Patent Publication Number: US-2019171755-A1

Title: Systems and methods for determining relevance of place data

Description:
TECHNICAL FIELD 
     The described embodiments generally relate to map data and, more particularly, to systems, methods, and machines for determining relevance of data regarding (e.g., that describes) one or more places on a geographic map. 
     BACKGROUND 
     Beyond just address and road information, certain map-based services operate by using additional information regarding locations on a geographic map, such as whether a location on the geographic map is a place of business and, if so, whether the business is still open, what are its business hours, what type of business is it, whether the business is accessible from a location from a public road, and whether the business is accessible by the public. Such additional information is usually included in, or provided as, place information. Map-based services, such as a ride service, a ride-sharing service, or a delivery service, may require place information for operation or may use place information to improve the quality of results, accuracy of results, or overall performance. 
     Unfortunately, the usefulness of place information can be highly dependent on its accuracy and relevance, and place information accuracy can vary between different data sources providing place information, and place information relevance can depend on accuracy (e.g., inaccurate place information is not relevant for use). This is particularly true when a data source providing place information to the map-based service is maintained by a third party, or the place information data source is based on (e.g., populated or updated) by crowd sourcing. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various ones of the appended drawings merely illustrate various embodiments of the present disclosure and cannot be considered as limiting its scope. 
         FIG. 1  is a block diagram illustrating an example networked computing environment  100  that includes a place data system, according to some embodiments. 
         FIG. 2  is a diagram illustrating an example relevance determination system for determining relevance of place data, according to some embodiments. 
         FIGS. 3-5  are flowcharts illustrating example methods for determining relevance of place data, according to some embodiments. 
         FIG. 6  is a block diagram illustrating components of an example machine used to implement some embodiments. 
     
    
    
     The headings provided herein are merely for convenience and do not necessarily affect the scope or meaning of the terms used. 
     DETAILED DESCRIPTION 
     The description that follows describes systems, methods, techniques, instruction sequences, and computing machine program products that illustrate various embodiments for determining relevance of data (hereafter, “place data”) regarding one or more places on a geographic map. For various embodiments described herein, place data is maintained as one or more place data records (hereafter, “place records”), where each place record can comprise data regarding a single place located on a geographic map (hereafter, “map”). 
     Various embodiments can determine relevance of place data by determining whether a place record, including the record&#39;s place data, is relevant based on a set of features associated with the place record. For a given place record, a set of features may be generated (e.g., derived or extracted) based on values of one or more attributes (e.g., record field or fields) included in the given place record. Accordingly, a set of features generated for a place record can represent information extracted from, or derived based on, one or more values provided by the place record with respect to a place on a map. For some embodiments, a given place record is processed by at least one classifier, which receives as input a set of features of the given place record and outputs a prediction score indicating the certainty or probability that the given place record is associated with, or belongs to, a particular class (e.g., class label). In this way, the at least one classifier can predict the association of the given place record to the particular class based on the set of features of the given place record, which can function as signals or suggestions for or against the class association. The certainty/probability of association between a given place record and a particular class assists some embodiments in determining whether the given place record is relevant or non-relevant. For instance, where the particular class indicates that the given place record is relevant, a prediction score for a given place record may represent the given place record&#39;s relevance score. Where the particular class indicates that the given place record is relevant, the prediction score can also represent the given place record&#39;s trustworthiness, which can determine how much weight is given to the given place record&#39;s attribute values. 
     Though various embodiments are described herein with respect to using a classifier to determine place record relevance, other embodiments may use other types of machine learning (ML) models instead of, or in addition to, the classifier. For some embodiments, the classifier comprises a binary classifier that can associate a place record to a positive or a negative class. Depending on the embodiment, the classifier may be implemented using logistic regression, random decision forest, or gradient boosted trees. For instance, an embodiment using gradient boosted trees can implement the classifier such that the classifier receives a category value as a feature (e.g., “education” category is associated with the value 1, “shops and services” category is associated with the value 2, “airport” category is associated with the value 3, etc.). Alternatively, each category may be represented by its own feature (e.g., “is_education” which can have a value of true or false, “is_shops_and_services” which can have a value of true or false, “is_airport” which can have a value of true or false, etc.), and the classifier may receive a set of such features. 
     In some embodiments, a relevance of a place record is determined relative to the place record&#39;s intended use, such as its use by a specific type of service. For instance, the relevance of the given place record may be determined based on whether the place record is, or would be, a relevant origin or destination for a location-based service, such as a mapping service, a transportation or transportation arrangement service (e.g., ride or ride-sharing service), a delivery service (e.g., package or food delivery), or a directory service. 
     With respect to use by a ride or ride-sharing service, for example, a given place record may be relevant if the given place record describes a place on a map that is open to the public and that a rider would want to go to. Examples of such places could include, without limitation, a restaurant, a hotel or motel, a public transit station, an airport, a venue (e.g., for sports or concert), a clinic, a hospital, a gym, a retail store, and an office building. With respect to use by a ride or ride-sharing service, a place record that describes a place that is closed may be relevant because it can be used to inform a rider that the place is closed (e.g., before the rider specifies that place as their intended destination). Additionally, with respect to use by a ride or ride-sharing service, a place record that inaccurately describes the location of a place may be relevant because it can help train a classifier of an embodiment in estimating the accuracy of one or more attribute values of the place record. 
     In regard to non-relevant place records, a place record may be non-relevant with respect to use by a ride or ride-sharing service if, for example, the place record describes a place that is a private location, such as an individual&#39;s home, or a small business run out of an individual&#39;s home. With respect to use by a ride or ride-sharing service, a place record may be non-relevant if the place record describes a temporary, one-time event or a short-time event. Additionally, with respect to use by a ride or ride-sharing service, a place record may be non-relevant if the place record describes a place that does not really exist. Examples of such non-relevant place records can include, without limitation, a place record that is vaguely named (e.g., “favorite sunset spot”), that refers to a flight or a trip, or that refers to a business no longer in operation. 
     Accordingly, for various embodiments, the classifier is built (e.g., trained) such that the classifier can identify the relevance of place records according to their intended use. The classifier may be built using training data comprising a set of place records having confirmed or trusted associations with class labels (e.g., confirmed associations with positive and negative class labels). For a given place record, a prediction score provided by the classifier can be used to identify whether the given place record is a non-relevant place record that should be filtered out before being used for its intended use, such as use by a networked computer system that facilitates a ride service, a ride-sharing service, a delivery service, or another type of location-based service. An embodiment may be particularly useful in filtering out non-relevant place records generated, maintained, or otherwise provided by a third party. For place records that are user-generated or user-maintained (e.g., crowd-sourced place records), which may be a type of place records provided by a third party, the place records may have attributes (e.g., fields) with missing, inaccurate (e.g., outdated), or fabricated information. For instance, a user-generated or user-maintained place record may include poor geocoding information or be missing information regarding what type of place is described. As a result, information extracted or derived from one or more attributes (e.g., during feature generation) may be noisy and vary in quality. Consequently, user-generated/maintained place records can include one or more place records that are not relevant (e.g., not desirable or useful) for their intended purpose, such as use by a networked computer system that facilitates a location-based service (e.g., ride or ride-sharing service). 
     Various embodiments described herein can improve the ability of a computer system to determine relevance of a place data that describes a place on a geographic map. Additionally, various embodiments described herein can assist in building a comprehensive database of relevant place data, which may be utilized to accurately describe potential destinations for a location-based service, such as a ride or ride-share service. Accordingly, various embodiments can also improve a computer system&#39;s ability to build a comprehensive database of relevant place data. 
     For example, an embodiment may be used in conjunction with a place data process pipeline used to process (e.g., ingest, match and combine, filter for relevance, and analyze for accuracy) place records obtained from a data source, such as a third-party data source for place data, prior to the place records being used in the comprehensive database. Where place records are sourced from multiple data sources (e.g., third-party providers), a place data process pipeline may comprise matched place records from the different data sources to identify place records that refer to the same physical location. Place records may be matched, for instance, based on one or more attribute values included in place records, such as place names, place addresses, place types, or place geographic coordinates. For each set of matched place records that results, the place data process pipeline can combine the information of the set of matched place records (e.g., by selecting the best latitude and longitude coordinates, best name, and best address) to output a single place record to describe the place corresponding to the physical location and originally described by the set of matched place records. With respect to the place data process pipeline, an embodiment described herein may be used to filter out non-relevant place records received from each data source prior to place records being matched and combined. Alternatively, an embodiment described herein may be used to filter out non-relevant place records subsequent to place records from different data sources being matched and combined. 
     For some embodiments, the at least one classifier comprises a binary classifier such that a prediction score that surpasses a predetermined classification threshold indicates a given place record&#39;s association with a positive class, and a prediction score that does not surpass the predetermined classification threshold indicates the given place record&#39;s association with a negative class. Depending on the embodiment, the positive class may represent relevance and the negative class may represent non-relevance, or vice versa. Additionally, some embodiments may include a score range such that a prediction score that surpasses the upper bound of the range indicates a given place record&#39;s association with a positive class, a prediction score that does not surpass the lower bound of the score range indicates the given place record&#39;s association with a negative class, and a prediction score that is within the score range indicates that the given place record&#39;s association is ambiguous and can be associated either positively or negatively. A place record having an ambiguous class association may be a place record describing a place that, with minimal analysis or evidence (e.g., online evidence), can be moved into the positive class, moved into the negative class, ignored, or removed (e.g., from storage) altogether. 
     For some embodiments, at least one class (e.g., positive, negative, or ambiguous class) comprises a plurality of sub-labels which can explain why the given place record is labeled a certain way. Such sub-labels can reduce ambiguity in labeling and may be used for detailed analysis. Detailed analysis can include, for example, comparing the specific features between relevant place records that are sub-labeled as open versus relevant place records that are sub-labeled closed, or comparing the specific features between non-relevant records that are sub-labeled private versus non-relevant records that are sub-labeled temporary events. 
     Table 1 below provides examples of positive class sub-labels that may be used by an embodiment. 
     
       
         
           
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Example Positive 
                   
               
               
                 Class Sub-Label 
                 Sub-Label Description 
               
               
                   
               
             
            
               
                 RELEVANT 
                 This sub-label is associated with a place 
               
               
                   
                 record describing a place that is a 
               
               
                   
                 customer-facing, commercial location, 
               
               
                   
                 such as a store, restaurant, or an office 
               
               
                   
                 building, or a public place, such as a 
               
               
                   
                 park, downtown area, or a stadium. The 
               
               
                   
                 place record is assumed to be correctly 
               
               
                   
                 named and to have reasonably accurate 
               
               
                   
                 address information, which may be 
               
               
                   
                 confirmed by an authority website or 
               
               
                   
                 street-side imagery. 
               
               
                 RELEVANT - INCORRECT 
                 This sub-label is associated with a place 
               
               
                 LOCATION 
                 record describing a place that exists but 
               
               
                   
                 the place record has outdated or 
               
               
                   
                 incorrect location information (e.g., 
               
               
                   
                 place may have moved from its original 
               
               
                   
                 location). The place&#39;s existence and 
               
               
                   
                 correct address may be confirmed by an 
               
               
                   
                 Internet-based search. 
               
               
                 RELEVANT - CLOSED 
                 This sub-label is associated with a place 
               
               
                   
                 record describing a place that is closed 
               
               
                   
                 (e.g., reasonably recently), and this 
               
               
                   
                 closure may be confirmed. This place 
               
               
                   
                 may be associated with a franchise (e.g., 
               
               
                   
                 local or national chain) and the place 
               
               
                   
                 may be a specific franchise location that 
               
               
                   
                 has been closed. The closure may be 
               
               
                   
                 stated, for example, in a news article. 
               
               
                 RELEVANT - PRIVATE 
                 This sub-label is associated with a place 
               
               
                 PRACTICE 
                 record describing a place associated 
               
               
                   
                 with a private practice by a professional, 
               
               
                   
                 such as a physician, a therapist, a dentist, 
               
               
                   
                 or an attorney. These place records may 
               
               
                   
                 be confirmed based on affiliation with 
               
               
                   
                 an open clinic or business, such as by 
               
               
                   
                 being listed on an official website. 
               
               
                   
               
            
           
         
       
     
     Table 2 below provides examples of negative class sub-labels that may be used by an embodiment. 
     
       
         
           
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 Example Negative 
                   
               
               
                 Class Sub-Label 
                 Sub-Label Description 
               
               
                   
               
             
            
               
                 NOT RELEVANT - 
                 This sub-label is associated with a place 
               
               
                 PRIVATE LOCATION 
                 record describing a place that is a private 
               
               
                   
                 location (e.g., private residence) and the 
               
               
                   
                 place record includes a proper name for 
               
               
                   
                 the place (e.g., a fancy name for an 
               
               
                   
                 individual&#39;s house, such as “Joe&#39;s party 
               
               
                   
                 house”). Such places may include 
               
               
                   
                 private locations from which an 
               
               
                   
                 individual operates a business (e.g., 
               
               
                   
                 private contractor or one-person 
               
               
                   
                 business). 
               
               
                 NOT RELEVANT - 
                 This sub-label is associated with a place 
               
               
                 TEMPORARY 
                 record describing a location of a 
               
               
                   
                 temporary or one-time event, such as a 
               
               
                   
                 marathon, race, festival, sports event, or 
               
               
                   
                 concert. 
               
               
                 NOT RELEVANT - DOES 
                 This sub-label is associated with a place 
               
               
                 NOT EXIST 
                 record describing a non-existent place. 
               
               
                   
                 The described place may have no online 
               
               
                   
                 presence or an associated authority site. 
               
               
                   
                 Such place records may have vague 
               
               
                   
                 descriptions or names (es., “favorite 
               
               
                   
                 sunset spot” or “work”). 
               
               
                   
               
            
           
         
       
     
     Table 3 below provides examples of ambiguous sub-labels that may be used by an embodiment. 
     
       
         
           
               
               
             
               
                 TABLE 3 
               
               
                   
               
               
                 Example Ambiguous Sub-Label 
                 Sub-Label Description 
               
               
                   
               
             
            
               
                 MINIMAL - SMALL 
                 This sub-label is associated with a place 
               
               
                 BUSINESS 
                 record describing a place that is a small 
               
               
                   
                 business, such as one that does not have 
               
               
                   
                 an online presence. Such a place may or 
               
               
                   
                 may not be open, may or may not have 
               
               
                   
                 an official website, but may be 
               
               
                   
                 mentioned on a non-authority website. 
               
               
                 MINIMAL - PRIVATE 
                 This sub-label is associated with a place 
               
               
                 PRACTICE 
                 record describing a professional 
               
               
                   
                 individual, such as a physician or 
               
               
                   
                 attorney, who is not affiliated with a 
               
               
                   
                 practice. This professional individual 
               
               
                   
                 may have an online rating, such as on a 
               
               
                   
                 non-authority website, but there may be 
               
               
                   
                 no official website. 
               
               
                   
               
            
           
         
       
     
     As noted herein, one or more features for a given place record may be generated based on one or more attribute values of a place record. For instance, a feature may comprise a value extracted from, or a value derived based on, one or more values of one or more attributes (hereafter, “attribute values”) of the given place record. Additionally, at least one feature in the set of features may be normalized (e.g., between a value range of 0 to 1) to facilitate its use by a classifier according to an embodiment. For instance, a feature may be generated by extracting an attribute value from a place record, the attribute value being normalized between a range of 0 and 1. 
     For various embodiments, the set of features generated for a place record include one or more features that are determined (e.g., by offline regression analysis) to be useful in identifying relevant or non-relevant place records. The one or more attributes selected for use during generation of the one or more features may be less than all the attribute values included in a given place record. Selection of attributes used for feature generation of a particular feature may depend on the data source providing the given place record. For example, a particular feature may be generated for a first place record provided by a first data source (e.g., managed by a first third-party) based on values from a first set of attributes of the first place record, while the same particular feature may be generated for a second place record provided by a second data source (e.g., managed by a second third-party) based on values from a second (alternative) set of attributes of the second place record. The first and second sets of attributes may overlap or be mutually exclusive with respect to the attributes they include. An attribute included in place records provided by a first data source may not be an attribute included in place records provided by a second data source, and vice versa. Additionally, a particular feature generated for a first place record provided by a first data source may not be a feature generated for a second place record provided by a second data source. 
     Examples of features generated (e.g., by extraction or derivation) for a place record can include, without limitation: whether information is missing (e.g., whether a website address is missing from the place record or whether a portion of an address provided by the place record is missing, such as a street name, zip code, street number, or locality name); whether a locality name provided by the address is valid; whether the locality name provided by the address is found in all cities of a particular country (e.g., the U.S.); number of social media accounts associated with the described place; a characteristic of an attribute value (e.g., place name) provided by the place record (e.g., whether all in lower case, whether containing only numbers, number of words, or average length of each word); whether the place record provides an airport code (e.g., IATA code); whether an indication of a private location is present (e.g., “flight,” “house,” “spot,” or “trip” in an attribute value); whether an indication of a temporary event is present (e.g., marathon, concert, festival, voting, meeting, 5 k, or 10 k); whether an indication of a private practice is present (e.g., “MD,” “PhD,” “PA,” “CPA,” “OTR,” “CRNA,” or “LCPC” in an attribute value); whether there is a fuzzy match between a website address provided by the place record and the place name provided by the place record (e.g., it is a strong indication that the described place exists and is current when a website URL matches the place&#39;s name based on a normalized Levenshtein score); whether the place described is associated with a franchise (e.g., is a chain store or restaurant); a count for the number of times the place described by the place record has been visited by a unique individual; a category identifier (e.g., “education,” “shops and services,” etc.) associated with the place described by the place record; whether the category identifier is provided; zoning associated with the place described by the place record; whether there is a fuzzy match between information in the place record and text on a website associated with the place described by the place record (e.g., fuzzy match between place address, locality, or zip code); a score provided by the place record that represents the trustworthiness of the information included in the place record; a rank value provided by the place record for the place described; and a score provided by the place record representing the certainty that the place described exists. 
     During use, an embodiment may permit the addition of one or more new features not previously generated or considered by the embodiment when determining relevance of place records. The addition of one or more new features to an embodiment can assist the embodiment in more effectively determining the relevance of place records. For some embodiments, forward feature selection is utilized to determine the number of different features that should be generated for a place record to achieve desirable performance by the classifier. 
     Depending on the embodiment, one or more features generated for a place record may be those extracted or derived based on one or more place record attribute values determined to have high correlation with one another. Such feature correlations may be determined by offline analysis of sample place records (e.g., from a ground truth collection) that have been confirmed to be relevant or non-relevant. 
     Reference will now be made in detail to embodiments of the present disclosure, examples of which are illustrated in the appended drawings. The present disclosure may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. 
       FIG. 1  is a block diagram illustrating an example networked computing environment  100  that includes a place data system  104 , according to some embodiments. As shown, the place data system  104  is part of the networked computing environment  100  that includes one or more data sources  102  for place data, a client device  108 , and a communications network  106  communicatively coupled to the place data system  104 , the data sources  102 , and the client device  108  to facilitate communication therebetween. The communications network  106  may comprise one or more local or wide-area communications networks, such as an ad hoc network, an intranet, an extranet, the Internet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, or a Wi-Fi® network. Additionally, although only one client device  108  is illustrated, it will be appreciated that the networked computing environment  100  could include two or more additional client devices. 
     The data sources  102  provide the place data system  104  with place data (e.g., place records) for determining relevance of the place data for a particular use, such as use by a specific type of service. For some embodiments, the data sources  102  are implemented by one or more machines (e.g., networked machines), which may be similar to a machine  600  described herein with respect to  FIG. 6 . The data sources  102  can include one or more data sources maintained or operated by an entity that is a third party with respect to an entity operating the place data system  104 , or an entity intending to use relevant place data (e.g., relevant place records). Additionally, the data sources  102  can include one or more data sources that collect and store place data generated or maintained by crowd-sourcing, where a plurality of users (e.g., user base) can directly or indirectly input information to be included in the place data. Examples of data sources for crowd-sourced place data can include, without limitation, location search and location discovery services, such those provided by FOURSQUARE or social network services (e.g., FACEBOOK or TWITTER). One or more of the data sources  102  may comprise one or more datastores. As used herein, a datastore can include any organization of data stored on a data storage device, such as tables, comma-separated values (CSV) files, databases (e.g., SQL or NoSQL-based database systems), or other known organizational data formats. Datastores can include data structures that provide a particular way of storing and organizing data such that the data can be used efficiently within a given context. As noted herein, place data may be maintained as one or more place data records, where each place record can comprise data regarding a single place located on a geographic map. 
     The place data system  104  comprises a data ingestion system  120 , a matching system  122 , a relevance determination system  124 , an accuracy system  126 , a data store  128  for relevant place data, and a place data export system  130 . According to some embodiments, the place data system  104  ingests place data (e.g., in the form one or more place records) from the data sources  102 , determines relevance of the ingested place data, and provides (e.g., exports) relevant place data for use by one or more software applications that provide, support, or otherwise facilitate a service, such as a mapping service, a transport/transportation arrangement service, or a delivery service. For some embodiments, the place data system  104  is implemented by one or more machines (e.g., networked machines), which may be similar to the machine  600  described herein with respect to  FIG. 6 . 
     The data ingestion system  120  accesses place data (e.g., place records) from the data sources  102 , thereby permitting the place data system  104  to ingest place data from at least one of the data sources  102 . The data ingestion system  120  may include one or more data interfaces, such as a database interface, that facilitate the system  120 &#39;s access to data stored on at least one of the data sources  102 . 
     The matching system  122  receives a plurality of place records and identifies (e.g., matches) place records that refer to the same physical location on a geographic map. In this way, the matching system  122  can determine a set of matched placed records that refer to the same physical location on the map. As described herein, place records may be matched, for instance, based on one or more attribute values included in place records, such as place names, place addresses, place types, or place geographic coordinates. The plurality of place records received by the matching system  122  may originate from two or more different data sources in the data sources  102 . As noted herein, place records accessed by the place data system  104  (e.g., via the data ingestion system  120 ) can be sourced from multiple data sources (e.g., third-party providers) that are part of the data sources  102 . For some embodiments, the matching system  122  combines the information of a set of matched place records that refer to the same physical location on a geographic map and generates a single place record to describe the place corresponding to the physical location and originally described by the set of matched place records. Combining a set of matched place records to generate a single place record may comprise, for instance, selecting the best latitude and longitude coordinates for the place, best name for the place, and the best address for the place. 
     The relevance determination system  124  receives a place record and determines whether the place record is relevant or non-relevant for a specific use, such as use by a software application that provides, supports, or otherwise facilitates a service, such as a mapping service, a transport/transportation arrangement service, or a delivery service. According to various embodiments, a set of features is generated (e.g., derived or extracted) based on values of one or more attributes (e.g., record field or fields) included in the received place record. For some embodiments, the received place record is processed by a machine learning (ML) model, such as a classifier. The ML model can receive as input the generated set of features of the received place record and can output a prediction score that indicates the certainty or probability that the received place record is associated with, or belongs to, a particular class (e.g., class label). In this way, the set of features of the received place record can function as signals or suggestions for or against the class association. The certainty/probability of association between the received place record and the particular class assists some embodiments in determining whether the received place record is relevant or non-relevant. Where the particular class indicates that the received place record is relevant, a prediction score for the received place record may represent the received place record&#39;s relevance score. Additionally, where the particular class indicates that the received place record is relevant, the prediction score can also represent the received place record&#39;s trustworthiness, which can determine how much weight is given to the received place record&#39;s attribute values. 
     Where the ML model comprises a binary classifier, the binary classifier can associate the place record received by the place data system  104  to a positive or a negative class, where the positive class (e.g., positive class label) represents that the received place record is relevant, and where the negative class (e.g., negative class label) represents that the received place record is not relevant. The ML model may comprise two or more binary classifiers, and each binary classifier may be associated with its own positive and negative class labels. The binary classifier can further associate the received place record to an ambiguous class, which can indicate that the received place record can be moved into the positive class, moved into the negative class, ignored, or removed with some analysis (e.g., analysis by a human individual). At least one class comprises a plurality of sub-labels that can explain why the given place record is labeled a certain way. Example sub-labels for positive, negative, and ambiguous classes can include, without limitation, those listed in Tables 1-3. 
     The accuracy system  126  receives a place record and determines an accuracy of the received place record. For some embodiments, the accuracy system  126  determines the accuracy of the received place record based on a set of criteria. An example criterion can include, without limitation, accuracy of geographic coordinates (e.g., latitude and longitude coordinates) included in the received place record. Depending on the embodiment, the place data system  104  can use the accuracy system  126  to filter out place records that fail to satisfy a predetermined accuracy threshold. 
     The data store  128  for relevant place data receives a place record and stores the received place record for subsequent use, such as by a location-based service. For some embodiments, one or more place records received by the data store  128  are those determined to be relevant by the relevance determination system  124 . The place records determined to be relevant and stored on the data store  128  may be those already processed and determined by the accuracy system  126  to satisfy one or more accuracy criteria. In addition to storing a place record, the data store  128  can store a probability that the place record is relevant. The probability, which may be used as a relevance score, may be generated by the relevance determination system  124 . 
     The place data export system  130  accesses the data store  128  and provides (e.g., exports) one or more place records from the data store  128  to one or more client devices, such as the client device  108 . The place data export system  130  may provide a set of place records on demand by a client device or push the set of place records to a client device. For instance, the set of place records may be provided to the client device  108  in response to a search request submitted by the client device  108  (e.g., a search for a place to eat). For some embodiments, the one or more place records provided to a client device are relevant for use by a software application associated with a service, such as a mapping service, a transportation or transportation arrangement service, a delivery service, or a directory service. 
     During operation according to some embodiments, a set of place records flows through the place data system  104 , from the data ingestion system  120 , to the matching system  122 , to the relevance determination system  124 , to the accuracy system  126 , and to the data store  128 . In this way, the set of records can be matched and combined by the matching system  122  prior to being evaluated for relevance by the relevance determination system  124 . Alternatively, during operation according to some embodiments, a set of place records flows through the place data system  104  from the data ingestion system  120 , to the relevance determination system  124 , to the matching system  122 , to the accuracy system  126 , and to the data store  128 . In this way, the set of records can be evaluated for relevance by the relevance determination system  124  prior to the matching system  122 . 
     For some embodiments, the client device  108  comprises one or more machines (e.g., networked machines), which may be similar to the machine  600  described herein with respect to  FIG. 6 . For instance, the client device  108  may comprise a user device, such as a mobile phone, desktop computer, laptop, a portable digital assistant (PDA), smart phone, a tablet, an ultrabook, a netbook, a microprocessor-based or programmable consumer electronic device, a game console, a set-top box, or another communication device that a user may use to access the communications network  106 . In some embodiments, the client device  108  comprises a display interface (not shown) to display information (e.g., in the form of user interfaces). In further embodiments, the client device  108  comprises one or more touch screens, accelerometers, gyroscopes, cameras, microphones, global positioning system (GPS) devices, and the like. The client device  108  may be a device of a user that is used to access a location-based service, such as a mapping service, a transportation or transportation arrangement service, a delivery service, or a directory service. A user of the client device  108  may comprise a human individual or a machine. 
     The client device  108  may include one or more software applications such as, but not limited to, a web browser, a messaging application, an electronic mail (e-mail) application, and the like. As shown, the client device  108  comprises a transportation software application  140 , a delivery software application  142 , and other software application  144 . 
     The transportation software application  140  provides, supports, or otherwise facilitates a transportation or transportation arrangement service. For instance, in the context of a ride service, the transportation software application  140  may comprise a software application used by a ride requester (e.g., rider), a ride provider (e.g., a driver), or both (e.g., by a software application that has different modes) to facilitate a ride from a pick-up location to a destination. For example, the transportation software application  140  can use relevant place data (e.g., place records), provided by the place data system  104 , to enable a ride requester to set a pick-up location or a destination, described by the relevant place data, for a requested ride. 
     The delivery software application  142  provides, supports, or otherwise facilitates a delivery service, such as a service for delivering food or a package. For example, in the context of a food delivery service, the delivery software application  142  may comprise a software application used by a food requester (e.g., restaurant patron), a food provider (e.g., a restaurant customer), or both (e.g., software application has different modes) to facilitate food delivery. For example, the delivery software application  142  can use relevant place data (e.g., place records), provided by the place data system  104 , to enable a restaurant customer to search for a restaurant described by the relevant place data, and submit to that restaurant a request for food delivery to a destination described by the relevant place data. 
     The other software application  144  represents a software application that can provide, support, or otherwise facilitate another type of service for a user of the client device  108 . Another type of service may include a mapping service that provides the user with directions from their current location to a place located on a geographic map using relevant place data provided by the place data system  104 . Yet another type of service may include a directory service that provides the user with directory and location information for places on a geographic map using relevant place data provided by the place data system  104 . 
       FIG. 2  is a diagram illustrating an example relevance determination system  200  for determining relevance of place data, according to some embodiments. For some embodiments, the relevance determination system  124  described with respect to  FIG. 1  comprises the relevance determination system  200 . As shown, the relevance determination system  200  comprises an access module  202 , a feature generation module  204 , a machine learning (ML) module  206 , a relevance determination module  208 , and a relevant place data output module  210 . Though the relevance determination system  200  is described and depicted herein as including specific components and details, for some embodiments, the relevance determination system  200  is practiced according to different details, or with more, less, or different components than those shown. 
     The access module  202  accesses a particular place record for which relevance needs to be determined. In some instances, the particular place record accessed by the access module  202  may be one resulting from a process that matches different place records referring to the same place and combines them into the particular place records. The feature generation module  204  generates a set of features for the particular place record based on at least one value (e.g., extracted or derived) from an attribute included in the particular place record. The ML module  206  processes the set of features using a ML model, such as a classifier, to generate a probability that the particular place record is associated with a class label. The relevance determination module  208  determines, by the one or more hardware processors, whether the particular place record is relevant based on at least the probability that the particular place record is associated with a class label. 
     The relevant place data output module  210  can designate a particular place record as relevant in response to the relevance determination module  208  determining that the particular place record is relevant. Additionally, the relevant place data output module  210  can designate a particular place record as non-relevant in response to the relevance determination module  208  determining that the particular place record is not relevant. The relevant place data output module  210  can cause a particular place record, determined to be relevant by the relevance determination module  208 , to be stored on a data store for subsequent use, such as a software application associated with a service. Additionally, for a particular place record determined to be relevant, the relevant place data output module  210  can cause data storage of a probability (e.g., generated by the ML module  206 ) that the particular record is associated with a class label indicating that the particular place record is relevant. 
       FIGS. 3-5  are flowcharts illustrating example methods for determining relevance of a place record, according to some embodiments. It will be understood that example methods described herein may be performed by a device, such as a server executing instructions of a transportation or transportation arrangement system. Additionally, example methods described herein may be implemented in the form of executable instructions stored on a computer-readable medium or in the form of electronic circuitry. For instance, the operations of a method  300  of  FIG. 3  may be represented by executable instructions that, when executed by a processor of a computing device, cause the computing device to perform the method  300 . Depending on the embodiment, an operation of an example method described herein may be repeated in different ways or involve intervening operations not shown. Though the operations of example methods may be depicted and described in a certain order, the order in which the operations are performed may vary among embodiments, including performing certain operations in parallel. 
     Referring now to  FIG. 3 , the flowchart illustrates the example method  300  for determining relevance of place data, according to some embodiments. In particular, the method  300  may be used to determine the relevance of one or more place records provided by one or more data sources (e.g., the data sources  102  of  FIG. 1 ). For some embodiments, the method  300  is performed by the place data system  104  described above with respect to  FIG. 1 . An operation of the method  300  may be performed by one or more hardware processors (e.g., central processing unit or graphics processing unit) of a computing system. 
     The method  300  as illustrated begins with operation  302  (e.g., the access module  202 ) accessing a particular place record from at least one data source, where the particular place record describes a particular place on a geographic map. The at least one data source may include a place record that is generated or maintained by a plurality of human users. For instance, a place record on the at least one data source may be crow-sourced, whereby one or more fields of the place record may be populated or periodically updated by one or more users (e.g., by way of a location search or discovery service, such as one provided by FOURSQUARE). As noted herein, place data generated or maintained by users may have missing information (e.g., missing field values), include inaccurate information (e.g., outdated field values), or include fabricated information (e.g., fabricated field values). 
     The method  300  continues with operation  304  (e.g., the feature generation module  204 ) generating a set of features for the particular place record (accessed during operation  302 ) based on at least one value from an attribute included in the particular place record. For some embodiments, generating the set of features for the particular place record comprises extracting at least one value from an attribute (e.g., field) of the accessed particular place record. Additionally, for some embodiments, generating the set of features for the particular place record comprises deriving a feature value based on values from one or more attributes (e.g., fields) of the accessed particular place record. 
     The method  300  continues with operation  306  (e.g., the machine learning module  206 ) processing the set of features, generated by operation  304 , using a classifier to generate a probability that the particular place record is associated with a class label. The classifier may output a probability that the particular place record is associated with the class label. As noted herein, the class label can assist with determining the relevance of the particular place record. For example, the class label can represent that the particular place record is relevant to transportation or transportation arrangement services, such as a ride or ride-sharing service. In particular, the class label may represent that: the particular place record is relevant and describes an incorrect location for the particular place; the particular place record is relevant and describes that the particular place is closed; the particular place record is relevant and describes that the particular place is a private practice; the particular place record is not relevant to a transportation/transportation arrangement service; the particular place record is not relevant and describes that the particular place is a private location; the particular place record is not relevant and describes a temporary event; or the particular place record describes that the particular place does not exist. 
     For some embodiments, the classifier comprises one or more binary classifiers, where each classifier may be associated with its own positive and negative class label. The classifier may be trained on ground truth data comprising a set of place records (e.g., approximately three thousand place records) and a set of corresponding class labels curated by a human individual. 
     The method  300  continues with operation  308  (e.g., the relevance determination module  208 ) determining whether the particular place record is relevant based on at least the probability (generated by operation  306 ) that the particular place record is associated with a class label. 
     Referring now to  FIG. 4 , the flowchart illustrates the example method  400  for determining relevance of place data, according to some embodiments. Like the method  300 , the method  400  may be used to determine the relevance of one or more place records provided by one or more data sources (e.g., the data sources  102  of  FIG. 1 ). Additionally, the method  400  may be performed by the place data system  104  described above with respect to  FIG. 1 . An operation of the method  400  may be performed by one or more hardware processors of a computing system. The method  400  illustrates some embodiments that match and combine place records prior to the resulting place records being evaluated for relevance. 
     The method  400  as illustrated begins with operation  402  (e.g., the matching system  122 ) producing a set of matched place records. According to some embodiments, the set of matched place records comprises matching a first set of place records, from a first data source of place records (e.g., one of the data sources  102 ), with a second set of place records from a second data source of place records (e.g., another one of the data sources  102 ). The matching the first set of place records with the second set of place records may comprise matching a first place record, from the first set, to a second place record, from the second set, based on attribute values from the first place record and attribute values from the second place record. 
     The method  400  continues with operation  404  (e.g., the access module  202 ) accessing a particular place record from the set of matched place records produced by operation  402 . Subsequently, the method  400  continues with operations  406 - 410 , which, according to some embodiments, are respectively similar to operations  304 - 308  of the method  300  described above with respect to  FIG. 3 . 
     After operation  410 , the method  400  continues with operation  412  (e.g., the relevant place data output module  210 ) generating relevant place data in response to operation  410  determining that the particular place record is relevant. For some embodiments, the relevant place data includes the particular place record and an associated relevance score based on the probability generated at operation  408 . Depending on the embodiment, the relevant place data may be stored on a data store for subsequent use by a software application associated with a service, or may be processed by operation  414 . 
     After operation  412 , the method  400  continues with operation  414  (e.g., the accuracy system  126 ) processing the particular place record (e.g., as stored in the relevant place data) for accuracy, which may be determined based on a set of accuracy criteria. The set of accuracy criteria can include, without limitation, accuracy of geographic coordinates described by the particular place record. 
     Referring now to  FIG. 5 , the flowchart illustrates the example method  500  for determining relevance of place data, according to some embodiments. Like the method  300 , the method  500  may be used to determine the relevance of one or more place records provided by one or more data sources (e.g., the data sources  102  of  FIG. 1 ). Additionally, the method  500  may be performed by the place data system  104  described above with respect to  FIG. 1 . An operation of the method  500  may be performed by one or more hardware processors of a computing system. The method  500  illustrates some embodiments that evaluate place records for relevance prior to relevant place records being matched and combined. 
     The method  500  as illustrated begins with operation  502  with the x component generating a set of relevant place records for each different data source (e.g., each data source in the data sources  102 ). According to some embodiments, operation  502  generates a set of relevant place records for each different data source by operations  520 - 538 . In particular, operation  520  includes operations  530 - 538  performed for each place record in a set of place records for each different data source. For some embodiments, operations  530 - 536  are respectively similar to operation  302 - 308  of the method  300  described above with respect to  FIG. 3 . In response to determining that a place record is relevant by operation  536  based on at least the probability generated by operation  534 , operation  538  includes the place record in the set of relevant place records for the current different data sources. Once operations  530 - 538  have been performed on each place record in a set of place records, for each different data source, the set of relevant place records may be provided by operation  502 . 
     The method  500  continues with operation  504  with the x component producing a set of matched relevant place records by matching the sets of relevant place records resulting from operation  502 . The matching the sets of relevant place records may comprise matching and combining together (e.g., combining the information) place records, in the sets of relevant place records, that refer to the same place, thereby generating a single place record for each physical location. 
     The method  500  continues with operation  506  with the x component processing at least one place record, from the set of matched relevant place records produced by operation  504 , for accuracy, which may be determined based on a set of accuracy criteria. As noted herein, the set of accuracy criteria can include, without limitation, accuracy of geographic coordinates described by the at least one place record. 
       FIG. 6  is a block diagram illustrating components of the machine  600 , according to some embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically,  FIG. 6  shows a diagrammatic representation of the machine  600  in the example form of a computer system, within which instructions  610  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  600  to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions  610  may cause the machine  600  to execute the flow diagrams of other figures. Additionally, or alternatively, the instructions  610  may implement the servers associated with the services and components of other figures, and so forth. The instructions  610  transform the general, non-programmed machine  600  into a particular machine  600  programmed to carry out the described and illustrated functions in the manner described. 
     In alternative embodiments, the machine  600  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  600  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine  600  may comprise, but not be limited to, a switch, a controller, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  610 , sequentially or otherwise, that specify actions to be taken by the machine  600 . Further, while only a single machine  600  is illustrated, the term “machine” shall also be taken to include a collection of machines  600  that individually or jointly execute the instructions  610  to perform any one or more of the methodologies discussed herein. 
     The machine  600  may include processors  604 , memory/storage  606 , and I/O components  618 , which may be configured to communicate with each other such as via a bus  602 . In an embodiment, the processors  604  (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor  608  and a processor  612  that may execute the instructions  610 . The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although  FIG. 6  shows multiple processors  604 , the machine  600  may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof. 
     The memory/storage  606  may include a memory  614 , such as a main memory, or other memory storage, and a storage unit  616 , both accessible to the processors  604  such as via the bus  602 . The storage unit  616  and memory  614  store the instructions  610  embodying any one or more of the methodologies or functions described herein. The instructions  610  may also reside, completely or partially, within the memory  614 , within the storage unit  616 , within at least one of the processors  604  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  600 . Accordingly, the memory  614 , the storage unit  616 , and the memory of the processors  604  are examples of machine-readable media. 
     As used herein, “machine-readable medium” means a device able to store instructions and data temporarily or permanently and may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., Electrically Erasable Programmable Read-Only Memory (EEPROM)), and/or any suitable combination thereof. The term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store the instructions  610 . The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., instructions  610 ) for execution by a machine (e.g., machine  600 ), such that the instructions, when executed by one or more processors of the machine (e.g., processors  604 ), cause the machine to perform any one or more of the methodologies described herein. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” excludes signals per se. 
     The I/O components  618  may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  618  that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components  618  may include many other components that are not shown in  FIG. 6 . The I/O components  618  are grouped according to functionality merely for simplifying the following discussion, and the grouping is in no way limiting. In various embodiments, the I/O components  618  may include output components  626  and input components  628 . The output components  626  may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components  628  may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instruments), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. 
     In further embodiments, the I/O components  618  may include biometric components  630 , motion components  634 , environmental components  636 , or position components  638  among a wide array of other components. For example, the biometric components  630  may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components  634  may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components  636  may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detect concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components  638  may include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like. 
     Communication may be implemented using a wide variety of technologies. The I/O components  618  may include communication components  640  operable to couple the machine  600  to a network  632  or devices  620  via a coupling  624  and a coupling  622 , respectively. For example, the communication components  640  may include a network interface component or other suitable device to interface with the network  632 . In further examples, the communication components  640  may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices  620  may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a Universal Serial Bus (USB)). 
     Moreover, the communication components  640  may detect identifiers or include components operable to detect identifiers. For example, the communication components  640  may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components  640 , such as location via Internet Protocol (IP) geo-location, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth. 
     In various embodiments, one or more portions of the network  632  may be an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, a portion of the PSTN, a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network  632  or a portion of the network  632  may include a wireless or cellular network, and the coupling  624  may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling  624  may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third-Generation Partnership Project (3GPP) including 3G, fourth-generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long-Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology. 
     The instructions  610  may be transmitted or received over the network  632  using a transmission medium via a network interface device (e.g., a network interface component included in the communication components  640 ) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  610  may be transmitted or received using a transmission medium via the coupling  622  (e.g., a peer-to-peer coupling) to the devices  620 . The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions  610  for execution by the machine  600 , and includes digital or analog communications signals or other intangible media to facilitate communication of such software. 
     According to some embodiments, a method comprising: accessing a particular place record from a data source, the particular place record describing a particular place on a geographic map; generating a set of features for the particular place record based on a value from an attribute included in the particular place record; processing the set of features using a classifier to generate a probability that the particular place record is associated with a class label; and determining whether the particular place record is relevant based on at least the probability that the particular place record is associated with a class label. 
     The generating the set of features for the particular place record comprises extracting a value from an attribute of the particular place record. The classifier may comprise a binary classifier. The classifier may be trained on ground truth data comprising a set of place records and a set of corresponding class labels curated by a human individual. The data source may comprise a place record that is generated or maintained by a plurality of human users. 
     The method may further comprise, in response to determining that the particular place record is relevant, generating relevant place data that includes the particular place record and an associated relevance score, the associated relevance score being based on the probability. 
     The method may further comprise producing a set of matched place records by matching a first set of place records, from a first data source of place records, with at least a second set of place records from a second data source of place records, where the accessing the particular place record from the data source comprises accessing the particular place record from the set of matched place records. 
     The class label may represent that the particular place record is relevant to a ride-sharing service. The class label may represent that the particular place record is relevant and describes an incorrect location for the particular place. The class label may represent that the particular place record is relevant and describes that the particular place is closed. The class label may represent that the particular place record is relevant and describes that the particular place is a private practice. The class label may represent that the particular place record is not relevant to a ride-sharing service. The class label may represent that the particular place record is not relevant and describes that the particular place is a private location. The class label may represent that the particular place record is not relevant and describes a temporary event. The class label may represent that the particular place record describes that the particular place does not exist. 
     The method may further comprise in response to determining that the particular place record is relevant, processing the particular place record for accuracy, where the accuracy at least includes accuracy of geographic coordinates described by the particular place record. 
     The method may further comprise producing a set of relevant place records for each different data source in a plurality of data sources by performing the accessing of the particular place record, the generating of the set of features, the processing of the set of features, and the determining of whether the particular place record is relevant for each place record provided the different data source. The method may further comprise producing a set of matched relevant place records by matching together place records within the sets of relevant place records for the different data sources. The method may further comprise processing the set of relevant place records for accuracy, where the accuracy at least includes accuracy of geographic coordinates described by the particular place record. 
     Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein. 
     The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     One or more embodiments described herein can be implemented using modules, engines, or components, which may be programmatic in nature. As used herein, a module, engine, or component can comprise a unit of functionality that can be performed in accordance with one or more embodiments described herein. A module, engine, or component might be implemented utilizing any form of hardware, software, or a combination thereof. Accordingly, a module, engine, or component can include a program, a sub-routine, a portion of a software application, or a software component or a hardware component capable of performing one or more stated tasks or functions. For instance, one or more hardware processors, controllers, circuits (e.g., ASICs, PLAs, PALs, CPLDs, FPGAs), logical components, software routines or other mechanisms might be implemented to make up a module, engine, or component. In implementation, the various modules/engines/components described herein might be implemented as discrete elements or the functions and features described can be shared in part, or in total, among one or more elements. Accordingly, various features and functionality described herein may be implemented in any software application and can be implemented in one or more separate or shared modules/engines/components in various combinations and permutations. Even though various features or elements of functionality may be individually described or claimed as separate modules, for some embodiments, these features and functionality can be shared among one or more common software and hardware elements. The description provided herein shall not require or imply that separate hardware or software components are used to implement such features or functionality. 
     As used herein, the term “or” may be construed in either an inclusive or exclusive sense. The terms “a” or “an” should be read as meaning “at least one”, “one or more”, or the like. The presence of broadening words and phrases such as “one or more”, “at least”, “but not limited to”, or other like phrases in some instances shall not be read to mean that the narrower case is intended or required in instances where such broadening phrases may be absent. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, modules, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.