How Linked Open Data finds the bar near you

Video in TIB AV-Portal: How Linked Open Data finds the bar near you

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Title
How Linked Open Data finds the bar near you
Title of Series
Part Number
127
Number of Parts
193
Author
License
CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Release Date
2016
Language
English

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Subject Area
Abstract
Within the GIS community we became very fond of our web map servers and feature request possibilities to share and access data. Sharing data is relevant and applicable to other fields and communities. This led to the rise of the semantic web and to web 3.0. Clearly defined relationships between objects make it possible to interlink them and allow to search for relationships themselves. In this presentation I will demonstrate a web application that uses different techniques to access linked open data and show how the individual results can be used as input for the next search request. An open innovation platform on linked data was started in the Netherlands. One of their results was to open a server to store and access linked open data. I have used this data warehouse as a starting point for a demonstration in a geo web application. The application is based solely on open source frameworks (OpenLayers, proj4js, jQuery, and pure). The user enters a zipcode and house number, and the application uses linked data techniques to retrieve the location. This first search result connects to the next open dataset to obtain statistical information about the area. One of these statistics is the average number of bars within a 1 km radius. But where exactly are these bars? Using yet another open dataset (OpenStreetMap with Overpass API) we can pinpoint the location of bars and pubs.
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Linked data Level of measurement Focus (optics) Presentation of a group Greatest element Computer animation Block (periodic table) Mereology Value-added network
Functional (mathematics) Presentation of a group Service (economics) Linked data Demo (music) Calculation Mereology Revision control Estimator Envelope (mathematics) Information Address space Linked data Geometry Demo (music) Concentric Expression Data storage device Physicalism Cartesian coordinate system Statistics Approximation Computer animation Calculation Revision control Thermal conductivity Address space
Web page Service (economics) Table (information) Link (knot theory) Weight Linked data Virtual machine Semantic Web Computer Value-added network Variance Neuroinformatik Uniform resource locator Internetworking Data structure Link (knot theory) Relational database Uniqueness quantification Data storage device Semantics (computer science) Semantic Web Uniform resource locator Computer animation Right angle Key (cryptography) Table (information) Data structure Identity management
Level of measurement Link (knot theory) Open source Data storage device Predicate (grammar) Entire function Value-added network Machine vision Word Computer animation Predicate (grammar) Object (grammar) IRIS-T Formal grammar Object (grammar) Spacetime
Linked data Scheduling (computing) Geometry Texture mapping Relational database Forcing (mathematics) Linked data Data storage device Combinational logic Database Instance (computer science) Bookmark (World Wide Web) Entire function Category of being Computer animation Query language Different (Kate Ryan album) Buffer solution Query language Natural language Extension (kinesiology) Table (information)
Statistics Mapping Open source Linked data Open set Mereology Code Variance Homography Query language Information Endliche Modelltheorie output Address space Form (programming) Area Addition Texture mapping Demo (music) Information Mapping Building Open source Data storage device Cartesian coordinate system Statistics Open set God Computer animation Query language Personal digital assistant Address space
Linked data Predicate (grammar) Connected space Goodness of fit Computer animation Predicate (grammar) Query language Object (grammar) Spacetime Object (grammar) output Address space Address space
Geometry Building Computer animation Building Linked data Object (grammar)
Point (geometry) Geometry Building Mapping Server (computing) Linked data Bit Database Function (mathematics) Number Neuroinformatik Mathematics Latent heat Uniform resource locator Computer animation Different (Kate Ryan album) output Cuboid Object (grammar) Data structure Data structure Address space
Geometry Point (geometry) Building Mapping Service (economics) Number theory Network operating system Multiplication sign Linked data Demo (music) Maxima and minima Average Distance Area Number Average Information Conditional-access module Summierbarkeit Error message Area Geometry Information Demo (music) Mapping Building Neighbourhood (graph theory) Code Bit Cartesian coordinate system Statistics Distance Number Radius Computer animation Tower Personal digital assistant IRIS-T Address space
Neighbourhood (graph theory) Demo (music) Demo (music) Neighbourhood (graph theory) Value-added network Computer animation 4 (number) Query language IRIS-T Cuboid 5 (number) Uniform boundedness principle Amenable group
Revision control Uniform resource locator Building Fibonacci number Computer animation Revision control Data storage device Set (mathematics) Cartesian coordinate system Address space Address space
Uniform boundedness principle Uniform resource locator Service (economics) Computer animation Personal digital assistant Neighbourhood (graph theory) Revision control Bit Cartesian coordinate system Uniform boundedness principle
Presentation of a group Mapping Service (economics) Online help Multiplication sign Core dump Coma Berenices Database Mass Value-added network Latent heat Computer animation Meeting/Interview Different (Kate Ryan album) Computer cluster Iteration Maize Conditional-access module
Computer animation
and so for the next
session this block we have problems thinking about how easily do find bursary which is something all tend to well good good afternoon my name is called followed them going to give you a presentation on how linked open data can help you to find the bottom you now with the title like this you can so I'm wondering why why do you want to do this why do you want to use Linked Open Data a why actually do you want to find the bar In the Linked Open Data parts of all all get to that during my presentation but let's focus 1st of all why you want to find out what my
background is in physics and 1 of the great names in physics is an was a very smart guy and the woman about right and all but he also had a very special abilities and that is that he could make rough estimates of very difficult calculations and he was always well quite right so once they really did all the calculations and they got the final answer and they took his approximation well more or less it was again so you could say that his specialism was making calculations on the back of an envelope while abduction and that we don't have this expression calculational back-of-the-envelope conduction expression is a bit different we make calculations on the back of a beer coasters when you find your coasters right at bottom pops to if you want to do these kind of calculations you have to find yourself to closer therefore if function of now let's move on to other
linked data and during my presentation I'll give you a short introduction Linked Data tell you what it is give a short example also how do you is connected into this and then I'll move on to this demo application that I make which searches through addresses over all the linked data store I'm throwing stones map service to make much pictures useful feature services to get some more statistical information and in the end use OpenStreetMap defined ourselves our this demo application is based on a Dutch dataset so for this conference I also made major international version as we can find out still find out which part to go to so Linked Open Data while 1st concentrate on that or they got on the
internet and when the internet was develop and there were lots of well pages that interlinked documents to 1 document to have yet another document and while that's all for for us humans we can just visible on page read what it's about interpret that data and then we'll go on to the next stage and all these webpages there might be a well in HTML table the might be a comma separated values that you might use your features services key-value stores relational databases you name it that's that's out there on the internet and as a set that's quite useful for us humans we can read a we can interpret that data are not yet not so much for you and they don't really understand all the data so luckily we could help the computer by adding a universal structure to our data we can start by naming things and with things I being well what actually is your data and also important start naming relationships between all your data and don't just name them but also identified and make a unique resource locator so you can just link to that and you find your data back and also well start using vocabularies that means that you and don't start randomly randomly naming things that you think of a structure before and the right of course for humans what is status about the and if you do all this then we
move into the Semantic Web which is machine readable because the machine can then understand all this data is about since we have links to all the data things that we you and also links to their relationships between all the data while you can start interlinking all your different datasets and thereby also well include external links to fuel the years the perhaps is going to be clear with a simple example and link
data users triple stores and when I 1st was introduced to this store idea really well I got the idea of say a kindergarten grammar where you have a subject predicate and object so in this example food is connected to borrow food this subject in is the object on and the relationship between them that is connected to well that's predicate the couple more
examples and of triple stores of of triples wrong it's it is a person wrong word for 48 and 49 space in the Netherlands and if a signal and then I mean that's the same Netherlands as well link and the DB pedia is a big attempts to gets like the entire of Wikipedia and put it into a linked data source so other people can say linked to the Netherlands and as everyone knows will talk you talking about the same Netherlands and you can interlinked our we can then uh
assessed or or query this triple stores with a a language called sparql which basically is just the the SQL but it also has a will select star or select some properties where well have your you wear clothes but not very special but what we can do if we had an extension but that's called due spot you can handle and able well you capabilities within your link data so you can get your favourite so well as well known text you can use within more textures overlap buffer buffers all your favorite spatial relationships you can use and create your data so for
instance if we have make agree on select make might make making an overview of all the same people country combinations of all the forces for G participants where the participant places foosball table football and so we can make a well competition schedule perhaps if you want to have to do this using relational databases where all your data is different databases on different data silos it's not going to be that easy but with Linked Data you can combine this entire search query just 1 go and you get your you get your to and so
much for well over a short introduction on the Linked Data let's find herself so far I make this small demo application using open
source technologies and Open Data as open source technology evolves just additional JavaScript with J. query to tie things together and you should see is as to make it look nice OpenLayers 3 to put things on a map and Freud for to handle with project transformation what although Open Data Part I use a triplestore from the pilots and pilots is a Dutch pilot project on the data and 1 of the things they did is to open up a triple store and their song while such Open Data in there and made available with a SPARQL endpoint this case I'm going to use the the addresses and that the data and then I also use some other open data from PET OK which is a very nice well dataset of all kinds of maps from their lands all kinds of subjects and and stuff we're going to use information from statistical models which is the statistical bureau of benevolence because information about areas and Indians OpenStreetMap define ourselves apart so and they're going to use the the data store of pilots to to search strategy and in this by application the user enters a simple address zip codes house number of via what form so special and and then we're going
to use that and I said search queries to the SPARQL endpoints mind how pointing here and they go from subject predicates to objects once we have the the address well then we're going to have start looking the for all the publication that all the public spaces that are somehow connected with the predicates has headers good at specifically address objects once we find this data public space
and we use that as the 1st was the subject now we're going to use it as an object and we're looking at going to look for the subject with the predicates is connected to to the object of public space that we just and then we find ourselves residential objects
then again we use another lead to get from the subject residential objects to obtain the objects of the building from the
building we're going to make another search and get the geometry of the building and then the geometry of the
building don't outputs as a text so as an overview this is what we did we start with a search input from the from the user to just sit coat house number found that the object of the address that's uh representing that that specific address moves on to the public space residential objects uh find 2 connected buildings to that from the building we went it geometry and so in the end we will get the geometry of the buildings associated to that specific mind you get something different than just the location of the address the location of the address would just give you a single point on the map this clearly gives you all the geometries of the associated building with that specific at so it's similar but still a bit different door
lessons here 1st of all I don't this don't do this unless you know well how to live interlinked is data and know your data and data structure well let's see if you I don't know the data structure beforehand then sparkle and linked data techniques and help you to really find out how this structures to Indians arrived at the juncture box and 2nd lesson is what actually this is really where Linked Data shines making all this interconnection interconnections and in the end you'll get the geometries are somehow linked to that specific address and we would be able to do this if you had access to the and to database with SQL but if you all you have is a Web Feature Service well it's computer the but now let's
let's look at the at the demo application and the user and of house number and we started Linked Data search as I just draw for you and of the building owner and this particular building is a bit of a special building it's the church tower and out of 40 and that's the the this 0 0 coordinates of the old that's references I was there a couple of a couple of months ago and in the Florida is is nice thing that's as this was Georgia from user enters a ghost house number and and filing data we get geometry of that specific bills then we basically use a weapon that service to just draw all the other buildings in this neighborhood on the map as stuttering special but then I will determine the at the center of the the building that we got in the 1st place and now get some statistical information about this but this area what do we get well
the Dutch Bureau of Statistics gives you some specific information about the area and the name of the area of the number of houses the number of people the number of cars and distance to the nearest highway distance to the train station all kinds of 1st but also it can also get the average number of bars within a 1 kilometer radius and in this case 42 point 3 and the 1st time I thought I saw this of 1st of all laughter that's sounds like actually quite a lot of 42 point 3 but then of course quite obvious the 2nd question is well great we find that error
bars in the neighborhood where what likely we can use OpenStreetMap if their over API it's very nice and we can do a search query give me all the amenities that are far apart for dispersive particular bounding box of constitute Jason which again can plot that
can see the blue stars on yeah and I was there a couple of weeks ago and I can assure you the spot actually this demo
application this as a set of all made of specific Dutch dataset only and for this the for this conference I I tried to make the international version books and fortunately I couldn't find
a a triple store that has all the addresses of the world's Fair so know what they're but luckily OpenStreetMap to the you once again because there you can search also for address locations and they also provide buildings have so just use the the the locator of OpenStreetMap to find the
location of the way looking for then secondly well we use of and then again to and and to find a bars and pubs in the neighborhood when it tested the application of search for long I realized that this is not gonna work this is not work because this is not enough remember hearing Germany and Germans don't just plotted out there's also the garden and you might laugh at this but there is an important lesson here as well and you really if you make applications like this this you really have to know and understand what they are you're expecting back from Europe but from your your data and services and how to deal with this so in this case we have to help you got as a a bit
of a special for the phosphor phosphogens conference if you search involved and for conference there are a couple of bars that are associated with with the conference and what they are presented with a was your accomplice instead of this just to make it clear
so in overview of and the clean data is great for combining datasets datasets that are present different databases and different services to begin just linked together the data is also nice to to publish and share your datasets with other people and what is big lesson and that's what have you got we have to know your data and so you know what you expect so I would say go ahead and find yourself and have reference but the fear that they too are from so the
time for a few questions and many questions from no OK thanks for always have to wait for the iterates evidence of what and the freedom of I and mean there's so so apparently it was eventually doesn't have get a SPARQL endpoint and the that but there there is a also in tends to pick up OpenStreetMap and put it into a a labeling data so because I think there will be a great opportunity also for this presentation to to do that aquarium 1 sparkle and then idea that of course of massive use great but for specific and collection of people getting bottom cops yeah you're the crickets the it always follows with all the questions is next the world that we want
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