eswc ss 2013 - tuesday tutorial 2 maribel acosta and barry norton: interaction with linked data

Interaction with Linked Data

Presented by: Maribel Acosta Barry Norton

Motivation: Music!

2

Visualiza3on Module

Metadata Streaming providers

Physical Wrapper

Downloads

Data acquisi3

on R2R Transf. LD Wrapper

Musical Content

Applica3

on

Analysis & Mining Module

LD Dataset

Access

LD Wrapper

RDF/ XML

Integrated Dataset

Interlinking Cleansing Vocabulary Mapping

SPARQL Endpoint

Publishing

RDFa

Other content EUCLID – Interac3on with Linked Data

Motivation: Music! (2)

EUCLID – Interac3on with Linked Data 3

•  Our aim: build a music-‐based portal using Linked Data technologies

•  So far, we have studied different mechanisms to consume Linked Data: •  Execu3ng SPARQL queries •  Dereferencing URIs •  Downloading RDF dumps •  Extrac3ng RDFa data

•  The output of these mechanisms corresponds to data in machine-‐readable formats

CH 2

CH 3

CH 1

Examples of machine-‐readable output:



Visualiza=ons techniques are needed in order to transform the machine-‐readable data into this:



Source: hZp://musicbrainz.fluidops.net/

In addi3on, visualiza=on techniques allow for:



•  Telling a story

•  Engaging our paZern matching brain

•  Iden3fying data characteris3cs which cannot be directly inferred from sta3s3cal proper3es: •  Anscombe’s quartet: 4 datasets very

different, but with same sta3s3cal values.

Image: hZp://en.wikipedia.org/wiki/Anscombe's_quartet

Source: Donaldson, I. and Lamere P. Using Visualiza,ons for Music Discovery

Image: Chan W., Qu. H, Mak, W. Visualizing the Seman,c Structure in Classical Musical Works.

Agenda 1.   Linked Data visualiza=on

2.   Linked Data search

3.   Methods for Linked Data analysis

7 EUCLID – Interac3on with Linked Data

LINKED DATA VISUALIZATION


LD Visualization Techniques

•  Linked Data visualiza3on techniques should provide graphical representa=ons of the informa3on within the LD datasets

•  Visualiza3on techniques should be selected accordingly to:

–  The type of data: Specific types of data should be visualized in a certain way

–  The purpose of the visualiza=on: Depending on the type of analysis/applica3on to employ


LD Visualization Techniques (2)


•  (Raw) RDF data: Instance data, taxonomies, ontologies, vocabularies.

•  Analy=cally extracted data: Subset of the data denominated region of interest (ROI), obtained via data extrac,on mechanisms, for example, SPARQL queries.

•  Visualiza=on abstrac=on: It is obtained by applying visualiza,on transforma,ons to render the data into displayable informa3on.

•  View: Final result. The visual mapping transforma3ons obtain a graphic representa3on of the data using the selected visualiza3on technique.

•  User interac=on: The user interacts (click, zoom, etc.) with the visualiza3on, which may trigger a new visualiza3on process.

RDF data

Analy3cally extracted data

Visualiza3on abstrac3on

View

Data extraction

Visualization transformation

Visual mapping transformation

Overview of the Linked Data Visualization process

Process par3ally based on: Brunej , J.M.; Auer, S.; García, R. The Linked Data Visualiza,on Model.

(Op3onal)

User interaction

country releases

United Kingdom 225

United States 140

Germany 30

Luxembourg 29



Example of the Linked Data Visualization process

…

RDF data

Analy3cally extracted data

…

Visualiza3on abstrac3on

SELECT ?country (COUNT(?release) AS ?releases) WHERE { <http://dbpedia.org/resource/The_Beatles> foaf:made ?release . ?release a mo:Release ; mo:label ?label . ?label foaf:based_near ?country .} GROUP BY ?country ORDER BY DESC(?releases)

Data extraction

SPARQL query: Retrieve number of releases per country of The Beatles

#widget : HeatMap | input = 'country_code' | output = {{ 'releases' }}

Visualization transformation

country_code releases

GB 225

US 140

DE 30

LU 29

?country_code2 := REPLACE(str(?country), "hZp://ontologi.es/place/", "", "i”) ?country_code := REPLACE(?country_code2, "%", "", "i")

Formajng the names of the countries

View Visual mapping transformation

Selec3ng the visualiza3on technique (input, output)

Can be performed in a single step

… …



Example of the Linked Data Visualization process

View

Challenges for Linked Data Visualization


•  Enabling user interac=on –  Users must be able to navigate through the data by exploi3ng the

connec3ons between Linked Data resources –  The user might edit the underlying data to enrich it by:

•  Crea3ng addi3onal metadata •  Highligh3ng or correc3ng errors •  Valida3ng data

•  Suppor3ng data reusability –  The output (the ploZed data or the visualiza3on itself) might be

encoded using standard ontologies and vocabularies

•  Scalability –  Linked Data visualiza3on techniques should support the display of

large amount of data in an efficient way

Challenges for Linked Open Data Visualization


•  Extrac3ng data from different repositories –  A Linked Data set might be par33oned into several repositories –  The region of interest (ROI) might include data from different data

sets, requiring the access to distributed repositories

•  Handling heterogeneous data –  The same data (concepts) might be modeled differently, for example,

using different vocabularies –  Certain values might have different formats, for example, dates

represented as DD-‐MM-‐YYYY, MM-‐DD-‐YYYY or just YYYY

•  Dealing with missing values –  Due to the semi-‐structuredness of Linked Data, some instances might

have missing values for certain proper3es

Classification of Visualization Techniques


Task Visualiza=on techniques

Comparison of aZributes / values

•  Bar/column and pie chart •  Line charts •  Histogram

Analysis of rela3onships and hierarchies

•  Graph •  Arc diagram •  Matrix •  Node-‐link visualiza3ons •  Space-‐filling techniques: Treemaps, icicles and sunburst,

circle packing and rose diagrams

Analysis of temporal or geographical events

•  Timeline •  Maps

Analysis of mul3-‐dimensional data

•  Parallel coordinates •  Radar/star chart •  ScaZer plot

Bar/column chart Allows the comparison of values of different categories.

Pie chart Useful for performing comparison of percentages or propor3ons.

Comparison of Attributes / Values


Line chart Allows visualizing data as a series of data points, where the measurement points (x-‐axis) are ordered.

Histogram Graphical representa3on of the distribu3on of the data.

Image source: hZp://mbostock.github.io/protovis/ Image source: hZp://musicbrainz.fluidops.net

Image source: hZp://mbostock.github.io/protovis/ Image source: hZp://musicbrainz.fluidops.net

Arc diagram The nodes are displayed in one dimension, and the arcs represent the connec3ons.

Analysis of Relationships and Hierarchies Graph The data entries are represented as nodes and the links as edges.


Adjacency Matrix diagram The nodes are displayed as rows and columns, and the links between the nodes are entries in the matrix.

Node-‐link visualiza3ons The data is organized in hierarchies.

Source of images: hZp://mbostock.github.io/protovis/

Icicles and sunburst Hierarchies are represented by adjacencies.

Analysis of Relationships and Hierarchies (2) Treemaps Subdivide area into rectangles.


Circle-‐packing Containment is used to represent the hierarchies.

Rose diagrams Areas are equal angles and the data is represented by the extension of the area.

Source of images: hZp://mbostock.github.io/protovis/

Space-‐filling techniqu

es

Analysis of Temporal or Geographical Events

Timeline


Maps

Source: hZp://mbostock.github.io/protovis/

Choropleth maps Aggregate data by geographical area

Loca3on maps Display geo-‐points on a map

Dorling cartograms Aggregate data and replace each area with a circle

Discrete data points in 3me Con3nuous data in 3me

Source: hZp://www.koZke.org/08/08/2008-‐movie-‐box-‐office-‐chart Source: hZp//musicbrainz.fluidops.net

Source: Google Map API Source: hZp//musicbrainz.fluidops.net

ScaZer plot Useful for performing comparison of percentages or propor3ons.

Analysis of Multidimensional Data

Radar/star chart Displays mul3variate data as a two-‐dimensional chart. The axes correspond to the variables.


Parallel coordinates Allows visualizing high-‐dimensional data. Each ver3cal axis denotes a dimension, and a mul3dimensional point is represented as a polyline with ver3ces on the axes.

Source: hZp://mbostock.github.io/protovis/

Source: hZp://mbostock.github.io/protovis/ Source: hZp://mbostock.github.io/protovis/

Other Visualization Techniques


•  Text-‐based visualiza3ons: tag clouds

•  Some of the previously presented techniques can be combined to produce more complex data visualiza3ons

Phrase Net of Beatles Lyrics DBpedia music genres

Source: hZp://www.wordle.net Source: hZp://many-‐eyes.com

•  Get an overview of the data

•  Iden3fica3on of relevant resources, classes or proper=es in datasets

•  Learning about certain underlying characteris=cs of the data, e.g., vocabularies or ontologies

•  Detec3ng missing links between nodes in an RDF graph

•  Discovering new paths between nodes in an RDF graph

•  Iden3fying hidden paUerns in the data

•  Finding errors or atypical values (outliers) 22 EUCLID – Interac3on with Linked Data

Applications of Linked Data Visualization Techniques

Linked Data Visualization Tool Requirements The requirements for visualiza3on tools that consume Linked Data can be summarized as follows:

•  Data naviga=on and explora=on capabili3es in order to understand the structure and the content

•  Exploi3ng data structures: •  Links to visualize hierarchies or graphs •  Mul3-‐dimensional

•  User interac=on: •  Basic and advanced querying •  Filtering values •  Interac3ve UI: responsive to the user input

•  Publica=on/syndica=on of the graphical representa3on of the data •  Data extrac=on in order to export the data such that can be reused by

third par3es


Linked Data Visualization Tool Types 1. LD browsers with text-‐based representation

•  Dereference URIs to retrieve the resource descrip3on •  Use a textual representa3on of LD resources •  Display adequately texts and images •  Mainly support exploratory browsing and knowledge discovery

2. LD and RDF browsers with visualization options •  Exploit picture, graphics, images and other visual representa3ons of the data

•  Support user interac3on: allows for querying, filtering and jumping between resources

•  Suitable for browsing and knowledge discovery as well as analy3c ac3vi3es


Linked Data Visualization Tool Types (2) 3. Visualization toolkits

•  Frameworks providing a wide range of visualiza3on techniques •  General toolkits support LD visualiza3on by applying a set of transforma3ons of the data

•  Some toolkits are specially designed to consume LD

4. SPARQL visualization •  These tools allow transforming the output of SPARQL queries into graphics

•  Contact SPARQL endpoints in order to evaluate the query •  Suitable for analy3cal ac3vi3es


Linked Data Visualization Tool Types (3)


LD browsers with text-‐based presenta3ons

Sig.ma

Sindice

OpenLink RDF Browser

Marbles

Disco Hyperdata Browser

Piggy Bank (SIMILE)

Zitgist DataViewer

iLOD

URI Burner

Dipper – Talis Pla�orm Browser

LD and RDF browsers with visualiza=on op3ons

Tabulator

IsaViz

OpenLink Data Explorer

RDF Gravity

RelFinder

DBpedia Mobile

LESS

SIMILE Exhibit

Haystack

FoaF Explorer

Humboldt

LENA

Noadster

Visualiza3on toolkits

Linked Data tools: Informa3on Workbench

Visual RDF (by Graves)

LOD Live

LOD Visualiza3on

Data-‐Driven Documents (D3)

NetworkX

Many Eyes

Tableau

Prefuse

SPARQL visualiza3on

Informa3on Workbench

Google Visualiza3on API

SPARQL package for R

Gruff (for AllegroGraph)

Linked Data:

General data:

Linked Data Visualization Examples (1)


Sig.ma

Source: hZp://sig.ma/search?q=The+Beatles

Retrieves informa3on from different LD sources

Keyword search

Displays values per predicate

Displays the source for each value



Sig.ma

Source: hZp://sig.ma/search?q=The+Beatles

Displays values per predicate:

May include (redundant) informa3on in different languages, for example: annés and anno

Summary: •  Sig.ma lists all the triples, and group

them per predicate •  Useful for browsing predicates and

values within data sets •  The meaning of the values is not evident

URIs are clickable, allowing naviga3on through RDF resources



Sindice Keyword search

Filtering per type of document

Retrieves links to documents

Allows accessing cache documents

Allows inspec3ng resources

Source: hZp://sindice.com/search?q=The+Beatles



Sindice

Both interfaces display the set of triples related to the inspected resource

Cache triples

Live triples



Information Workbench •  Demo available at: hZp://musicbrainz.fluidops.net

•  Displays human-‐readable content about Linked Data resources

•  Supports visualiza=on techniques (different types of charts,

maps, 3melines, etc.) to plot results from SPARQL queries

•  Allows the user to interact with the displayed data



Information Workbench: Browsing a music artist (1) Search op3ons (2) Search results



Information Workbench: Browsing a music artist (3) Browsing the selected resource



Information Workbench: Visualization techniques (3) Browsing the selected resource



Information Workbench: User interaction LD visualiza3ons must support naviga3on through the data

Source: hZp://musicbrainz.fluidops.net/resource/Analy3cal5



Information Workbench: SPARQL Visualization

Implements widgets which allow:

•  Retrieving ROI via SPARQL queries •  Selec3ng the appropriate visualiza3on technique •  Configuring parameters of the visualiza3on



Information Workbench: SPARQL visualization

SELECT ?release ((SUM(xsd:double(?duration/60000))) AS ?avg) WHERE { <http://dbpedia.org/resource/The_Beatles> foaf:made ?release . ?release mo:record ?record . ?record mo:track ?track . ?track mo:duration ?duration .} GROUP BY ?release ORDER BY DESC(?avg) LIMIT 10

SPARQL Query

Result set

Top ten The Beatles releases according to the sum of track dura,ons in minutes



Information Workbench: SPARQL visualization Top ten The Beatles releases according to the sum of track dura,ons in minutes

Widget

Visualization: Bar chart

{{#widget: BarChart | query ='SELECT (COUNT(?Release) AS ?COUNT) ?label WHERE { <http://musicbrainz.org/artist/8538e728-‐ca0b-‐4321-‐b7e5-‐cff6565dd4c0#_> foaf:made ?Release. ?Release rdf:type mo:Release . ?Release dc:title ?label .} GROUP BY ?label ORDER BY DESC(?COUNT) LIMIT 20' | settings = 'Settings:barvertical_mb' | asynch = 'true' | input = 'label' | output = 'COUNT' | height = '300’}}



Information Workbench: SPARQL visualization Top ten The Beatles releases according to the sum of track dura,ons in minutes Other visualiza3ons of the same result set …

Line chart:

Pie chart:



Information Workbench: Automated Widget Suggestion

Bar chart

Line chart

Pie chart

1

2 3 Table

Pivot view

Select a suggested visualiza3on Visualiza3on automa3cally built



Other tools

Source: hZp://en.lodlive.it Source: hZp://lodvisualiza3on.appspot.com

LOD Visualization LOD live

•  Graph visualiza3ons •  Interac3ve UI (the graph can be

expanded by clicking on the nodes) •  Live access to SPARQL endpoints

•  Hierarchy visualiza3ons: treemaps and trees •  Live access to SPARQL endpoints

(suppor3ng JSON and SPARQL 1.1)

Linking Open Data Cloud Visualization (1)


“The Linking Open Data cloud diagram” by Richard Cyganiak and Anja Jentzsch

Source: hZp://lod-‐cloud.net

•  The nodes correspond to Linked Data sets

•  The edges represent connec3ons between Linked Data sets

•  The size of the nodes is propor3onal to the number of triples in each data set

•  The datasets are categorized by knowledge domains represented with colors



Image source: hZp://twitpic.com/17qj1h

“Linked Open Data Cloud” generated by Gephis

•  The central cluster (green) displays DBpedia as a central focus

•  The size of the nodes reflect the size of the datasets

•  The length of the connec=ons encode informa3on about the data structure

Source: A. Dadzie and M. Rowe. Approaches to Visualizing Linked Data: A Survey. 2011



“Linked Open Data Graph” by Protovis

Source: hZp://inkdroid.org/lod-‐graph/

•  The data to be displayed are retrieved using the CKAN API

•  The nodes represent Linked Data sets available in the Data Hub “lod-‐cloud” group

•  The size of the nodes is propor3onal to the data set size

•  Edges are connec3ons between data sets

•  The colors reflect the CKAN ra3ng and the intensity of the color reflects the number of received ra3ngs

•  The nodes can be clicked to go to the data set CKAN page

LD Reporting


•  Visualiza3ons techniques are used in the crea3on of reports included in data monitoring and management solu3ons

•  Provides and overview of the dataset by genera3ng a low-‐level descrip=ve analysis: •  Quan3ta3ve informa3on about the dataset

•  Users may interact with the data via dashboards

•  Some systems support this feature over structured data: •  Google Webmaster Tools (hZps://www.google.com/webmasters/tools) •  Informa3on Workbench (hZp://www.fluidops.com/informa3on-‐workbench)

•  eCloudManager (hZp://www.fluidops.com/ecloudmanager)

Google Webmaster Tool: Structure Data Dashboard (1)


•  Provides to webmasters informa3on about the structured data embedded in their websites (and recognized by Google)

•  The dashboard three levels: i.   Site-‐level view: aggregates the data by classes defined in

the vocabulary schema

ii.   Item-‐type-‐level view: provides details per page for each type of resource

iii.   Page-‐level view: shows the aZributes of every type of resource on a given web page



Source: hZp://googlewebmastercentral.blogspot.de/2012/07/introducing-‐structured-‐data-‐dashboard.html

Site-‐level view



Source: hZp://googlewebmastercentral.blogspot.de/2012/07/introducing-‐structured-‐data-‐dashboard.html

Page-‐level view

Site-‐level view

LINKED DATA SEARCH


Semantic Search Process

Using semantic models for the search process


Faceted Search

Seman=c Search

Image based on: Tran, T., Herzig, D., Ladwig, G. SemSearchPro-‐ Using seman3cs through the search process

Data graphs Query

Result visualiza=on/presenta=on

User query (e.g. keywords, NL)

Query visualiza=on (Op3onal) User

System

Refinement

Presenta3on

Analysis

Presenta3on / Ranking

Graph matching

En3ty Extrac3on / Seman3c query analysis

Image Source: hZp://musicontology.com

Semantic Search: Example (1)


User query (NL) “songs wriZen by members of the beatles”

En=ty extrac=on:

Query expansion:

song

track

melody

tune

synonym

synonym

mo:Track Candidates

…

song member (of) wriZen by (the) beatles

En=ty mapping:




En=ty extrac=on:

Query expansion:

writer

composer

creator synonym

mo:composer


Candidates wriZen by

inverse of

…

song member (of) wriZen by (the) beatles

En=ty mapping:




En=ty extrac=on: song member (of) wriZen by (the) beatles

Query expansion:

member (of)

mo:member_of mo:member

inverse of


En=ty mapping:





En=ty mapping:

(the) beatles

Candidates

Beatles (Book)

The Beatles (Music Group)

Beatle (Animal)

Beatle (Automobile)

How to iden3fy the right “Beatle”? Examine the context (Contextual Analysis)





En=ty mapping:

(the) beatles

Contextual Analysis

foaf:Agent mo:composer

mo:Track

mo: MusicAr3st

rdfs:subClassOf

mo: MusicGroup

mo:member

rdfs:subClassOf

This subgraph is part of the query

The Beatles (Music Group)

dbpedia: The_Beatles

En=ty mapping:





?y

Mo:Track

?x mo:composer

a

dbpedia: The_Beatles

mo:member

Results (I want to) Come Home Angel in Disguise Another Day …

Answers presented to the user The results could be ranked

Query foaf:Agent a

Semantic Search

•  Aims at understanding the meaning of the resources specified in the query

•  Different approaches to exploit seman3cs:

•  Query expansion using ontologies Since ontologies represent knowledge about specific domains, they can be used to expand the query by incorpora3ng related ontology terms into the query.

•  Contextual analysis In LD, this approach may explore the resources specified in the query and their adjacent nodes in the RDF graph. Mainly applied to disambiguate query terms.

•  Reasoning In some cases, the answer to a specific query is not explicitly contained in the data, but it can be computed by using reasoning methods.


Semantic Search & Linked Data


Component Seman=c search SPARQL query

Keyword or NL / concept matching

Performs en3ty extrac3on and matching to formal concepts

Not supported

Fuzzy concepts/rela3on/logics

Allows the applica3on of fuzzy qualifiers as query constrains

Not supported

Graph paZerns Uses the context and other seman3c informa3on to locate interes3ng sub-‐graphs

Applies paZern matching

Path discovery Finds new interes=ng links that may lead to addi3onal informa3on

Not supported

Semantic Search vs. SPARQL query

Semantic Search: Google (1)


Input: query in NL Output: List of answers

Google performs seman3c search on certain en33es and queries!

Semantic Search: Google (2)


Input: ques3on in NL

Output: List of web pages ranked using the algorithm Google PageRank to display the most relevant pages first

Semantic Search: DuckDuckGo (1)


Input: ques3on in NL

Output: List of answers

Semantic Search: DuckDuckGo (2)


Performs disambigua=on of the query terms.

The 45 sugges=ons are grouped by classes according to their corresponding knowledge domain: This approach is denominated Faceted Search

Faceted Search: Example

Information Workbench: Searching for artists in categories


Facet

Facet

Facet

Source: hZp://musicbrainz.fluidops.net/resource/mo:MusicAr3st?view=pivot

Depic3ons of ar3sts

Faceted Search

•  Facets = proper3es

•  Suitable for browsing mul=-‐dimensional taxonomies based on the search aZributes

•  Allows user to explore the data: •  User submits a (keyword) query

•  Faceted system dynamically iden3fies the relevant facets (proper3es) for the given query and the constrains (values of those proper3es), and display the search results

•  User may “drill down” by selec3ng specific constrains to the search results

•  Informa3on can be accessed and ranked in mul3ple ways


Faceted Search (2)

Challenges for supporting Faceted Search

•  Iden3fying which facets to surface: •  In heterogeneous datasets, data entries may have different facets

•  Dynamically iden3fy the most appropriate facets for each query

•  Ordering the facets depending on the relevance to the query

•  Compu3ng previews: •  Accurately predic3ng counts, without examining all the results

•  Offering facet preview to give users an idea of what to expect


Source: Teevan , J., Dumais, S., GuZ. Z. Challenges for Suppor3ng Faceted Search in Large, Heterogeneous Corpora like the Web

Faceted Search: LD Example (1)

FacetedDBLP

•  Retrieves informa3on from the DBLP collec=on

•  Shows the result set with different facets: •  Publica3on years •  Authors •  Conferences

•  It is implemented upon the DBLP++ dataset (enhancement of DBLP including addi3onal keywords and abstracts): •  DBLP ++ is stored in a MySQL database •  Uses D2R server to consume RDF triples


Faceted Search: LD Example (2)


Input: “crowdsourcing”

Facets

485 results

FacetedDBLP

Classification of Search Engines


Seman=c Search Systems

Faceted Search Systems

Google (GKG) Bing

KIM

sig.ma

LOD cloud cache /facet

Longwell

mSpace

Exhibit (SIMILE)

PoolParty Seman3c Search Server

DuckDuckGo

Hakia

SenseBot

PowerSet

DeepDive

Kosmix Fac3bles

Lexxe

Informa3on Workbench

Searching for Semantic Data


Search for

•  Ontologies

•  Vocabularies

•  RDF documents

Semantic Data Search Engines (1)


Searching for ontologies Swoogle

hZp://kmi-‐web05.open.ac.uk/WatsonWUI hZp://swoogle.umbc.edu

Watson

Keyword search

Keyword search


Searching for vocabularies: LOV Portal

•  Allows to search proper=es, classes or vocabularies in the Linked Open Vocabulary (LOV) catalog

•  The LOV search engine implement faceted search on: •  The knowledge domain •  The role of the resource matched from the input query •  The vocabulary containing the resource

•  Results are ranked according to a score considering: •  Relevancy to the query (string) •  Element labels matched importance •  Number of LOV vocabularies that refer to the element




Facets 84 results

Input: “ar3st”

CH 3

Searching for vocabularies: LOV Portal



Searching for documents

hZp://swse.deri.org hZp://sindice.com

Seman3c Web Search Engine Sindice

METHODS FOR LINKED DATA ANALYSIS


Features of Data Analysis


Sta3s3cal analysis •  Allows describing the data via Exploratory Data Analysis (EDA) methods •  Includes sta3s3cal inference and predic3on

Data aggrega3on & filtering •  One of the first steps in data analysis is pre-‐processing in order to select the

appropriate data to study

Visualiza=on techniques can be built on top of these as part of data analysis

Machine learning •  Focuses on predic3on •  Combines Ar3ficial Intelligence and Sta3s3cs •  Includes supervised and unsupervised learning (not covered in this course)

LD Data Aggregation & Filtering


•  Data aggrega3on refers to merging/summarizing several values into a single a one

•  Filtering allows retrieving relevant data proper3es and selec3ng a par3cular range of data values

•  SPARQL is able to perform these features via SELECT queries as follows:

Features SPARQL capabili=es

Aggrega3on Combining aggregate func3ons (COUNT, SUM, AVG, … ) and GROUP BY operator

Filtering Combining projec3on, FILTER and HAVING operators

LD Statistical Analysis


•  Sta3s3cal analysis supports descrip=ve and predic=ve opera3ons

•  SPARQL supports some descrip=ve opera=ons (average, maximum, minimum) but does not offer more sophis3cated sta3s3cal features like: •  Fijng distribu3ons •  Linear regressions •  Analysis of variance •  …

•  Some approaches are able to consume data retrieved from SPARQL endpoints: –  “R for SPARQL” by Willen Robert van Hage & Tomi Kauppinen –  “Performing Sta,s,cal Methods on Linked Data” by Zapilko & Mathiak

R – Statistical Computing


•  R is a language and environment for sta=s=cal compu=ng

•  R provides a wide variety of sta=s=cal and graphical techniques •  Linear and nonlinear modeling •  Classical sta3s3cal tests •  Time-‐series analysis •  Classifica3on (Machine Learning) •  Clustering (Machine Learning) •  Extensible with further func3onali3es

•  R is available as Free So_ware (under the terms of the GNU general public license)

Statistical Analysis with R


R for SPARQL


•  The R for SPARQL Package enables to: •  Connect a SPARQL endpoint over HTTP •  Pose a SELECT query or an UPDATE opera3on (LOAD, INSERT, DELETE)

•  If given a SELECT query, it returns the results as a data frame •  The results can directly be mapped and visualized

•  Posing requests: •  If the parameter query is given, it is assumed that the input is a SELECT query

and a GET request will be performed to get the results from the URL of the endpoint

•  If the parameter update is given, it is assumed that the input is an UPDATE opera3on and a POST request will be submit to the URL of the endpoint. Nothing is returned

Source: hZp://linkedscience.org/tools/sparql-‐package-‐for-‐r/

R for SPARQL: Example (1)


1. Download the R package and load it: •  library(SPARQL) •  Library(sp) #user for plotting spatial data

2. Define the endpoint with the triples •  endpoint = "http://spatial.linkedscience.org/sparql"

3. Define the query •  q = "SELECT ?cell ?row ?col ?polygon ?DEFOR_2002

WHERE { ?cell a <http://linkedscience.org/lsv/ns#Item> ; <http://spatial.linkedscience.org/context/amazon/Lin> ?row ; <http://spatial.linkedscience.org/context/amazon/Col> ?col; <http://observedchange.com/tisc/ns#geometry> ?polygon . <http://spatial.linkedscience.org/context/amazon/DEFOR_2002> ?DEFOR_2002 . }"

Source: hZp://linkedscience.org/tools/sparql-‐package-‐for-‐r



4. Link the result to an object •  res <-‐ SPARQL(endpoint,q)$results

5. Handling the results •  res$row <-‐ -‐res$row •  coordinates(res) <-‐ ~col -‐ row

6. Chose the graphical format and plot the results •  spplot(res,"DEFOR_2002",col.regions=rev(heat.colors(

17))[-‐1], at=(0:16)/100, main="relative deforestation per pixel during 2002")


Machine Learning


•  Machine Learning techniques allow to extract interes3ng informa3on from data sources, and can be used to discover hidden paUerns within datasets by generalizing from examples

•  Different ML approaches can be applied: •  Clustering: groups similar data into data par33ons called clusters •  Associa=on rule learning: discovers rela3ons between variables •  Decision tree learning: analyses observa3ons to build a predic3ve

model represented as a tree •  Many others …

•  Weka is a Data Mining framework commonly used to apply ML on tabular data: –  www.cs.waikato.ac.nz/ml/weka

Machine Learning on LD


Challenges for applying Machine Learning on LD •  LD heterogeneity introduces noise to the data:

–  Same LD resources, different URIs –  Predicates with similar seman3cs, but different constraints

•  The data is not independent and iden3cally distributed (iid): –  It does not consist of only one type of objects –  The en33es are related to each other

•  LD rarely contains nega=ve examples needed for ML algorithms: –  For example, owl:differentFrom

Source hZp://www.cip.ifi.lmu.de/~nickel/iswc2012-‐slides

Applications of Machine Learning on LD


•  Node ranking: –  Ranking nodes according to their relevance for a query

•  Link predic=on: –  Infer edges between LD resources –  Predict the new edges that will be added to the RDF graph

•  En=ty resolu=on: –  Determine whether two URIs correspond to the same real-‐world object

•  Taxonomy learning: –  Infer taxonomies or concept hierarchies from a given vocabulary or ontology

Summary


•  Linked Data visualiza3on techniques: •  Visualiza3ons must be chosen according the type of the data •  Wide variety of tools suppor3ng SPARQL results’ visualiza=on •  Might be used in dashboards for suppor3ng administra3ve tasks

•  Linked Data search •  Seman=c search: exploits the meaning of user queries (NL or set of

keywords) to present useful results •  Faceted search: allows browsing mul3-‐dimensional data

•  Linked Data analysis: •  Includes data manipula3on such as aggrega=on & filtering •  Applies sta=s=cal methods to get a beZer understanding of the data •  Machine Learning techniques can be applied for predic3ve analysis •  Visualiza=on techniques can be built on top of the previous features

For exercises, quiz and further material visit our website:

EUCLID -‐ Providing Linked Data 88

@euclid_project euclidproject euclidproject

http://www.euclid-‐project.eu

Other channels:

eBook Course

eswc ss 2013 - tuesday tutorial 2 maribel acosta and barry norton: interaction with linked data

Technology

country releases unitedkingdom

country orderbydesc

code releases gb

code output

onsformusicdiscover

quartet source

release mo

heatmap input