative sample feature matrices, and l representing a loss function.
8. A method for calculating a semantic vector of a POI (point of information), which is characterized by comprising the following steps:
acquiring the image meaning characteristics of a target POI, wherein the image meaning characteristics of the target POI aggregate the semantic characteristics between the target POI and N neighbor nodes, and N is an integer greater than 1;
and calculating the semantic vector of the target POI according to the image semantic features of the target POI.
9. The method of claim 8, wherein the obtaining the graphical semantic features of the target POI comprises:
acquiring N neighbor nodes of the target POI;
respectively acquiring semantic association between the target POI and each neighbor node to obtain N semantic features;
and aggregating the N semantic features to obtain the image semantic features of the target POI.
10. The method according to claim 8, wherein the N neighbor nodes retrieve information for N maps, the N map retrieval information being responded to by a user in response to a selection operation of the target POI; alternatively, the first and second electrodes may be,
the N neighbor nodes are N POIs sequentially selected by a user within preset time, and the target POI and the N POIs belong to the same type.
11. The method according to any one of claims 8 to 10, further comprising:
acquiring basic information of the target POI, wherein the basic information of the target POI comprises at least one of the name of the target POI, the address of the target POI, a tag to which the target POI belongs and the geographical position of the target POI;
the calculating the semantic vector of the target POI according to the image semantic features of the target POI comprises the following steps:
and calculating the semantic vector of the target POI according to the image semantic features of the target POI and the basic information of the target POI.
12. The method according to claim 11, wherein the calculating the semantic vector of the target POI according to the graph semantic features of the target POI and the basic information of the target POI comprises:
calculating a first vector representation of the target POI according to the graphic meaning characteristics of the target POI;
calculating a second vector representation of the target POI according to the basic information of the target POI;
and calculating a semantic vector of the target POI according to the first vector representation and the second vector representation.
13. The method according to any one of claims 8 to 10, wherein the semantic vector of the target POI is calculated by a pre-trained semantic model;
the training function of the semantic model is as follows:
Figure FDA0002557646740000031
Rneg＝Q·PT
l＝max(0,m-Rpos+Rneg)
wherein Q represents a feature matrix of a map retrieval information sample, P represents a feature matrix of a POI sample,
Figure FDA0002557646740000032
represents the kronecker product, RposRepresenting a positive sample feature matrix, RnegRepresenting a negative sample feature matrix, m representing the spacing between the positive and negative sample feature matrices, and l representing a loss function.
14. A map retrieval apparatus, comprising:
the first calculation module is used for calculating semantic vectors of the map retrieval information;
the searching module is used for searching a semantic vector of a target POI matched with the semantic vector of the map retrieval information according to a pre-established information point POI semantic vector index library; the semantic vector of the target POI is obtained by calculation according to the image semantic features of the target POI, the image semantic features of the target POI aggregate the semantic features between the target POI and N neighbor nodes, and N is an integer greater than 1.
15. An apparatus for computing a semantic vector of a POI, comprising:
the system comprises a first acquisition module, a first search module and a second search module, wherein the first acquisition module is used for acquiring the image semantic features of a target POI, the image semantic features of the target POI aggregate the semantic features between the target POI and N neighbor nodes, and N is an integer greater than 1;
and the calculation module is used for calculating the semantic vector of the target POI according to the image semantic features of the target POI.
16. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7 or to enable the at least one processor to perform the method of any one of claims 8 to 13.
17. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 7 or the method of any one of claims 8 to 13.
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