=Paper=
{{Paper
|id=None
|storemode=property
|title=Discovering Places of Interest through Direct and Indirect Associations in Heterogeneous
Sources - The TravelSampo System
|pdfUrl=https://ceur-ws.org/Vol-798/paper5.pdf
|volume=Vol-798
}}
==Discovering Places of Interest through Direct and Indirect Associations in Heterogeneous
Sources - The TravelSampo System==
https://ceur-ws.org/Vol-798/paper5.pdf
Discovering Places of Interest through Direct
and Indirect Associations in Heterogeneous
Sources — The TravelSampo System
Eetu Mäkelä, Aleksi Lindblad, Jari Väätäinen, Rami Alatalo, Osma Suominen,
and Eero Hyvönen
Semantic Computing Research Group (SeCo),
Aalto University and University of Helsinki, Finland
first.last@aalto.fi, http://www.seco.tkk.fi/
Abstract. Linked data related to places offers a superior collection
of information to base search and recommendation functionality on in
eTourism visit planning as well as location-aware mobile applications.
Besides places interesting in themselves, through linked data it is possi-
ble to discover places interesting only through association, such as being
the venue for a concert by an artist with an interesting genre. How-
ever, in order to harness this collective data source, challenges relating
to data heterogeneity, quality, scale, and indexing and querying com-
plexity must be resolved. In this paper, the TravelSampo visit planning
and mobile application is presented, which tackles these issues. Using the
system, queries describing both simple and complex interests can be run
over some 17 million places of interest from over 20 vastly heterogeneous
sources.
1 Introduction
Location-aware mobile devices are becoming increasingly commonplace. This
has lead to a multitude of mobile applications to search for e.g. events, places of
interest or services near the user’s physical location. On the other hand, many
eTourism web applications also now allow people to design travel plans online,
picking sites to visit and exporting visit lists to their phone’s navigator software.
The TravelSampo project is an attempt to harness linked data as a source
of material for an application to help travellers find content relevant to them,
both in planning as well as during a trip. As compared to existing non-linked
data solutions as well as similar linked data systems such as DPBedia Mobile [2],
mSpace Mobile [13] and SmartMuseum [11], it tries to improve upon the state of
the art in being able to integrate both massively more heterogeneous material,
as well as to provide more intelligent services on top of it.
Particularly, the TravelSampo system takes into account that there are mul-
tiple ways in which a location may be of interest to a user. First, the place itself
may have some quality of interest, such as being a church, or being a church
in the gothic style. On the other hand, the place may also be of interest only
through a more or less direct association, such as being the venue for an inter-
esting event or having been the birthplace of a painter with a style of interest.
In addition, a place may be of interest by virtue of the services offered there,
such as Internet access.
This variety of ways in which data can be both interesting as well as en-
coded necessitates a flexible architecture for querying locations of interest. The
strength of this is that the application should ultimately be able to cater to a
wide variety of interests, from people looking for nearby museums through music
fans interested in concerts by Norwegian heavy metal bands to freegans search-
ing for dumpsters near big supermarkets without nearby surveillance cameras.
At the same time, this scale of heterogenuity causes severe problems in both
integrating the content as well as providing efficient and intelligent search and
recommendation services and user interfaces on top of it.
In the current demonstration system of TravelSampo, some 17 million lo-
cations have been loaded into the system, integrating information from over
20 vastly different datasets of places, places of interest, and content making
places interesting through association, such as fiction taking place in real-world
locations, or the birthplaces of famous artists. Included are for example the
huge datasets of DBPedia [3] and the LinkedGeoData.org [1] version of Open-
StreetMap, but also fast-changing, dynamically converted datasets such as four
different sources for current events and exhibitions in Finland.
In this paper, the TravelSampo application is presented first through its user
interface. After that, the challenges faced and solutions developed in integrating,
mapping and making usable the disparate heterogeneous data sources are de-
scribed. Finally described are the indexing and querying interfaces created that
make possible the complex queries required to provide the advanced functionality
of the TravelSampo application.
2 The TravelSampo Application
The TravelSampo application has two distinct interfaces. The web interface is
used to plan the trip beforehand and to examine and share the trip afterwards.
The mobile interface is used during the trip to find the destinations and to get
more information about them.
2.1 The Visit Planning Interface
A typical user would be someone who is going on a trip to a new city. Before
the trip he can use the planning interface to find out what kind of cultural
destinations and events the city offers during his trip. The destinations can be
searched with different levels of complexity. In the simplest case our user is
interested in churches, which are places themselves. Our user is also interested
in wall climbing, which is a service located in a place. And finally he’s interested
in modern art, which is a topic of an exhibition held in a place. The application
can handle all these searches.
The user has found a couple of churches, a sport center with wall climbing
and an exhibition of modern art and now he can save them to his destination
shelf which can be accessed during the trip in the mobile interface. The filters
used to find these destinations can also be saved to be used on other trips either
in the planning interface or the mobile interface. The visit planning interface is
not yet implemented but the shelves and filters can be produced and used in the
mobile interface.
2.2 The Mobile Interface
During his trip the user can use the mobile application to find the previously
saved destinations. The starting page of the mobile interface, depicted in figure
1 has a list of nearby destinations (1.A) with their types, distance to them and
interesting instances associated to them which can be filtered using the filter
menu (1.B). This menu contains the users destination shelves that are relevant
in his current location as well as his personal filters and some predefined ones.
Fig. 1. Main screen of the mobile interface
The user can now use the shelf containing his destinations to access their
pages and get a route map to the destination as well as information about it,
links to associated instances and a button to mark the destination for future
reference. As the user is in the destination page in the context of the destination
shelf there are also buttons to browse through other destinations of the shelf.
If the user finds he has more time than he expected he can go back to the
main page and use his saved filters to find for example more churches or in-
stances related to modern art. This can also be used to quickly find interesting
destinations or useful services on the vicinity without prior planning.
There will also be a possibility to use free text search to find destinations
(1.C). The location area (1.D) shows the user’s current address and allows him to
reload it using the geolocation capabilities of the mobile device or set it manually.
On the top right corner there is a link to the user’s profile page or a login page
if the user hasn’t already logged in (1.E).
3 Data Sources and Modeling
As already stated, the TravelSampo data repository contains some 17 million
locations sourced from over 20 vastly different datasets. These sources are de-
scribed in table 1, stating the general type of data sourced from each provider,
the number of location and reference items in the data sources, as well as some
example content types in the data thus gained. As can be seen from the table,
the TravelSampo system contains a truly heterogeneous mix of data of different
types, sources and schemas.
Particularly interesting in analyzing the data sources integrated is the fact
that the boundaries between geographic place names, places of interest and ser-
vices are not crisp. For example, the general place name registries contain not
only hills and swamps, but also areas of sporting services, churches and aban-
doned police posts, while on the other hand the locations in the Espoo nature
site point of interest database are precisely hills and swamps of special inter-
est. Further, the Helsinki City service database for example contains both office
entities such as childcare services as well as located services such as swimming
pools, which also feature in point of interest databases.
Based on this observation, the TravelSampo system was designed not to dis-
cern between these sources for locations at all, but to treat all location informa-
tion as equal. This puts the burden of discovering whether a location is a point
of interest to someone on the information gathered for that location. Among
direct indicators of interest, primary among them is the type of the location.
To be able to use this across all the place data in the TravelSampo system, it
was decided to attempt to build a single unified place and place of interest type
ontology POIO from all the type ontologies used in the various data sources.
This was done semiautomatically so that first all place type labels were com-
pared automatically, already yielding several hundred equivalency mappings.
Then, these mappings were examined by hand, and a large number of spurious
mappings rejected, while an equally large amount of new mappings and subclass
relations were curated, until all place types could be found under a single root.
Table 2 relates the numbers of distinct place types in the constituents of the
POIO ontology, as well as the size of the final ontology. In total, of the 2499
concepts in the final ontology, 62% (1539) were found to be shared between at
least two source ontologies.
Besides differing in place types, the datasets also differed vastly in terms of
modeling and level of content description. For example, the RKY database of
Table 1. TravelSampo Data Sources
Type Source Size Example types Description
LinkedGeoData ∼3.1 million Statue, tunnel, crossing, school, ruins, Entities with non-amenity types in the RDF conversion [1]
bench of the open mapping project OpenStreetMap
Places of DBPedia ∼432,000 Site of earthquake, tv-mast, bridge RDF conversion [3] of structured information in Wikipedia
Interest MJREKI 25,343 Hill fort, rock tomb, place of village Finnish ancient monuments
RKY 1,850 Manor, powerplant, cemetery, rectory Finnish nationally cultural-historically significant milieu
Espoon rakennukset 80 Villa, rectory, school, jugend building Cultural-historically significant buildings in the city of Espoo
Espoon luontokohteet 282 Swamp, glacial erratic, meadow, nature Nature sites in the city of Espoo
reserve
LinkedGeoData ∼3.7 million Pub, pharmacy, restaurant Entities tagged as amenities in the RDF conversion [1] of the
Services
global open mapping project OpenStreetMap
Pääkaupunkiseudun 2,929←4350 Swimming pool, daycare, museum, Public services provided by the cities in the Helsinki
palvelukartta child protection services metropolitan area
EvenemaX ∼1480←4300 Pub quiz, concert, exhibit Finnish commercial cultural event aggregator
Turku 2011 ∼305←3400 Concert, exhibition, circus Events included in Turku’s year as European culture capital
Events
Museot.fi ∼80←140 Exhibition Current exhibitions at Finnish museums
Agricola ∼60←70 Lecture, seminar, exhibition Event-calendar of the Agricola network of Finnish historians
Other Tarinoiden Helsinki 1629←4091 Book, music, film, fact Fiction and facts relating to places in Helsinki
Place- CultureSampo ∼20,000← Poem, photograph, painting, video, An integrated portal of some 30 different content types from
Related 600,000 skill some 20 different institutions [6]
Content DBPedia ∼432,000← Person, organization, invention, event Every conceivable notable aspect of human existance that
3.6 million linked to a place
SUO ∼800,000 Hill, swamp, meadow, glacial erratic, Finnish registry of place names
area of sporting services
Places SAPO 1,261 Administrative area A spatio-temporal ontology of historical Finnish counties [7]
GNS ∼4.2 million Oil field, ramp, hill, abandoned police Geonet Names Server, a US government place database
post, church, glacier
GeoNames ∼6.9 million Oil field, ramp, hill, abandoned police Open database of geographical names, using the same feature
post, church, glacier codes as GNS
TGN ∼895,000 Aqueduct, mausoleum, sinkhole, earl- Getty thesaurus of historic and current locations
dom, Nicaraguan center, dynasty
Karelian places 37,476 Village, house Historical places in the Karelia region of Finland and Russia
Total ∼17 million←300 million
Table 2. Number of distinct place types in
the consituents of the POIO ontology
Name Size
OpenStreetMap 1506
TGN 1737
GNS/Geonames 648
SUO 648+142a
POIO 2499
a
GNS types+additions
culturally significant milieu contains areas of cultural interest defined as poly-
gons. However, most of these areas are actually collections of multiple points of
interest, which are not modeled separately at all. On the other hand, most of the
other databases listed do not model areas at all, but only provide centerpoints
for even large features. Even worse, it is often difficult to automatically deduce
when a location actually refers to a notable mass of land, such as an amusement
park, instead of a small point, such as a statue.
As regards services, in the vast majority of data and data sources used in
the TravelSampo project, the services described are those that can be described
indirectly through place type, such as being a restaurant or a pharmacy. However,
in the Helsinki City data source the services offered at a particular location are
described separately, for example noting if a particular library offers Internet
access, has a scanner or loans AV equipment.
In the case of the TravelSampo system, particularly as it was making use of
many automatically converted and dynamically updating data sources, it was
decided that these heterogeneities in content modeling could not be unified, at
least without losing information or the expressivity of the original data, but
would have to be resolved at the query construction level. Fortunately, it seems
that some quite general mapping rules could be made to facilitate this, for ex-
ample linking services and events described as separate resources to the places
they are provided or help in, or linking a culture site with no direct description
to the compound description of the larger area it was found in.
As can be gleamed from the table listing the TravelSampo data sources,
events such as concerts and exhibitions were identified as a particularly inter-
esting non-direct element signaling a place of interest. That is, particularly for
cultural applications, often one is for example not interested in a museum per
se, but in the exhibitions that are on display in that museum at present.
Now, events are particularly dynamic sort of data. At the time of creating
the TravelSampo system, there were no sources for current and coming event
information in RDF. However, there were multiple sources from which such could
be gleamed in other formats, such as comma separated values, JSON or RSS.
The event content for TravelSampo thus comes from a converter pipeline that is
capable of being run at regular intervals, or by request. This pipeline is actually
a more general one, called Harava [12], created in the FinnONTO project1 [5] as
a Semantic Web infrastructure tool by which data can be harvested, converted,
enriched and validated to be published as quality Linked Data for anyone to
use2 .
A major problem in the event data sources to be used in the project however
was that none of them contained any machine-processable descriptions of the
topics related to the event, such as the style of an exhibition or the artist. This
problem was also evident in some of the point of interest data sources. For
example, in the OpenStreetMap data on Helsinki, there is an object of type
“memorial”, which only in its textual description says that 1) it is actually a
statue and 2) it depicts the runner Paavo Nurmi.
To overcome these limitations, automated information extraction services
were integrated into the TravelSampo architecture and the Harava pipeline,
which could then extract relevant entities such as people, organizations and
places as well as general content keywords from the textual descriptions of the
events and other data items.
Because the information extraction tools were configured to use the whole
vast TravelSampo database as a source for keywords, they are usually able to pick
up a huge number of potentially related instances. The problem then became
more of filtering these potential instances to the most important and factual
ones. Fortunately, here the project could make use of the open source Maui
information extraction tool [10], which has been previously shown to be human-
competitive in selecting primary topic keywords from text.
4 Place and Event Instance Mapping
After getting all the different data sources together, one finds a large amount of
overlap between them. Besides the same places occurring in many of the place
databases, also events typically are entered in more than one of our dynamically
updated event sources.
The indexing system used in the TravelSampo project is capable of inferring
and resolving ontology language equivalency statements transparently. Thus,
mapping between the different datasources does not need to happen at indexing
time, but can be done centrally and iteratively in the global TravelSampo data
space through generating RDFS, OWL and SKOS equivalencies. Actually, this
task becomes one with the general task of mapping different RDF materials to
each other, and could use any of the readily available ontology and instance
mapping tools [8, 4] for doing just that.
Due to this, all but certain mappings are also relegated into this late stage
of processing, so that for example all keywords found in the data sources during
pipeline processing are created as resources in the data source’s own namespace,
instead of being equated with ontology concepts directly. This also makes sure
1
http://www.seco.tkk.fi/projects/finnonto/
2
The source code of Harava has been released under a MIT style open source license
and published as a Google Code project3 .
that no information is lost and no errors introduced in indexing, due to e.g. the
keywords used not being found in the reference vocabularies, or being translated
to a wrong concept based on improper fuzzy reasoning.
As already said, the semantic enrichment done to the materials through in-
formation extraction tools is also done in this global data space. This ensures
that, for example, when searching for concepts from textual descriptions, the
algorithms have a maximal amount of content available from which to draw
matches.
The transparent resolution of equivalency statements also means that any
erroneous mappings can be undone easily after the fact by just removing the
RDF triple specifying the bad mapping. The tasks of verifying and improving
the resource mappings generated, as well as verifying automatic enrichments, can
be done in the TravelSampo ecosystem through the SAHA metadata editor [9]
created in the FinnONTO project, which has special support for going through
annotations marked as suspect. The marking of such annotations can either be
done originally in the enrichment process, or at a later date by utilizing heuristic
or schema-based quality assessment rules.
For this latter task, the FinnONTO architecture contains the semantic con-
tent validator service VERA4 . The output that VERA produces is not a list of
errors per se, but rather a list of possible problems that an expert user can as-
sess, and modify the schema or data as needed. The report also contains general
statistics about the data, such as language definition usage, so it can also be
used for a general analysis instead of validation.
In this way, the dynamically updated content of the TravelSampo portal
can be iteratively improved and corrected as the system is running, right when
problems are discovered, allowing focusing on the areas most critical to efficient
use.
5 Indexing and Querying
Our stated choice of semantic integration by mapping properties and resources
in RDF required that the triple-store used had to support easy and efficient
resolution of both equivalency as well as subsumption relations, as those were
the primary means used to map content.
In fact, in the custom triple-store implemented for TravelSampo, both of
these are done transparently. As an example, a query for “?s rdfs:label ?o”
would return also all skos:prefLabel and skos:altLabel triples, as well as any
custom schema properties marked as equivalent to any of these. A query for
“?s rdf:type foaf:Agent” on the other hand returns also instances of all the sub-
classes of foaf:Agent. For ease in additional processing, a unified view to the
data is also provided, where all URIs in an equivalency set in the source are
replaced with a single canonical version. This way, anyone processing the results
of such inferred queries need not themselves repeat the equivalency calculation.
4
http://www.seco.tkk.fi/services/vera/
In the material used for TravelSampo, a total of 11 million equivalency sets were
discovered, touching 25 million resources out of a total 350 million.
In addition to subsumption and equivalency inference, the triple-store of
TravelSampo also includes support for quickly discovering all location resources
annotated with geo-coordinates inside a specified bounding-box, as well as all
other resources related to those locations. The same is done for any temporal
entity resources such as event times and further resources related to them. These
are all functionalities that were needed in the various user interfaces of the
TravelSampo system. Similarly, efficient text search is provided for searching 1)
objects by their labels, 2) objects by their literal attributes and 3) objects by
the labels associated with their object attributes. The last index is used in the
general text search interface of TravelSampo, so that one can for instance query
by the string “Pyhäjärvi” and be quickly returned all objects that relate to any
of the 50 or so lake Pyhäjärvis of Finland.
In order to cater to the complexity and heterogeneity of the data sources
used in TravelSampo, the indexing and querying system also has to be able
to efficiently query quite complex patterns. For example, the intent is that for
example the query “Finnish electronica near Helsinki in the following week”
would match a concert by Jimi Tenor at the Helsinki Ice Hall, because Jimi
Tenor is a Finnish electronica artist playing there in the specified timeframe.
However, inside the data model, this is quite a complex pattern, as visualized in
figure 2.
To answer the query, first, each resource with a label matching any of the
keywords must be found, as well as those matching the temporal and spatial
constraints. This results in a result set with (among others) the nationality
Finnish, the genre Electronica, the concert event and the location of Helsinki ice
hall. Then, all resources relating to these or their subconcepts must be added
to the result set. This results in (among others) the artist Jimi Tenor (who is
Finnish) and the album Intervision (which has a genre of downtempo, which is
a subgenre of electronica).
Finally, all resources that are not already locations must be mapped to any
that they refer to, and finally an intersection taken between all locations found to
reveal the final result. In this case, such mappings must also be done iteratively.
While the concert event relates directly to the location, but the artist and the
album are still two and three steps away, respectively. To obtain the final result,
one must follow first the link from album to the artist, and then from the artist
to the event, which then finally leads to the location.
To resolve this, the search functionality in the TravelSampo backend was
split into multiple stages, each taking in SPARQL queries. First, multiple “select”
queries are run, one for each incoming keyword, temporal and spatial constraint,
acting on a dedicated index. Using the index, it is easy to efficiently return
not only resources matching the spatial and temporal constraints, but also any
resource that is related in any way to them, or a literal or another resource with
a label matching a particular text query. In addition, this index also performs
subclass inference. Thus, from this stage, in the case of the example queries one
temporal:
coordinates: next week
near Helsinki
Location: Event:
Helsinki ice hall Jimi Tenor concert Genre:
Electronica
Nationality: Artist:
Finnish Jimi Tenor ...
Album: Genre:
Intervision Downtempo
Fig. 2. Mapping to a final search result after keyword and spatiotemporal matching in
TravelSampo. Dark grey resources are those returned from matching, while the light
grey resources are the final search result.
would already get the artist Jimi Tenor and the album Intervision, in addition
to the more direct resource hits.
Then, “mapping” queries are run separately and iteratively for each select
query result set. In the example, these would map for example any albums to
artists, artists to events and events to locations. After this, the system auto-
matically takes an intersection of the mapped results returned from each select
query. A further “filter” query is also run. In TravelSampo, this makes sure that
only locations ever make it to the final result set returned.
After the result set is finally obtained, it is paged and returned. This can
still be manipulated by a “grouping” query. This can be used to ensure that for
example a set amount of both event locations and culturally significant locations
matching a particular query are returned. To make sure all information to be
shown in the search listing for each matched resource is included (such as images,
event details, etc.), the system still runs any given “describe” queries for each
returned resource, before finally returning answers.
Because of the efficient indexes of TravelSampo as well as caching of e.g.
the mapping query results, the average processing time for even these complex
queries is still 100-400 milliseconds on a modern desktop server.
6 Contributions
While still a work in progress, the TravelSampo system already demonstrates the
potential for a much richer way of searching for points of interest. In developing
the system, multiple issues were identified.
Firstly, locations may be of interest not only through their immediate proper-
ties, but through quite long chains of associations. Secondly, it is hard to isolate
points of interest from other general locations.
In processing actual databases for use in the TravelSampo system, the lack of
machine-processable content keywords in most currently available datasets was
identified to be a major problem. In the TravelSampo system, this was addressed
by integrating state of the art information extraction tools into the system.
In order to enhance precision and recall in searching the heterogeneous datasets,
key class ontology level reference resources in the TravelSampo system such as
point of interest types were mapped to each other by hand. However, another
requisite part of an integration architecture such as TravelSampo is still the sup-
port for iterative, automatic mapping of the instances and keyword concepts in
the different datasets pouring in, sometimes dynamically each day. An equally
important feature is the ability of human editors to correct these mappings.
Finally, the TravelSampo system and the datasets loaded into it highlight
the complexity of queries needed to cater to complex needs, while demonstrating
that answering such queries efficiently even on massive data sources is still quite
possible.
Acknowledgements This research is part of the Semantic Ubiquitous Services
Project (SUBI) 2009–2011, funded by the Finnish Funding Agency for Tech-
nology and Innovation (Tekes) and a consortium of 18 companies and public
organizations, pre-eminently Turku – European Capital of Culture 2011. Some
work has been funded also by the Finnish Cultural Foundation.
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