CN110716999A

CN110716999A - POI positioning method based on position description containing qualitative position and quantitative distance

Info

Publication number: CN110716999A
Application number: CN201910837543.1A
Authority: CN
Inventors: 程若桢; 陈静
Original assignee: Wuhan University WHU
Current assignee: Wuhan University WHU
Priority date: 2019-09-05
Filing date: 2019-09-05
Publication date: 2020-01-21
Anticipated expiration: 2039-09-05
Also published as: CN110716999B

Abstract

The invention provides a POI positioning method based on position description containing qualitative position and quantitative distance, which is characterized by comprising the steps of constructing a semantic net based on ELLG based on a POI data set, constructing an instance of a concept in a model according to a POI ontology model, and using the instance as a node in the semantic net; performing multi-scale compression on the qualitative azimuth relationship based on the ELLG, and constructing the qualitative azimuth relationship in the semantic network; analyzing the position description into position semantics, a qualitative orientation relation and a quantitative distance relation; according to the semantic network, carrying out address similarity matching based on the POI body, and mapping the address in the position semantic to a corresponding example in the semantic network; and deducing the target POI meeting the quantitative distance relation in the qualitative direction through the qualitative direction relation in the semantic net and the ELLG-based space calculation based on the matched corresponding examples. The invention can be applied to positioning POI from qualitative position and quantitative distance description facing to a large-range area.

Description

POI positioning method based on position description containing qualitative position and quantitative distance

Technical Field

The invention relates to the field of similarity matching and spatial reasoning, in particular to a method for quickly positioning POI from position description containing qualitative position and quantitative distance under the condition of a POI data set of a large-range area.

Background

With the rapid development of mobile internet technology and spatial location technology, a great deal of information is generated that is directly or indirectly related to location. The location-related information describes the ubiquitous location in the form of geographic coordinates or location semantics such as a place name, an address, and the like. And POIs are closely related to people's social activities and are the focus of ubiquitous location descriptions. Compared with geographic coordinates, the POI is described by the spatial relationship among the place name, the address and the POI, so that the POI is more consistent with the daily habits of people. Therefore, the position description containing the position semantics and the spatial relationship is positioned to the POI, the conversion from the semantics to the position can be realized, and then various position-based technical services such as spatial position query, navigation instruction issuing, event position notification and the like are supported, and a foundation is laid for the analysis of large geographic spatiotemporal data.

In the existing research, the positioning to the POI is often realized by performing similarity matching on texts or word senses on position semantics in the position description and performing spatial reasoning according to a spatial relationship in the position description. Common descriptions of spatial relationships between POIs include relatively coarse qualitative orientations such as "east", "southeast", and more detailed quantitative distances in qualitative orientations such as "10 km north". Therefore, the spatial reasoning of the spatial relationship described above generally employs two methods: introducing a mathematical model of qualitative orientation and quantitative distance to realize space reasoning based on space calculation; or organizing the position semantics of the POI and the qualitative orientation relationship between the POI by utilizing the ontology and the semantic web, thereby realizing the space inference based on the semantic relationship.

However, the existing research often ignores the influence of the values in the addresses on the similarity matching, and can only adapt to the application of the POI facing a small data set, and is difficult to adapt to the requirements of similarity matching and spatial reasoning facing a large-range POI data set. In the aspect of similarity matching, similarity calculation on a large-scale address data set influences similarity matching efficiency, and a large amount of same information actually exists in different addresses, so that the similarity matching efficiency can be improved by avoiding repeated calculation of the same information. In the aspect of spatial reasoning, the method based on the semantic relationship improves the reasoning efficiency by establishing the qualitative orientation relationship among POI in advance, and can be more suitable for the spatial reasoning facing to a large-range area. However, a pair of qualitative orientation relationships exists between each pair of POIs, which means that the number of qualitative orientation relationships is usually quadratic to the number of POIs, and the semantic relationships are difficult to cover different quantitative distance conditions. Spatial reasoning when oriented to a large range of POI data sets therefore still presents two challenges: (1) the qualitative orientation relation of large data volume in the semantic network needs to be completely expressed, compressed and stored and efficiently retrieved; (2) qualitative orientation reasoning and quantitative distance calculation based on semantic web are required.

The present invention proposes that characteristics of ELLG help to address the challenges described above. ELLG discretizes the global space into a multi-scale grid based on a quadtree structure, and performs multi-scale indexing on the global space range through a grid code (k, column, row) consisting of grid scale, column number, and row number. The subdivision rule of ELLG makes it have the following characteristics: (1) the grids in the same scale have definite qualitative orientation relations, and the qualitative orientation relations among POI (point of interest) in different grids can be expressed by the relations, so that the compressive expression of the qualitative orientation relations with large data volume is realized; (2) the spatial resolution of the grid expresses well-defined quantitative distances and therefore may support quantitative distance calculations in qualitative orientations.

Related terms:

POI points of interest

ELLG equal longitude and latitude global discrete grid

S south

N North

E east

W West

NE northeast

NW northwest

SE southeast

SW southwest

Stanford CoreNLP Stanford university natural language processing kit

WordNet is English dictionary based on cognitive linguistics

Disclosure of Invention

The invention aims to overcome the defects of the existing method for positioning POI from position description, and provides a method for quickly positioning POI from position description containing qualitative position and quantitative distance under the condition of POI data set of large-scale area.

The technical scheme of the invention is a POI positioning method based on position description containing qualitative position and quantitative distance, which comprises the following steps:

step 1, constructing a semantic net based on ELLG based on a POI data set, wherein the implementation mode comprises the following substeps,

step a1, constructing an instance of a concept in the model according to the POI ontology model by using the POI data set as a node in a semantic net;

step a2, carrying out multi-scale compression of qualitative orientation relation based on ELLG, and constructing qualitative orientation relation in semantic net, including expressing NE, NW, SE, SW relation by using multi-scale grid, and expressing S, N, E, W relation by using maximum scale grid of ELLG;

step 2, analyzing the position description into position semantics, a qualitative azimuth relationship and a quantitative distance relationship;

step 3, according to the semantic web constructed in the step 1, carrying out address similarity matching based on the POI body, and mapping the address in the position semantic to a corresponding example in the semantic web;

step 4, based on the matched corresponding examples, deducing the target POI meeting the quantitative distance relation in the qualitative direction through the qualitative direction relation in the semantic net and the ELLG-based space calculation, wherein the realization mode comprises the following substeps,

b1, carrying out qualitative orientation reasoning based on the semantic net, wherein the qualitative orientation reasoning comprises the steps of reasoning NE, NW, SE and SW on the basis of the ELLG multi-scale grid, and reasoning S, N, E, W four qualitative orientation relations on the basis of the ELLG maximum-scale grid to obtain the multi-scale grid where the POI meeting the qualitative orientation relations are located;

and b2, carrying out quantitative distance inference based on ELLG on the basis of the grid set inferred in the qualitative direction in the step b2, carrying out quantitative distance inference in four qualitative directions of S, N, E, W on the basis of the grid with the maximum dimension of the ELLG, and carrying out quantitative distance inference in four qualitative directions of NE, NW, SE and SW on the basis of the multi-scale grid of the ELLG, thereby further inferring the target POI meeting the quantitative distance relationship.

Furthermore, the POI ontology model in step a1 contains two sub-categories of spatial information and location semantics, where the spatial information sub-concepts are geographic coordinates and ELLG-based multi-scale grid coding, the multi-scale grid coding describes the spatial range where the POI is located, and the sub-concepts of the location semantics include place name, address and category; the address is in a four-level structure, including sub-concepts such as country, city, street, and house number.

Furthermore, in step a2, multi-scale compression of qualitative orientation relation based on ELLG is realized as follows,

based on the maximum scale Lmax of the multiscale grid set GridSet and ELLG of the POI data set, the following steps are performed:

① initializing k to 0;

② obtaining grid set GridSet with k scale in GridSet_k，0≤k≤Lmax；

③ calculating GridSet_kThe obtained column nA is not less than column ni is not less than column B, rowA is not less than rowi is not less than rowB, (k, column ni, rowi) is a grid code with the scale of k;

④ if columnA < columnB and rowA < rowB, execute ⑤ and ⑥, otherwise, let k be k +1 and return to ②, if k be Lmax, execute ⑦;

⑤ through GridSet_kTo construct GridSet_k-1NE, NW, SE, SW relations among POI in the same line of grid in the middle, and add the collection DirectionSet of the qualitative orientation relation;

⑥ construction of GridSet by GridSetk_k-1NE, NW, SE, SW relations among POI in the same row of grids, and add the collection DirectionSet of the qualitative orientation relation;

⑦ GridSet through a grid set with a dimension Lmax_LmaxAnd constructing S, N, E, W relations among POI, and adding a qualitative orientation relation set Directionset.

Furthermore, in step 3, the implementation of address similarity matching based on the POI ontology includes the following steps,

①, replacing the abbreviation in the address to be matched with a full name through a preset mapping table;

② respectively calculating similarity of the country, city and street information in the address to be matched, and selecting the matching result with highest similarity of all levels of information;

③ obtaining a candidate address satisfying the matching result with the highest similarity of each level of information at the same time based on the four-level structure of the address in the POI body;

④ similarity calculation is carried out on the address to be matched and the candidate address, and the address with the highest similarity is selected as the best matching address.

And in step 3, when similarity calculation is carried out, the character similarity and the semantic similarity are integrated to obtain the final total similarity.

In step 3, the semantic similarity is calculated as follows,

splitting character strings string1 and string2 into word sets wordset1 and wordset2 respectively, calculating semantic similarity SemSim of each word1 in the wordset1 and each word2 in the wordset2 according to the following mode, obtaining maximum SemSimMax in the SemSim set, and synthesizing semantic similarity SemanticSimiarity of string1 and string2,

if word1 and word2 are both numeric, the following numeric semantic similarity calculation formula calculates SemSim,

if both word1 and word2 are included in WordNet, calculating SemSim based on the Wu-Palmer algorithm;

otherwise, let SemSim be 0. Where abs () represents the calculated absolute value. 7. A method of POI location based on location description comprising qualitative location and quantitative distance as claimed in claim 3, characterized in that: in step b1, the qualitative orientation reasoning implementation based on semantic web is as follows,

based on the address R of the reference POI, the qualitative bearing relation Direction, and the maximum dimension Lmax of the ELLG, the following steps are performed,

① if the Direction is one of S, N, E, W four qualitative orientation relations, let k equal to Lmax, execute ② and ③, end the process, if the Direction is one of NE, NW, SE, SW four qualitative orientation relations, initialize k equal to 0, when k < Lmax, iterate ② to ④;

② mapping R to a grid code1 with the scale k in which the R is located through semantic relation in a semantic network;

③, acquiring a grid code2 with a qualitative orientation relation to code1 in a semantic network, and adding a grid set;

④ let k be k +1 and return to ②.

Furthermore, in step b2, ELLG-based quantitative distance inference is implemented as follows,

according to the address R of the reference POI, the CodeSet of the grid set, the qualitative orientation relation Direction, the quantitative Distance and the maximum dimension Lmax of the ELLG, the following steps are executed,

① mapping R to grid code1(Lmax, column0, row0) with the size of Lmax by semantic relation in the semantic net, executing ② if the Direction is one of S, N, E, W qualitative orientation relations, and executing ③ to ⑤ iteratively for each grid code (k, column, row) in CodeSet if the Direction is one of NE, NW, SE, SW qualitative orientation relations;

②, calculating a target grid Code2 meeting the quantitative distance according to the following formula, if the Code2 contains POI, acquiring POI in the target grid in the semantic net, adding a POI set poiSet, and ending the process, otherwise, calculating the grid which is closest to Code2 and contains POI in the Code set as the target grid, acquiring the POI in the target grid through semantic relation in the semantic net, adding the POI set poiSet, and ending the process, wherein delta represents the grid number covered by the quantitative distance, pow () represents a power function, ceil () represents rounding up, PI represents a circumference ratio, and r represents the radius of the earth;

code2＝code1+delta

③ determining the range of codei in the grid with the dimension Lmax;

④ determining the spatial resolution of the grid at layer k-Lmax;

⑤ determining the minimum Distance minD and the maximum Distance maxD of codei and code 1. if minD ≦ Distance ≦ maxD and POI is contained in codei, ⑥ is performed;

⑥ adding codei into the grid set MultiCode, judging whether k is less than Lmax, if so, making k equal to k +1, obtaining four sub-grids of the codei, respectively making each sub-grid be the codei and recursively executing ③ to ⑤;

⑦, when i is size (codeset) -1, the POI in the grid with the largest size in the MultiCode is obtained through the semantic relation in the semantic net, and the target POI set poiSet is added.

The invention creatively constructs a semantic web based on ELLG, and realizes the positioning from the position description to the POI after analyzing, similarity matching and space reasoning the position description based on the constructed semantic web. When an example in a semantic network is constructed, a POI ontology model is provided, and the relation between multiple position semantics of a POI and an ELLG-based multi-scale grid is established; when a qualitative orientation relation in a semantic network is constructed, a multiscale compression method of the qualitative orientation relation based on ELLG is provided, and complete and compressed expression of the qualitative orientation relation of large data volume among POI is realized; when the similarity is matched, an address similarity matching method based on a POI ontology model is provided, the similarity matching efficiency is improved, and more accurate address similarity matching is realized by considering the space proximity degree of abbreviation and numerical expression in the address; during space reasoning, a qualitative direction reasoning method based on a semantic network and a quantitative distance reasoning method based on ELLG are provided, and rapid reasoning of quantitative distance in a qualitative direction is realized. The invention can support the application of positioning to POI from qualitative position and quantitative distance description facing to a large-range area.

Drawings

Fig. 1 is a diagram of a POI ontology model according to an embodiment of the present invention.

FIG. 2 is a schematic diagram of a multi-scale compression method for qualitative orientation relationship according to an embodiment of the present invention.

FIG. 3 is a flowchart of a multi-scale compression method for qualitative orientation relationship according to an embodiment of the present invention.

Fig. 4 is a flowchart of an address similarity matching method based on POI ontology according to an embodiment of the present invention.

Fig. 5 is a flowchart of similarity calculation according to an embodiment of the present invention.

FIG. 6 is a schematic diagram of a qualitative orientation inference method based on semantic Web according to an embodiment of the present invention.

FIG. 7 is a flow chart of a qualitative orientation inference method based on semantic Web according to an embodiment of the present invention.

FIG. 8 is a schematic diagram of an ELLG-based quantitative distance inference method in an embodiment of the present invention.

FIG. 9 is a flowchart of a quantitative distance inference method based on ELLG according to an embodiment of the present invention.

FIG. 10 is a flow chart of an embodiment of the present invention.

Detailed Description

The technical solution of the present invention is described in detail below with reference to the accompanying drawings and examples.

The embodiment of the invention provides a POI positioning method based on position description containing qualitative position and quantitative distance, which comprises the following steps:

step 1, constructing a semantic web based on ELLG based on a POI data set.

This step can be subdivided into two sub-steps:

step a1, constructing an example of a concept in a model according to a proposed POI ontology model by using a POI data set of a large-range area, wherein the example is used as a node in a semantic network;

in step a1, a POI ontology model (as shown in fig. 1) is proposed, which contains two sub-classes of spatial information and location semantics, which are expressed by the semantic relationships hasSpatial and hassematic, respectively. The spatial information includes sub-concepts such as geographical coordinates and ELLG-based multi-scale trellis coding, expressed by the semantic relations hasCode and hasCode, respectively. The multi-scale grid coding describes a multi-scale spatial range where the POI is located, is expressed by a semantic relation atLevelk, and is calculated according to a formula (1). Location semantics contain sub-concepts such as place names, addresses, and categories, expressed by the semantic relationships hasName, hasAddress, and hasCategory, respectively. The address adopts a four-level structure, comprises sub concepts such as country, city, street and house number, and is expressed by semantic relations of hasCountry, hasCity, hasStreet and hasHousenumber respectively, and the semantic relation 'belong to' expresses the spatial inclusion relation of all levels of information. The category expresses the type of social activity that people have in the location.

Wherein, the floor () represents the rounding-down, the pow () represents the power function, the% represents the remainder, (k, column, row) is the grid code with the scale k, column is the column number, row is the row number, and longitude and latitude are respectively the longitude and the latitude.

A2, constructing qualitative orientation relation in a semantic net according to the provided multi-scale compression method of the qualitative orientation relation, wherein the qualitative orientation relation comprises NE, NW, SE and SW four qualitative orientation relations among POI expressed by using a multi-scale grid, and S, N, E, W four qualitative orientation relations among POI expressed by using the maximum scale grid of ELLG, thereby constructing the qualitative orientation relation in the semantic net;

in step a2, the present invention preferably proposes a multi-scale compression method based on the qualitative orientation relationship of ELLG (as shown in FIG. 2), using a multi-scale grid to express the NE, NW, SE, SW four qualitative orientation relationships between POI, and using the maximum scale grid of ELLG to express S, N, E, W four qualitative orientation relationships between POI. The method is described below (as in fig. 3):

based on a multiscale mesh set GridSet (a trellis-coded construct with a scale from 0 to Lmax) of the POI data set and a maximum scale Lmax of the ELLG, the following steps are performed:

① initializing k to 0;

② obtaining grid set GridSet with k scale in GridSet_k，0≤k≤Lmax；

③ calculating GridSet_kThe coding range of (a), columnA is not less than columni not more than columnB, rowA is not less than rowi not more than rowB, (k, columni, rowi) is GridSet_kThe lower limit columnA and the upper limit columnB of the column number form a column number range, and the lower limit rowA and the upper limit rowB of the row number form a row number range;

④ if columnA < columnB and rowA < rowB, execute ⑤ and ⑥, then let k be k +1 and return to ②, otherwise, columnA or rowA be rowB, let k be k +1 and return to ②, if k be Lmax, execute ⑦;

⑤ through GridSet_k(FIG. 2(b)) to construct GridSet_k-1NE, NW, SE, SW qualitative positional relationship between POIs within the same row of grid (e.g. grid1 and grid2 in fig. 2 (a)). That is, when rowA is not more than rowi<When rowB, rowi is equal to rowA, if

Constructing a relation between Grid1(k, column1, rowi) and Grid2(k, column2, rowi +1) according to table 1, adding a qualitative orientation relation set DirectionSet, and then making rowi become rowi +1, and iteratively executing a relation construction process between grids; otherwise, the rowi is made to be rowi +1, and the relation construction process among the grids is executed in an iteration mode. Wherein columnA is not less than columnA 1 and not more than columnB, columnA is not less than columnA 2 and not more than columnB;

⑥ through GridSet_k(FIG. 2(b)) to construct GridSet_k-1NE, NW, SE, SW qualitative positional relationships between POIs within the same column of grids (e.g., grid1 and grid3 in fig. 2 (a)). That is, when columnA is less than or equal to columni<When columnB, columni is named columnA, if

Constructing a relation between Grid1(k, columni, row1) and Grid2(k, columni +1, row2) according to table 1, adding directive set, and then making columni equal to columni +1, and iteratively executing the relation construction process between grids; otherwise, let columni be columni +1, and iteratively execute the above-mentioned relationship construction process between grids. Wherein rowA is not less than rowW 1 and not more than rowB, rowA is not less than rowW 2 and not more than rowB;

⑦ GridSet through a grid set with a dimension Lmax_LmaxFour qualitative positional relationships S, N, E, W between POIs are constructed (fig. 2 (c)). That is, the relationship between Grid1(Lmax, column1, rowi) and Grid2(Lmax, column2, rowi) is constructed according to table 1, DirectionSet is added, rowi is changed to rowia +1, and the inter-Grid relationship is iteratively executedA relationship construction process until rowi ═ rowB; let columni ═ columnA, construct the relation of Grid1(Lmax, columni, row1) to Grid2(Lmax, columni, row2) and add DirectionSet according to table 1, then let columni ═ columni +1, iteratively execute the above-mentioned relation construction process between grids until columni ═ columnB.

Directionset is a set of qualitative orientation relationships expressed in a compressive manner.

TABLE 1. rules of qualitative orientation relationship between grids on the same scale

Step 2, analyzing the position description into position semantics, a qualitative orientation relation and a quantitative distance relation by using the Stanford CoreNLP;

and 3, based on the constructed semantic network, mapping the address in the position semantic to a corresponding example in the semantic network according to the address similarity matching method based on the POI ontology preferably provided by the invention. The method is described below (as in fig. 4):

① replacing the abbreviation in the address to be matched with a full name through a preset mapping table (table 2);

The best matching address is the corresponding example of the address to be matched in the semantic network.

TABLE 2 example mapping of abbreviations to full names

In the similarity calculation in the above process, for two strings string1 and string2, the similarity calculation process is as follows (as shown in fig. 5):

① calculating the character similarity StringSimilarity of string1 and string2 based on the edit distance method;

the character similarity calculation method used in the present invention is an edit distance-based method (Levenshtein 1966):

Sim(string1,string2)＝1-ed(string1,string2)/max(|string1|,|string2|)

in the formula, | string1| and | string2| are string lengths of string1 and string2, respectively, and ed (string1, string2) is the minimum number of editing operations required for string1 to convert to string 2.

② splits string1 and string2 into word sets wordset1 and wordset2, respectively, for each word1 in wordset1, calculates semantic similarity SemSim between it and each word2 in wordset2 in the following manner, and obtains a maximum SemSimMax in the SemSim set:

if both word1 and word2 are numbers, calculating SemSim according to the semantic similarity calculation formula (2)) of the numerical values provided by the invention;

otherwise, let SemSim be 0. Wherein abs () represents a calculated absolute value;

in addition to numerical values, the semantic similarity calculation method used in the present invention is the Wu-Palmer method (Wu and Palmer 1994):

in the formula, LCS (c1, c2) represents the minimum common inclusion of two concepts in the semantic dictionary, Depth (LCS (c1, c2)) represents the Depth of the minimum common inclusion in the semantic dictionary, and Distance (c1, c2) represents the Distance between two concept nodes in the semantic dictionary.

③ calculating semantic similarity between string1 and string2 according to equation (3) where Pairs is the number of words in wordset 1;

④, calculating total similarity, SimiarityTotal, according to formula (4), where W1 and W2 are the weights of character similarity and semantic similarity, respectively, and W1+ W2 is equal to 1.

SimiarityTotal is the total similarity of string1 and string 2.

SimilarityTotal＝StringSimilarity×W1+SemanticSimilarity×W2 (4)

And 4, based on the matched corresponding examples, deducing the target POI meeting the quantitative distance relation in the qualitative direction through the qualitative direction relation in the semantic network and the ELLG-based space calculation.

This step can be subdivided into two sub-steps:

b1, reasoning out a multi-scale grid where the POI meeting the qualitative orientation relation is located based on the semantic network;

in step b1, the present invention preferably proposes a qualitative orientation inference method based on semantic web (as shown in fig. 6), which performs inference of NE, NW, SE, SW four qualitative orientations based on the multi-scale grid of ELLG, and performs inference of S, N, E, W four qualitative orientation relationships based on the maximum-scale grid of ELLG, thereby inferring the multi-scale grid where POIs satisfying the qualitative orientation relationships are located. The method is described below (see fig. 7):

based on the address R of the reference POI (corresponding instance matched), the qualitative orientation relation Direction, and the maximum dimension Lmax of the ELLG, the following steps are performed:

②, mapping R to a grid code1 with the k dimension in the semantic network, wherein in the embodiment, the R is mapped to the grid code1 with the k dimension in the semantic network through HasAddress, HasSemantic, HasSpatial, HasCode and atLevelk semantic relations;

④ let k be k +1 and return to ②.

CodeSet is the set of grids where POI in Direction is located in the qualitative orientation relation with R.

And b2, further deducing the target POI meeting the quantitative distance relation through ELLG-based spatial calculation according to the multi-scale grid.

In step b2, the present invention preferably proposes an ELLG-based quantitative distance inference method (see fig. 8), which performs S, N, E, W quantitative distance inference in four qualitative orientations based on the maximum-scale grid of the ELLG and performs NE, NW, SE, SW quantitative distance inference in four qualitative orientations based on the multi-scale grid of the ELLG on the basis of the grid set of the qualitative orientation inference, thereby further inferring target POIs satisfying the quantitative distance relationship. The method is described below (see fig. 9):

based on the address R (corresponding matched instance) of the reference POI, the CodeSet of the grid set, the Direction of the qualitative orientation relation, the Distance of the quantitative Distance, and the maximum dimension Lmax of the ELLG, the following steps are executed:

① mapping R to the mesh code1(Lmax, column0, row0) with Lmax in the semantic net, in the embodiment, mapping is realized by five semantic relations of hasAddress, hasSemantic, hasSpatial, hasCode and atlevelLmax, if the Direction is one of S, N, E, W four qualitative orientation relations, ② is executed, if the Direction is one of NE, NW, SE, SW four qualitative orientation relations, for each mesh code (k, column, row) in CodeSet, iteration is executed ③ to ⑤, the mesh number i 0 can be initialized, wherein, size (CodeSet) in FIG. 9 represents the statistical number of meshes in the codeSet set, therefore, in the iteration process, 0< i < size (CodeSet);

②, calculating a target grid Code2 satisfying the quantitative distance according to the formula (5). if Code2 contains POI (such as B in FIG. 8 (a)), the POI in the target grid is obtained through semantic relationship in the semantic net, a POI set poiSet is added, and the process is ended, otherwise, the grid which is closest to Code2 and contains POI in the codeSet is calculated as the target grid, and the POI in the target grid is also obtained through semantic relationship in the semantic net, the poiSet is added, and the process is ended, wherein delta represents the number of grids covered by the quantitative distance, pow () represents a power function, ceil () represents upward rounding, PI represents the circumference PI (a value 3.1415926), and the earth radius r takes 6371 km;

③ calculating codei in the grid range with dimension Lmax according to formula (6), where column_nearAnd column_farDenotes the column number range, row_nearAnd row_farIndicates a range of line numbers;

④, calculating the spatial resolution of the grid when k is Lmax according to formula (7);

resolution＝PI×r/pow(2,k) (7)

⑤ calculate the minimum Distance minD and the maximum Distance maxD of codei and code1 according to equation (8). if minD ≦ Distance ≦ maxD and POI is contained in codei (as B1, B2, and B3 in FIG. 8 (a)), ⑥ is performed;

⑥ adding codei into the lattice set MultiCode, judging whether k is less than Lmax, if so, making k equal to k +1, obtaining four sub-lattices of codei (such as B21, B22, B23 and B24 in FIG. 8 (B)), making each sub-lattice be codei and recursively executing ③ to ⑤;

⑦ when i is size (codeset) -1, the POI in the mesh with the largest size in MultiCode (such as b1, b2 and b3 in fig. 8 (c)) is obtained by semantic relationship in the semantic web, and poiSet is added.

poiSet is a set of target POIs with a qualitative positional relationship to R of Direction and a quantitative Distance to R of Distance or close to Distance (if POIs that fully satisfy the quantitative Distance do not exist).

In specific implementation, the automatic operation of the process can be realized by adopting a software mode. The apparatus for operating the process should also be within the scope of the present invention.

To facilitate understanding of the technical effects of the present invention, a practical example of the method flow provided by the embodiment is provided as follows, see fig. 10:

step 1, constructing a semantic web based on ELLG based on 51019 POI of an area with a latitude range of 50 degrees N-58 degrees N and a longitude range of-7 degrees W-0 degrees W. This step can be subdivided into two sub-steps:

in step a1, according to the proposed POI ontology model, for each POI in the POI data set, instances of geographic coordinates, multi-scale grid codes, place names, addresses, countries, cities, streets and house numbers, categories, etc. are constructed, and semantic relationships between the instances are defined. Selecting 27 as the maximum scale of ELLG, and calculating the multi-scale grid coding based on formula (1);

in step a2, according to the proposed multi-scale compression method of qualitative orientation relationship, for a multi-scale grid set GridSet composed of grid codes from 0 to 27 in the POI data set, when k is greater than or equal to 0 and less than or equal to 27, for each k, a grid set GridSet with k in the GridSet is obtained_kAnd building NE, NW, SE, SW relation, adding Directionset; GridSet through grid set with scale 27₂₇To construct S, N, E, W a relationship, adding a DirectionSet. The DirectionSet contains 7,997,835 qualitative azimuth relationships, and the number of qualitative azimuth relationships after compression is 0.307% of the number of qualitative azimuth relationships before compression.

In specific implementation, the maximum scale of the POI data set and the ELLG can be determined according to actual conditions.

Step 2, using Stanford CoreNLP to analyze the position description of 400km normal west of 7 Market Sq, Llandovery and UK into position semantics of 7 Market Sq, Llandovery and UK, qualitative orientation of 'normal west' and quantitative distance of '400 km';

in specific implementation, the position semantics, the qualitative direction and the quantitative distance in the position description can be determined according to the actual situation.

Step 3, mapping the position semantics '7 Market Sq, Llandover, UK' into corresponding examples in a semantic network according to the proposed address similarity matching method;

① through Table 2, the abbreviation "Sq" is replaced with the full name "Square", i.e. the address to be matched is "7 MarketSquare, Llandover, UK";

② respectively carrying out similarity calculation on the national "UK", the urban "Llandovery" and the street "Market" based on national data, urban data and street data in the semantic network, and selecting the matching result with the highest similarity of all levels of information, namely the "UK", the "Llandovery" and the "Market" respectively;

③ candidate addresses meeting the requirements of 'UK', 'Llandover' and 'Market Square' in the semantic network, including '9 Market Square, Llandover, UK', '1 Market Square Llandover, UK';

④ similarity calculation is carried out on the address to be matched and the two candidate addresses respectively, and the address '9 Market Square, Llandover, UK' with the highest similarity is selected as the best matching address, namely the corresponding example in the semantic network.

The time for the above similarity matching was 4.74s, which was 27.40% of the time for the text matching based on the entire address.

In the similarity calculation, taking two character strings "17 Main Street" and "20 Denewood Avenue" as examples, the similarity calculation process is as follows:

① calculating the character similarity StringSimilarity of "17 Main Street" and "20 Denewood Avenue" to be 0.11 based on the edit distance method;

② splits "17 Main Street" into the word sets wordset1{ "17", "Main", "Street" }, and "20 Denewwood Avenue" into the word sets wordset2{ "20", "Denewwood", "Avenue" }. for each word1 of wordset1, the semantic similarity SemSim between it and each word2 of wordset2 is calculated in the following manner, and the maximum SemSimMax in the SemSim set is obtained;

if both word1 and word2 are numbers, such as "17" and "20", then SemSim is calculated according to equation (2); if both word1 and word2 are included in WordNet, such as "Street" and "Avenue," SemSim is calculated based on the Wu-Palmer method; otherwise, let SemSim be 0;

③ semantic similarity SemanticSimiarity of "17 Main Street" and "20 Denewood Avenue" is calculated as formula (3) to be 0.61, where Pairs is 3;

④ the total similarity SimiarityTotal of "17 Main Street" and "20 Denewood Avenue" was calculated to be 0.36 according to equation (4), where W1 and W2 both took the value of 0.5.

In specific implementation, the values of W1 and W2 can be determined according to actual conditions.

And 4, deducing the target POI meeting the qualitative direction 'northwest' and the quantitative distance '400 km' through the qualitative direction relation in the semantic network and ELLG-based space calculation based on the matched corresponding examples '9 Market Square, Llandovery and UK'. This step can be subdivided into two sub-steps:

in step b1, according to the proposed semantic web-based qualitative orientation inference method, a multi-scale grid where POIs satisfying the qualitative orientation "northwest" are located is inferred:

① initializes k to 0, and when k <27, for each k, ② to ④ are performed;

② mapping 9Market Square, Llandovery, UK to grid code1 with k dimension through five semantic relations of hasAddress, hasSemantic, hasSpatial, hasCode and atLevelk in the semantic network;

③, acquiring a lattice code2 with the qualitative orientation relation of code1 being NW from the semantic web, and adding CodeSet;

④ let k be k +1 and return to ②.

In one embodiment, the above process is applied to one of NE, NW, SE, SW, and if the qualitative orientation relationship is S, N, E, W, k is directly set to 27, and then ② and ③ are performed.

In step b2, according to the proposed ELLG-based quantitative distance inference method, a target POI satisfying a quantitative distance of "400 km" is further inferred based on CodeSet:

① maps "9 Market Square, Llandovery, UK" to its mesh code1(27,131386670,105878541) with the scale of 27 through five semantic relations of hasAddress, hasSemantic, hasSpatial, hasCode and atLevel27 in the semantic net successively, for each mesh code (k, column, row) in the codeSet, ② to ④ are executed, taking (7,124,103) in the codeSet as an example;

② calculation (7,124,103) in accordance with equation (6) at a grid scale of 27, column_near＝131071999，column_far＝130023424，row_near＝108003328，row_far＝109051903；

③, calculating the spatial resolution of the mesh at a level of 27 as 0.15m according to equation (7);

④ calculating minD 320150.23m and maxD 514781.95m according to equation (8). since minD 400km maxD is included in (7,124,103), ⑤ is performed, otherwise the grid is abandoned;

⑤ adding (7,124,103) to the gridding set MultiCode and obtaining four sub-gridding (8,248,206), (8,249,206), (8,248,207) and (8,249,207), and recursively executing ② to ④ for each sub-gridding;

⑥ when i ═ size (codeset) -1, the address "101 Braepark Road, balllycare, UK" of the target POI in the maximum-sized grid (scale 21) in MultiCode is obtained in the semantic web by five semantic relations of atLevel21, hasCode, hasspread, hassematic and hasAddress.

The spatial inference time is 11 ms.

In specific implementation, the above process is applied to determine the orientation relationship to be one of NE, NW, SE, SW; if the qualitative orientation relationship is one of S, N, E, W, calculating the target grid Code2 satisfying the quantitative distance directly according to formula (5), and if the Code2 does not contain POI, acquiring the grid closest to Code2 and containing POI in the CodeSet as the target grid. And then acquiring the address of the target POI in the target grid through five semantic relations of atLevelk, hasCode, hasSpatial, hasSemantic and hasAddress in the semantic network.

Through the specific implementation, the proposed POI ontology model can establish the relation between position semantics and an ELLG-based multi-scale grid, so that support is provided for the compressive expression of qualitative orientation relation and the rapid calculation of quantitative distance; the provided multi-scale compression method of the qualitative azimuth relationship can ensure the accuracy of the qualitative azimuth relationship while compressing the quantity of the qualitative azimuth relationship; the provided address similarity matching method improves the similarity matching efficiency by avoiding repeated calculation of the same information in different addresses, and realizes more accurate address similarity matching by considering the spatial proximity degree of numerical expression; the qualitative orientation reasoning method based on the semantic network and the quantitative distance reasoning method based on the ELLG can adapt to the rapid reasoning of different quantitative distance conditions on the qualitative orientation. Therefore, the method can effectively support the application of positioning to the POI from the qualitative position and the quantitative distance description facing to a large-range area.

Claims

1. A method of POI location based on location description including qualitative location and quantitative distance, comprising the steps of:

2. A method of POI location based on location description comprising qualitative location and quantitative distance as claimed in claim 1, characterized in that: the POI ontology model in the step a1 comprises two sub-categories of spatial information and position semantics, wherein the spatial information sub-concepts comprise geographic coordinates and ELLG-based multi-scale grid coding, the multi-scale grid coding describes a spatial range where the POI is located, and the sub-concepts of the position semantics comprise a place name, an address and a category; the address is in a four-level structure, including sub-concepts such as country, city, street, and house number.

3. A method of POI location based on location description comprising qualitative location and quantitative distance as claimed in claim 2, characterized in that: in step a2, multi-scale compression of qualitative orientation relation based on ELLG is realized as follows,

① initializing k to 0;

② obtaining grid set GridSet with k scale in GridSet_k，0≤k≤Lmax；

4. A method of POI location based on location description comprising qualitative location and quantitative distance as claimed in claim 3, characterized in that: in step 3, the method for implementing address similarity matching based on the POI ontology comprises the following steps,

5. A method of POI localization based on location descriptions including qualitative aspects and quantitative distances as claimed in claim 4, characterized in that: in step 3, when similarity calculation is performed, the character similarity and the semantic similarity are integrated to obtain the final total similarity.

6. A method of POI localization based on location descriptions including qualitative aspects and quantitative distances as claimed in claim 5, characterized in that: in step 3, the semantic similarity is calculated in the following manner,

otherwise, let SemSim be 0. Where abs () represents the calculated absolute value.

7. A method of POI location based on location description comprising qualitative location and quantitative distance as claimed in claim 3, characterized in that: in step b1, the qualitative orientation reasoning implementation based on semantic web is as follows,

④ let k be k +1 and return to ②.

8. The method of claim 7 wherein the POI location based on location description comprises a qualitative location and a quantitative distance, wherein: in step b2, ELLG-based quantitative distance inference is implemented as follows,

code2＝code1+delta

③ determining the range of codei in the grid with the dimension Lmax;

④ determining the spatial resolution of the grid at layer k-Lmax;