The Location Graph: Towards a Symbolic LocationArchitecture for the Web

Vlad StirbuNokia Research Center

vlad.stirbu@nokia.com

Petri SelonenNokia Research Center

petri.selonen@nokia.com

Arto PalinNokia Research Center

arto.palin@nokia.com

ABSTRACTIn this paper we outline a unified architecture for repre-senting locations of people, places and things in real orvirtual worlds, called realms, on the web. Our architec-ture is based on the location graph that encodes web-levelcontainment and connectedness relationships between loca-tions. The architecture provides an information processingmodel that allows realm independent queries such as posi-tion, range and path, and realm specific queries, such asdistance. We present existing systems that are enablers forthe proposed architecture. With this architecture we enablea common way to develop location based services and appli-cations across real or virtual realms, avoiding fragmentation.

Categories and Subject DescriptorsH.3.5 [Information Storage and Retrieval]: Online In-formation Services; H.3.4 [Information Storage and Re-trieval]: Systems and Software

General TermsDesign, Management, Experimentation

KeywordsSymbolic location, Location graph, Positioning, REST

1. INTRODUCTIONLocation information is essential for enabling location-

based services and applications. Basically these services andapplications aim at answering questions relevant for the userposition such as where is the closest restaurant, or where afriend or a family member might be. Having access to lo-cation information enables these services and applicationsto blend into the fabric of everyday life and changes ourperception of technology.

The current mainstream location-based services and appli-cations are heavily driven by mobile devices equipped with

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Global Positioning System (GPS) receivers while the loca-tion information processing model of these services assumesgeometric coordinates due to their reliance on the outdoormaps provided by web services such as Google Maps1. How-ever, as the location-based services become more sophisti-cated they move from the traditional outdoor base into newenvironments, such as urban areas or indoors. In these envi-ronments, the technical assumptions that have successfullydriven the services outdoor are no longer valid. For example,in densely built urban areas the GPS system has much loweraccuracy, while indoors it simply does not work. To makematters worse, most buildings are digitally “uncharted” andchances for getting cartography grade maps in the near fu-ture are low. These particularities induce a shift in the usagemetaphors for location-based services in which the need forgeometric accuracy decreases but the importance of symbolssuch as landmarks or user-defined points of interest, as wellas relative positions between these increases. A common lo-cation infrastructure that is able to accommodate all theseenvironments is necessary to avoid fragmentation.

In this paper we outline an architecture that enables prac-tical use in location aware applications and services of sym-bols that represent in a web friendly way concepts like peo-ple, places and things. The architecture is based on a loca-tion graph that has as nodes these concepts while the edgescorrespond to containment and connectedness relationshipsbetween them. This data structure enables an informationprocessing model that provides basic location queries suchas position, range, path, and, in certain conditions, distance.Applications and services can use this processing model ina uniform way, across real and virtual realms, to access thesymbolic location information of people, places and things.

The paper is structured as follows. We start by providingmotivation for this work in Section 2. The architecture andthe information processing model is described in Section 3.Key enablers are presented in Section 4. Application sce-narios enabled by the proposed architecture are describedin Section 5. Concluding remarks are provided in Section 6.

2. MOTIVATIONTechnology plays an ever increasing role in our lives affect-

ing the ways we perceive it. At one end, ubiquitous com-puting [30] pursues a vision in which technology disappearsinto the fabric of everyday world until it becomes indistin-guishable. At the other extreme, virtual reality’s vision isto create worlds inside computers with a practical purpose,

1http://maps.google.com

VocabulariesNa

mes

Centraly controlled User definedTh

ings

Plac

es

Finland (ISO)

90210 (US Postal Service)

London (UK)

Bob's bedroomat home

sandpit

city square

printer H1B2B1 (Nokia IT)

device 01:1D:00:4E:A3:56 (IEEE)Felix the Cat

Alice

"Telly"Wolverine (Marvel)

Route 66Joe's Fish

Figure 1: Examples of names relevant as locations

such as exploring inaccessible or distant places, or for en-tertainment, such as in first person shooter or fantasy com-puter games. In the middle, mixed reality aims at mergingreal and virtual worlds to produce new environments and vi-sualizations where physical and digital objects co-exist andinteract in real time, linked to each other [19]. Dependingon how we mix the real and the virtual, we can refine furtherthe augmented reality, in which the digital objects are visu-alized in the real world, or augmented virtuality, in whichreal objects are visualized in the virtual world.

Regardless of which“flavor”of reality we refer to, the mainconcepts are the same: people, places and things. Peopleare the human users that use the things, and places arecontainers for these people and things. The emergence ofInternet of Things affects the traditional understanding aspeople or things begin to be service providers. They startto be containers for these services themselves. In this newcontext, the people and the things are turning into location-wise “places”.

In Figure 1 we provide some examples of names that canbe used as locations. On one dimension we differentiate be-tween places, which tend to be geographically identifiable,and things, which tend to have a more subjective “objectscale geo-location”. The other dimension we differentiate onhow widely known the interpretation of the names is, and ifthey are regulated by some authority. Generally, the namesthat are regulated are easily interpreted by a wider audi-ence as the rules that govern those vocabularies are properlyformalized and managed centrally by the respective author-ity. For example, countries or city names are centrally man-aged by international or national organizations that enablesthem to be easily identifiable and mapped to geographic lo-cations. Similarly, each country has its own zip codes thatare assigned by the local postal service. Devices that useethernet-like interfaces have manufacturer specific names forthese interfaces assigned by IEEE. On the other hand, theinterpretation of names belonging to user defined vocabu-laries tends to be more limited to the user or a small groupof users. They can easily identify themselves by their firstnames, know which pets or things belong to whom, or whichplace is “at home”, for example.

Location based services are the main vehicle to deliver thisvision of interweaving the location with technology. How-

ever, the current mainstream services and applications aredesigned mainly for outdoor environments. A prime exam-ple of such service in consumer domain is navigation, inwhich the user is presented with the route overlaid on a mapor a list containing junctions and turn instructions. Thismetaphor is familiar to users because it recreates the experi-ence when the same service was provided without technologyusing“the pen and the paper”. Although these services workinternally using geographic coordinates, the users are pre-sented with familiar symbols such as place and road names.However, as we move away into new environments, the mereuse of geographic coordinates augmented with orientationinformation is not enough.

3. SYMBOLIC LOCATION ON THE WEBIn this section we provide the overview of the architecture,

we describe how the information is processed, and how weleverage the capabilities of existing systems to populate thegraph with location information.

3.1 ArchitectureThe Web is a good platform to create web-presence for

people, places and things [14]. The Locative Web [32] goesfurther and formalizes a way to represent location as a web-level concept. Our system architecture leverages this foun-dation that follows the same principles as the Web [13].However, instead of representing the resources we focus oncapturing the real world relationships between them. Theresult is an information space in which the locations areitems of interest referred to as resources. They are uniquelyidentified using Uniform Resource Identifiers (URI), useragents can interact with them using HTTP protocol, whichallows them to exchange well-known representations thatconvey essential characteristic of the resource. Each re-source representation embeds the containment relationshipsand the connectedness relationships with other places. Thefollowing is an example of an XHTML representation of sucha resource, in which the relationships are described usingRDFa [23]:

<html xmlns:lg="http://example.com/locationgraph/ns#"><head><title>A place</title><linkrel="lg:contains"href="http://example.com/aplace" />

<linkrel="lg:contained"href="http://example.com/anotherplace" />

<linkrel="lg:connected"href="http://example.com/yetanotherplace" />

...</head>...</html>

In the resulting graph the symbols that identify locationnames are the nodes while the edges correspond to theirrelationships. Figure 2 provides an example of a locationgraph which has place names in both controlled and userdefined vocabularies. The names describe places in the realor fictional worlds, called in our context realms. It is possiblethat some of the realms have already more advanced locationsystems, in which case the representations of places shouldcontain these coordinates.

Azeroth

Bob's places

at home

at work

Finland

Tampere

Hermia

Goldshire

GeoTree+

Outdoor GIS

Everywhere

campingtent

Alice's places

kitchen

living telly

01:1D:00:4E:A3:56

my desk

Warcraft Realm

World Realm

Nokia Corp Realm

Bob's Home Realm

Nokia

Hermia 5

C 518

GeoTree+

Indoor GIS

NavTag

Locationmodel

Containsrelationship

Connectedrelationship

Sweden

342

Figure 2: A sample fragment of the location graph

3.2 Location ModelThe location graph encodes containment and connected-

ness information about places, things and people. The lo-cation graph is an extensible generalization of the NavTagsymbolic location model [27] (described in more details inSection 4.2.3). The subgraph containing only the edges rep-resenting containment forms a lattice in which the every-where symbol is the supremum while the nowhere is theinfimum. A user agent can follow recursively the contain-ment relationships to perform position queries, while follow-ing these relationships in reverse order enables range queries.The subgraph containing only the edges encoding connect-edness information enables user agents to identify the pathsbetween places.

The location graph model enables three global scopedqueries that yield symbolic results: position, range and path.Additionally, the location model of specific realms may havericher capabilities than the global baseline. For example, inthe real world realm there are GIS systems that can pro-vide geographic capabilities. Indoors, GIS-like systems pro-vide the geographic capabilities for building level realms.These extra capabilities enables the system to expose thisgeographic information in realm level query responses, whichcan be aggregated in the global results.

The baseline information encoded in the location graph(e.g. containment and connectedness) does not support thenotion of distance. However, if the geometric coordinatesinformation is available, the system is able to support realmlevel distance queries. The notion of distance is realm spe-cific and determined by the choice of location model in thespecific realm. Therefore, the response of a distance querymay be geometric (e.g. using the metric system), or sym-

bolic (e.g. in Azeroth2 two places may be“two walking days”apart).

Relationships between resources are embedded in theirrepresentations allowing user agents to crawl the locationgraph. Using this technique, a search engine can index thecontainment and connectedness information and provide aninterface for searching the location graph.

3.3 Modeling InfrastructurePopulating the location graph is a monumental task that

can not be undertaken by one party alone. Therefore, the ex-isting infrastructure must be reused whenever possible andthe information provided by these systems must be embed-ded in the resources’ representations. At least the followingproviders have information that should be linked in the lo-cation graph:

• Location infrastructure provides information used todescribe places. Some names are controlled by author-ities which provide a level of assurance about the infor-mation associated with this names. Other names areuser defined and the associated information consist ofself-assertions.

• Positioning systems and technologies provide valuableinformation that is used to map stationary and mobilethings to locations.

• Social networking sites provide the infrastructure thatenables users to name places as they like (e.g. Face-book Places) and share these with their friends. The

2Azeroth is a fictional kingdom in which the action of thecomputer game Warcraft takes place

location graph provides linking points with the socialgraph, enabling social search.

Some of the information in the location graph can beadded manually for those locations who’s characteristics donot change over time. However, most locations have a dy-namic nature and being able to change the structure of thegraph (e.g. adding or removing nodes and edges) is essen-tial. A programming environment that allows this processto be automated using software components is mandatory.

4. ENABLERS AND RELATED WORKThis section describes the architectures and technologies

that are relevant for enabling the concept of symbolic loca-tion for the web and the realization of the location graph.

4.1 REST Architectural Style and the WebThe REST architectural style enables the use of the Web

as an application platform. Using this style, services ex-pose their functionality as resources, each identified using aURIs, can be accessed via a fixes set of verbs (e.g. the GET,POST, PUT and DELETE methods of HTTP protocol), and ex-change well-known representations that convey the essentialcharacteristics of the resource. Additionally, the hypermediaconstraint defines the ability to encode in representations re-lationships with other resources using references. By havingonly these constrains on what a resources can be, the Webhas become the most successful global information system.Currently, the Web contains not only information resources(e.g. documents, media content, or synthetic artifacts), butalso people (e.g. using OpenID capable resources [22]) orthings (e.g. the web of things [26, 10]).

4.2 Location information systemsLocation based services and applications interact with the

location system to request positions of objects, find the near-est neighbor of an object, find a path between two objectsor find all the objects within a certain boundary [5]. A lo-cation model, supporting these interactions, has to provideobject positioning, distance functions, topological relations(e.g. spatial containment and connectedness), or orienta-tion. Geometric location models describe locations using ge-ometric figures, which can be described using coordinates ina local cartesian reference system, or in a global system (e.g.World Geodetic System 84 [20]). Geometric systems havebuilt-in ability to describe containment but explicit model-ing is required to capture connectedness. Symbolic locationmodels define positions in space using abstract symbols (e.g.names or labels). Explicit modeling is needed to describecontainment and connectedness relations between objects.Set-based and hierarchical models are able to model wellcontainment while graph based models are better at model-ing connectedness. Hybrid models overcome the limitationsof geographic and symbolic models by combining featuresfrom both.

We provide an overview of the geometric and symbolicfeatures provided by specific location information systems.They serve as information providers for annotating the rep-resentations of locations with realm specific information thataugments the baseline required in the location graph.

4.2.1 Geographic Information SystemsGeographic Information Systems are systems specialized

in capturing, storing, analyzing and presenting data linkedwith locations. They provide very sophisticated geomet-ric spatial data models optimized for application domainssuch as cartography, remote sensing, land surveying, urbanplanning, navigation, etc. GIS systems are repositories ofvaluable data about the physical environment but typicallythis information is not exposed in a Web friendly way al-though most of these systems have HTTP interfaces definedby OpenGIS Consortium [21]. A typical GIS system pro-vides at least latitude, longitude and altitude data abouta place. Although GIS systems are associated with appli-cation scenarios related to Earth surface, they have beenused successfully as location infrastructure for indoor smartspaces [8].

4.2.2 Hierarchical Symbolic Location SystemsThe most widely used format to identify locations in the

real world is the postal address. Following roughly thescheme: recipient name, street, zip code, city, country; postaladdresses can be easily mapped to geographic coordinatesusing GIS systems. An alternative way to find informationabout real world places is from various organizations suchas Getty Thesaurus of Geographic Names3 or GeoNames4.They provide structured information using hierarchical datastructures called geotrees. A typical entry is augmented withgeographic coordinates (e.g. WGS84), which enables easymapping to a GIS system, and a description of the placeaccording the service provider taxonomy.

Similar hierarchies have been used by different organiza-tions to identify places indoors [29]. The schemes used bythese systems tend to have a local scope.

4.2.3 NavTag Symbolic Location SystemOur previous work with NavTag resembles at micro scale

the symbolic location architecture for the Web. NavTag isa symbolic location information system that has the capa-bilities specific to both symbolic and geometric coordinatesystems. The underlaying location model is able to pro-vide four basic queries: position, range, path and distance.These operations provide enough flexibility for clients to con-struct more advanced queries (e.g. closest neighbor query)by pipelining the basic queries.

The main concept of the system is the tag, which corre-sponds to a point of interest identified by a label. Each tagcan have two relationships with other tags: contained thatencodes spatial containment, and connected that encodesconnectedness with other tags. The NavTag system has theability to capture spatial positioning by decomposing thephysical space using a quad-tree data structure. Quadrantsthemselves are points of interest, managed by the system.Other tags can establish contained relationships with quad-rants to be spatially positioned in the physical space.

The information encoded in the contained relationship al-lows the system to construct a lattice that encodes the spa-tial containment and, optionally, the positioning. The lat-tice enables the position query, by following recursively theconnected relationships, and the range query, by following

3http://www.getty.edu/research/conducting research/vocabularies/tgn4http://www.geonames.org

System Perspective

4433

Perspective-1

tag-1

tag-2

Perspective-2

tag-3Contained

2

3 4System Perspective

Path: tag-1 > tag-3

tag-1 tag-3

tag-2Connected

Perspective-1

Figure 3: NavTag location model: the lattice com-ponent (top) and graph component (bottom)

them recursively in reverse order. The information encodedin the connected relationship allows the system to constructa directed graph that captures how the tags are connected.The graph and the lattice enables path and distance queries.

Often, the perception of a point of interest depends onthe observer’s sociocultural background. Different users orgroups of users tend to group relevant points of interest intocollections, that reflect their view of the space. In NavTag,the collections are represented as perspectives. Dependingon what constraints apply to the names of the places oninterest, the system has two perspective categories: user forpoints of interest created by the end-users, and controlledperspectives for points of interest that have strict labelingrules enforced by some authority.

4.3 Location Sensing TechnologiesHighly accurate outdoor location sensing is driven by the

ubiquity of the GPS system and mobile devices equippedwith GPS receivers. However, devices without such capabil-ities can still find their ways, although with lesser accuracy,by listening to radio beacons from GSM (Global System forMobile Communications) cell towers or Wi-Fi hotspots [11].

Indoor positioning technologies offer the ability to locateand track objects in buildings. Objects have been typicallypeople carrying dedicated devices [4], but lately also loca-tion of any kind of objects, stationary or moving, has raiseinterest. This has emphasized new kind of requirements forpositioning technology such as the importance of relative po-sition compared to certain place or object instead of absoluteposition of the object inside the building.

Typically indoor position technologies are build with ra-dio technology but systems that use optical and ultrasoundtechniques, or combinations of these also exists [12, 25]. Thebenefit of using radio technologies comes from the ability to

Smartphone

0

180

Tag#1

Tag#2

d1

d2

Figure 4: Local Sensor

exploit inbuilt radio without of need to build and deploynew dedicated infrastructures for location purposes. Radiotechniques utilize mainly the received signal strength, thetime of arrival and triangular measurements [17].

Today, the most common indoor positioning technologiesrely on the already deployed infrastructure of WiFi beacons[9]. Alternative positioning technologies use ultra wide band(UWB) [35], low power radios such as Bluetooth [16], or evensignals emitted by electrical wires [28].

An interesting example of a system that uses low powertechnology for positioning and does not require dedicatedinfrastructure is Local Sensor [33]. Objects of interest aretagged with a radio beacon that enables devices to read andlocate these tags. The unique identity of tag allows a de-vice with local sensor antenna to find relative position anddistance of the objects in proximity, see Figure 4. If theposition of an object is known from another source (e.g. theobject exist in the location graph), that object becomes ananchor that allows accurate positioning for all local sensorenabled objects.

The ability to automatically map devices to symbols us-ing direct device-to-device positioning capabilities is a keyenabler for our symbolic location architecture.

4.4 Social NetworkingSocial networking has become an important part of the

Web. Users can rely on specialized sites (e.g. Facebook5 orLinkedin6) that aggregate information about their friendsor they can expose their friends list in their blogs usingXHTML Friends Network (XFN) [7] or Friend-of-a-Friend(FOAF) [6]. Regardless of the centralized or decentralizednature of the social network, the logical step is to move to-wards the physical world and expose also information aboutthe users’ locations.

The location data defined in OpenSocial Social Data [2]may contain coordinates in both geographical (e.g. latitudeand longitude) and symbolic (e.g. country, region, locality,zip code or street name) systems. Although this informationbelongs to two different coordinate systems there is an easyway to map from one to another. The location informationcan be tagged or labeled (e.g. work or home) to differentiatebetween locations but the semantic is limited to business re-lated scenarios (e.g. mailing or billing). It is important tomention here that address data structure that encodes loca-tion information is not considered primary social data, but

5http://www.facebook.com6http://www.linkedin.com

belongs to the additional social data group. Similarly, theinformation defined by Open Graph protocol [1] mentionslocation in terms of latitude and longitude, or full address.

5. APPLICATION SCENARIOSIn this section we provide examples of application scenar-

ios where the symbolic location infrastructure for the webcan be leveraged to provide a superior user experience.

5.1 Pedestrian Navigation in Urban AreasCar navigation is a prime example of an established con-

sumer oriented location based service. A new breed of de-vices equipped with GPS capabilities drive this scenario intonew territories, such as pedestrian navigation especially indensely build areas. Most likely such a geographic are is cov-ered by a GIS system outdoor and several GIS-like or sym-bolic location systems for indoors. Using traditional navi-gation solutions for a route that contains several indoor andoutdoor segments, the user would have to switch betweenseveral applications as it moves from from one place to an-other. A navigation application able to use the informationprovided by the location graph would be able to present ina seamless manner the different segments of the route to theuser.

5.2 Mixed RealityAugmented Reality refers to a view of a physical real-

world environment whose elements are augmented by vir-tual, computer-generated imagery. The augmentation is inreal-time and in semantic context with environmental ele-ments, making the information about the surrounding realworld of the user interactive and digitally usable [3]. Aug-mented Virtuality, on the other hand, refers to predomi-nantly virtual spaces, where physical elements like thingsor people, are dynamically integrated into, and can interactwith the virtual world in real-time.

Together, Augmented Reality and Augmented Virtualityare referred to as Mixed Reality. With smart phones grow-ing both in their processing power and sensory capabilities,mobile Mixed Reality applications have grown from virtu-ally non-existent to an important emerging trend in mobilespace. The various device build-in sensors such as GPS,accelerometer, magnetometer as well as audio and imagerecognition provide a solid technical infrastructure for mak-ing new mixed reality applications and services, turning thedevice into a “magic lens” which lets users look through themobile display at a world that has been supplemented withinformation about the objects that it sees.

Until recently, most mobile Mixed Reality applicationshave typically been running on their own with minimal orno linkage to centralized systems [18] with their content lo-cally stored on the mobile device. As the number of genericand commercially available Mixed Reality solutions grows,they are linking to solution specific backends. Mobile MixedReality applications such as Layar [15] and Wikitude [31]overlay location-based spatial data on top of real-time me-dia sceneries such as camera live-feed based. However, thisdata is fetched from proprietary closed Web service back-ends, prohibiliting content sharing and mashing up MixedReality solutions. The Mixed Reality Web service platformbuilt at Nokia Research Center (e.g. [24][34]) has been oneattempt at building better support for interoperability andopen linked data for Mixed Reality domain. The recent

W3C Workhop on Augmented Reality on the Web7 identi-fied several issues related to how Mixed Reality can benefitfrom Web technologies, including the need to establish stan-dards for Mixed Reality Web service and device side APIsand data formats.

Mixed Reality emphasizes user experience and interactionin a particular context where the physical environment is anessential element of the experience, aligning it by naturewith Internet of Things and ubiquotuous computing wheresmart devices become sources of services. User’s locationis often the most important contextual component definingthe interaction with an overwhelming majority of Mixed Re-ality clients augmenting the world with visual information.Mixed Reality services can be seen as a special category ofLocation Based Web services that naturally bridge physicalworld with Web services and applications.

Current Mixed Reality solutions are presenting content tothe user based on a provided location, be it an AugmentedVirtuality map view with POI information and user’s pho-tos, or an Augmented Reality view through camera lens withthe same content tied to a particular coordinate levitatingon screen with possibly addition orientation information forin situ positioning. These solutions rely on geo-coordinates(longitude, latitude, altitude) and sometimes orientation in3D space (pitch, yaw, roll). The location graph based ar-chitecture presented in this paper provides a natural wayof expanding the Mixed Reality use cases beyond the basiccoordinate system and into arbitrary realms. This makes itpossible to associate information related to things, peopleand places, essentially moving to object-scale geo-location.

In a pervasive environment with ability to use sensorydata to detect a symbolic location, Mixed Reality clientscan provide the user a way of exploring and interacting withthe services and content offered by local devices. The abilil-ity to link resources can be exploited further and used forlinking the symbolic location to arbitrary services and con-tent, allowing remote browsing of content and services basedon a symbolic location.

6. CONCLUSIONS AND FUTURE WORKIn this paper we presented an architecture that captures

at web level the locations of people, places and things andthe containment and connectedness relationships betweenthem, in real or virtual worlds (e.g. realms). The result-ing location graph enables an information processing modelthat allows three global operations (e.g. position, range andpath queries) and one realm specific operation (e.g. dis-tance). The location architecture enables a common way todevelop location based services, and avoids fragmentationacross different real and virtual realms.

The location graph vision is based on our limited expe-rience using symbolic location systems (e.g. NavTag) anddeveloping mixed reality services and applications. We havenot considered the practical issues of implementing the loca-tion graph at web-scale, the operational issues of intercon-necting the different parts of the graph using informationfrom realm specific enabling systems, or the privacy and se-curity implications of using the graph. We plan to explorethese challenges in our future work.

7http://www.w3.org/2010/06/w3car/report.html

7. ACKNOWLEDGMENTSWe gratefully acknowledge Nokia Foundation for support-

ing this work.

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