Searching the Web of Data (Tutorial)

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Searching the Web of Data
Outline I
1 Structured data on the Web
Semantic markup
Semantic Web and Linked Open Data
Data management
2 Querying Linked Open Data
Browser-based link traversal
Keyword search for Linked Data
Structured queries
Katja Hose Searching the Web of Data March 24, 2013 – ECIR 2013 2 / 73

Structured data on the Web
Semantic Markup (metadata, tags) embedded in HTML
Microformats, hCard, hCalendar, RDFa
Knowledge bases
Large collections of RDF data
Linked (Open) Data
References between collections of RDF data

Semantic markup
Semantic markup
If the search engine gets some help in better “understanding” the content
of a web page, rich snippets highlighting and displaying certain information
can be created.
http://support.google.com/webmasters/bin/answer.py?hl=en&answer=99170

Semantic markup
Microformats
Eﬀorts started in 2003
Fixed formats for speciﬁc type of information
hCard: people, companies, organizations, and places
hCalendar: calendaring and events
hReview: reviews of products, companies, events
. . .
Cannot represent arbitrary data
Indexed by Google and Yahoo since 2009

Semantic markup
hCard example
<div>
<img src="www.example.com/bobsmith.jpg" />
<strong>Bob Smith</strong>
Senior editor at ACME Reviews
200 Main St
Desertville, AZ 12345
</div>
<div class="vcard">
<img class="photo" src="www.example.com/bobsmith.jpg" />
<strong class="fn">Bob Smith</strong>
<span class="title">Senior editor</span> at
<span class="org">ACME Reviews</span>
<span class="adr">
<span class="street-address">200 Main St</span>
<span class="locality">Desertville</span>,
<span class="region">AZ</span>
<span class="postal-code">12345</span>
</span>
</div> http://support.google.com/webmasters/bin/answer.py?hl=en&answer=146897

Semantic markup
RDFa
Proposed in 2004, W3C recommendation
Can be used together with any vocabulary (no restriction on schema)
Can assign URIs as global primary keys to entities

Semantic markup
RDFa example
<div>
My name is Bob Smith but people call me Smithy. Here is my home page:
<a href="http://www.example.com">www.example.com</a>.
I live in Albuquerque, NM and work as an engineer at ACME Corp.
</div>
<div xmlns:v="http://rdf.data-vocabulary.org/#" typeof="v:Person">
My name is <span property="v:name">Bob Smith</span>,
but people call me <span property="v:nickname">Smithy</span>.
Here is my homepage:
<a href="http://www.example.com" rel="v:url">www.example.com</a>.
I live in Albuquerque, NM and work as an
<span property="v:title">engineer</span>
at <span property="v:affiliation">ACME Corp</span>.
</div>

Semantic markup
Facebook’s Open Graph Protocol
Introduction of “like” buttons in 2010
Allows site owners to determine how entities are displayed in Facebook
Relies on RDFa for encoding data in HTML pages
http://developers.facebook.com/docs/opengraphprotocol/

Semantic markup
Microdata
Proposed in 2009 as part of HTML5
Alternative technique for embedding structured data
Tries to be simpler than RDFa

Semantic markup
Microdata
<div>
My name is Bob Smith but people call me Smithy. Here is my home page:
<a href="http://www.example.com">www.example.com</a>
I live in Albuquerque, NM and work as an engineer at ACME Corp.
</div>
<div itemscope itemtype="http://data-vocabulary.org/Person">
My name is <span itemprop="name">Bob Smith</span>
but people call me <span itemprop="nickname">Smithy</span>.
Here is my homepage:
<a href="http://www.example.com" itemprop="url">www.example.com</a>
I live in Albuquerque, NM and work as an
<span itemprop="title">engineer</span>
at <span itemprop="affiliation">ACME Corp</span>.
</div>

Semantic markup
schema.org
“Standardized” vocabulary 2011, supported by Bing, Google, Yahoo
and Yandex
Ask site owners to embed data to enrich search results
200+ types: event, organization, person, place, product, review,. . .
Encoding: basically microdata (RDFa)
Main usage: highlighting and enriching data snippets in search results

Semantic markup
Embedded data in HTML
RDFa and Microdata usage grows, microformats are still present
A rather small set of vocabularies is used
The content and the vocabularies are very focused towards the major
consumers

Google Knowledge Graph
Currently1, more than 500 million entities, such as celebrities, cities,
movies,. . .
Consists of commercial third-party data and Web data
Enriching search results with summaries
Is increasingly being used by Google to answer queries
1
Google Oﬃcial Blog http://googleblog.blogspot.co.uk/2012/05/
introducing-knowledge-graph-things-not.html

Google Knowledge Graph

Semantic markup
Data management
Structured queries

Semantic Web

http://www.slideshare.net/lod2project/the-semantic-data-web-sren-auer-university-of-leipzig

Semantic Web
Web 1.0 (standard Web)
Web 2.0 (data generated by users)
Web 3.0 (Semantic Web)
Machine-readable data
URIs for documents and concepts (entities)
“Web of data”
Web server Web 1.0:

http://www.slideshare.net/cloudofdata/
toward-the-data-cloud

Standards
Sharing structured data across the Web relies on standards
Standard graph-based data model
RDF
Diﬀerent syntaxes and formats
RDF/XML, RDFa
Powerful, logic-based schema languages and reasoning
OWL
Query languages and protocols
HTTP, SPARQL

Linked Open Data: design issues
Design issues (rules)
1 Use URIs as names for things
2 Use HTTP URIs so that people can look up those names
3 When someone looks up a URI, provide useful information, using the
standards (RDF, SPARQL)
4 Include links to URIs in other datasets
Goal: linking URIs in diﬀerent data sets describing the same real
world entity

Growth: Semantic Web and Linked Open Data
May 2007

March 2008

July 2009

September 2011

Data management
Semantic markup
Data management
Structured queries

Data management
Knowledge bases
Large collections of semantic data
YAGO [9], Freebase [10], DBpedia [2],. . .
Mostly result of information extraction
Data format in general RDF
Often participate as sources in the Linked Open Data cloud

Data management
RDF
Each resource (entity) is identiﬁed by a globally unique URI
Data is stored in the form of facts
Triple: (subject, property, object)
Subject: URI
Predicate/Property: URI
Object: URI or literal (strings, integers, booleans, etc.)
http://dbpedia.org/resource/Aalborg
http://dbpedia.org/ontology/country
http://dbpedia.org/resource/Denmark

Data management
RDF
Each resource (entity) is identiﬁed by a globally unique URI
Data is stored in the form of facts
Triple: (subject, property, object)
Subject: URI
Predicate/Property: URI
Object: URI or literal (strings, integers, booleans, etc.)
Using preﬁxes
dbpedia:Aalborg
dbpedia-owl:country
dbpedia:Denmark
(dbpedia:Aalborg, dbpedia-owl:country, dbpedia:Denmark)

Data management
RDF
Triples connect to graphs
dbpedia-owl:country
dbpedia:Aalborg
dbpedia-owl:country
dbpedia:Denmark
dbpedia-owl:isPartOf
dbpedia:North_Denmark_Region
123432
dbpedia:populationTotal

Data management
RDF
Triples connect to graphs. . . and possibly other sources
dbpedia-owl:country
dbpedia:Aalborg
dbpedia-owl:country
dbpedia:Denmark
123432
yago:Denmark
geonames:2624886

Data management
Schema
The schema (vocabulary, ontology) can be expressed in OWL
Deﬁnition of classes, properties, restrictions,. . .
Allows for validation and reasoning
Schema information is also represented as RDF triples

Data management
Managing large amounts of RDF data
Relational RDF data management
A single relational table
Three columns (subject, property, object)
Property tables
n-ary table columns for the same subject
Binary tables
One two-column table for each property

Data management
A single relational table
Example triples
(dbpedia:Aalborg, dbpedia-owl:isPartOf dbpedia:North Denmark Region)
(dbpedia:North Denmark Region, dbpedia-owl:country, dbpedia:Denmark)
(dbpedia:Aalborg, dbpedia-owl:populationTotal, 123432)
subject property object
dbpedia:Aalborg dbpedia-owl:country dbpedia:Denmark
dbpedia:Aalborg dbpedia-owl:isPartOf dbpedia:North Denmark Region
dbpedia:North Denmark Region dbpedia-owl:country dbpedia:Denmark
dbpedia:Aalborg dbpedia-owl:populationTotal 123432
Works with standard relational DBMS and SQL
Problems: self joins, query optimization

Data management
Property tables
Example triples
city
subject country isPartOf populationTotal
dbpedia:Aalborg dbpedia:Denmark dbpedia:North Denmark Region 123432
region
subject country
dbpedia:North Denmark Region dbpedia:Denmark
Grouping information about entities with similar properties
n-ary tables for the same subject
Diﬃcult to create a proper layout
Null values
Problems with multi-valued attributes

Data management
Property tables
Example triples
city
dbpedia:Kassel dbpedia:Germany 195530
region
subject country
Null values
Problems with multi-valued attributesKatja Hose Searching the Web of Data March 24, 2013 – ECIR 2013 30 / 73

Data management
Property tables
city
dbpedia:Kassel dbpedia:Germany 195530
region
subject country
Null values
Problems with multi-valued attributes

Data management
Binary tables
Example triples
dbpedia-owl:country
subject object
dbpedia:Aalborg dbpedia:Denmark
subject object
dbpedia:Aalborg dbpedia:North Denmark Region
dbpedia-owl:populationTotal
subject object
dbpedia:Aalborg 123432
Create a seperate table for each property
Can become inefficient for queries involving many common properties
Becomes inefficient if there are too many different properties

Data management
Binary tables
dbpedia-owl:country
subject object
dbpedia:Aalborg dbpedia:Denmark
subject object
dbpedia:Aalborg dbpedia:North Denmark Region
dbpedia-owl:populationTotal
subject object
dbpedia:Aalborg 123432
Create a seperate table for each property
Can become inefficient for queries involving many common properties
Becomes inefficient if there are too many different properties

Data management
Native triple stores
RDF-3X [7]
Dictionary encoding to reduce storage space
Extensive use of B+-tree indexes
(SPO, OPS, PSO, SOP, OSP, POS)
Aggregated indexes: S, P, O, SP, SO, PO, PS, OP, OS
Triples are materialized in the indexes
Histograms provide the query optimizer with further statistcis

Data management
Column stores
SW-Store [1]
Binary tables in combination with a column-oriented DBMS (C-store)
Sorted tables
Supports multi-valued attribues (listed in a successive row)
Increased costs for updates and tuple reconstruction

Data management
More alternatives
Store RDF data in a matrix with bit-vector compression
Store RDF as XML and use XML technology
Graph databases
. . .

Querying Linked Open Data
Semantic markup
Data management
Structured queries

Who is Carlo Pedersoli?

Browser-based link traversal is the most “natural” way of looking up
information using Linked Data
Might be very tedious and frustrating
Takes much time
But you will discover much information that you never intended to
search for

Semantic markup
Data management
Structured queries

Given a set of keywords, ﬁnd all relevant information.

Falcons
Focus: entities
Crawling
Follow links contained in RDF documents
Construct virtual documents for entities
Literals
Human-readable names and descriptions (rdfs:label, rdfs:comment)
Create indexes
Terms in virtual documents
Entity classes

Falcons
Query processing
Keywords
Filtering based on classes/types
Output: entities with snippets

Falcons
http://ws.nju.edu.cn/falcons/objectsearch/index.jsp

Sindice
Original focus: documents and sources
Indexes
URI (Uniﬁed Resource Identiﬁer)
IFP (Inverse Functional Properties)
Literal

Sindice
http://sindice.com

Indexing
Indexes on virtual documents
Creating virtual documents based on the data (entity,
source,triple,. . . )
Create inverted indexes on URIs, properties, classes, literals,. . . or
combinations

Structured queries
Semantic markup
Data management
Structured queries

Structured queries
Structured queries
What are the movies that both Carlo Pedersoli (Bud Spencer) and
Mario Girotti (Terence Hill) acted in?
Or what are the movies that only one of them (without the other)
acted in?

Structured queries
Structured queries
Structured query language
SPARQL
Query processing strategies
Materialized query processing
Lookup-based query processing
Federated query processing

Structured queries
SPARQL
Example query
PREFIX dbpedia: http://dbpedia.org/property/
PREFIX dbpedia-owl: http://dbpedia.org/ontology/
SELECT ?city, ?pop WHERE {
?city dbpedia-owl:country dbpedia:Denmark .
?city dbpedia:populationTotal ?pop .
FILTER (?pop 100000)
}
SPARQL
Similar to SQL
Variables start with “?”
Queries consist of triple patterns, e.g.:
?city dbpedia-owl:country dbpedia:Denmark
Joins between triple patterns are expressed by common variables
Filters express additional constraints

Structured queries
SPARQL
Query
dbpedia-owl:country
dbpedia:Denmark
?pop ?city
Data
dbpedia-owl:country
dbpedia:Aalborg
dbpedia-owl:country
dbpedia:Denmark
123432
yago:Denmark
geonames:2624886

Structured queries

Structured queries
Characteristics
Centralized storage
Crawl and download the data
Evaluate queries locally
As an alternative to evaluating queries on huge data sets on a single
machine, we can make use of distributed architectures and parallel
processing, e.g., MapReduce, NoSQL, P2P, Grid,. . . .
Problem
Hash partitioning is not optimal for complex queries
Possible solution
Clustered RDF management using graph partitioning [6]

Structured queries
Clustered RDF management
Data graph

Structured queries
Graph partitioning

Structured queries
Assigning triples to partitions – 1-hop guarantee

Structured queries
2-hop guarantee

Structured queries
Query execution is more eﬃcient in RDF stores than in Hadoop [6]
Goals
Pushing as much of the processing as possible into RDF stores
Minimizing the number of Hadoop jobs
The larger the hop guarantee, the more work is done in RDF stores
Query processing
Choose center of the query graph
Calculate distance from the center to the furthest edge
If distance = n: query can be handled by nodes independently
without communication
If distance n: communication is needed, split up into smaller
subqueries, Hadoop

Structured queries
Find football players playing for clubs in a region where they were born
SELECT ?player ?club ?region WHERE {
?player rdf:type ex:footballer .
?player ex:playsFor ?club .
?player ex:bornIn ?region .
?club ex:region ?region .
?region ex:population ?pop .
}
ex:footballer
?pop
?club
?player
?region
rdf:type
ex:bornIn
ex:population
ex:playsFor
ex:region

Structured queries
Find football players playing for clubs in a region where they were born
ex:footballer
?pop
?club
?player
?region
rdf:type
ex:bornIn
ex:population
ex:playsFor
ex:region

Structured queries
If the query is too “big”, the query is decomposed into multiple smaller
subqueries, and the results are combined using MapReduce.
ex:cites
ex:writtenBy
ex:hasTitle
ex:writtenBy
ex:hasName
ex:hasName
isOwned
isOwned
?name2?author2
?title2
?name1?author1
?art2?art1
Workload-Aware Replication [5]
Replicate additional queries at the boundaries
Avoid MapReduce by using a designated coordinator node

Structured queries
No statistics or indexes
Evaluate parts of the query locally
Dereference URIs in intermediate solutions, download the data
Use downloaded data to compute other parts of the query,
dereference. . .
Based on technologies originally developed for distributed database
systems, P2P systems, and data integration
Data is stored
Evaluate parts of the query on remote sources

Structured queries
Federated SPARQL query processing [8]
SPARQL Request Query Result
Parsing Source Selection
Query Execution
(Bound Joins)
Global Optimizations
(Groupings + Join Order)
SPARQL
Endpoint 1 . . .
Subquery Generation:
Evaluation at
Relevant Endpoints
Local
Aggregation of
Partial ResultsCache
Per Triple Pattern
SPARQL ASK queries
SPARQL
Endpoint 2
SPARQL
Endpoint N
Assumption
The sources are capable (and willing) to evaluate SPARQL queries
(SPARQL endpoints).

Structured queries
Source selection
Goal
Identify sources that might contribute to the query result
Approaches
Naive
SPARQL ASK requests and caching
Statistics and indexes
Keyword indexes
Predicate URIs, types of instances
URI indexes
Frequent paths
Service-level descriptions
VoiD statistics
Histograms
. . .

Structured queries
Naive federated query processing
Scenario
SPARQL endpoints
No statistics/indexes

Structured queries
Scenario
SPARQL endpoints
No statistics/indexes
Example query
SELECT ?Country ?Capital
?CountryPop ?CapitalPop WHERE {
?Country ex:capital ?Capital .
?Country ex:population ?CountryPop .
?Capital ex:population ?CapitalPop .
}

Structured queries
Example query
}
Send message with triple pattern
to all 4 sources → 4 requests
Receive 200 mappings for
?Country and ?Capital
e.g., ?Country=ex:Germany,
?Capital=ex:Berlin

Structured queries
Example query
}
Use results to evaluate 2nd
triple pattern (nested loop)
200 × 4 requests
e.g., SELECT ?CountryPop WHERE
{ex:Germany ex:population
?CountryPop .}
150 mappings

Structured queries
Example query
}
Use results to evaluate 3rd triple
pattern (nested loop)
150 × 4 requests
e.g., SELECT ?CapitalPop WHERE
{ex:Berlin ex:population
?CapitalPop .}

Structured queries
Example query
}
In total:
4 + 200 × 4 + 150 × 4 = 1404
requests
Many (unnecessary) requests sent to the sources!

Structured queries
Source selection
Goal
Approaches
Naive
Keyword indexes
URI indexes
Frequent paths
VoiD statistics
Histograms
. . .

Structured queries
Source selection with SPARQL ASK
Does not require special cooperation from the sources
Example query
}
Each source receives a message for each triple pattern
Response: true/false
ASK {
}

Structured queries
Example query
}
ASK {
}
ASK {
}

Structured queries
Example query
}
ASK {
}
ASK {
}
ASK {
}

Structured queries
Source selection
Goal
Approaches
Naive
Keyword indexes
URI indexes
Frequent paths
VoiD statistics
Histograms
. . .

Structured queries
Source selection with VoID statistics
Example DBpedia2
Preﬁxes
@prefix owl: http://www.w3.org/2002/07/owl#.
@prefix rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns# .
@prefix dbpedia: http://dbpedia.org/resource/ .
@prefix dbpo: http://dbpedia.org/ontology/ .
...
General information
Basic statistics
Predicate statistics
Class statistics
2
http://code.google.com/p/fbench/source/browse/trunk/EvalBenchmark/suites/SPLENDID/void/
dbpedia3.5.1_subset-void.n3?r=119

Structured queries
Example DBpedia2
Preﬁxes
General information
void:sparqlEndpoint http://dbpedia.org/sparql ;
...
Basic statistics
Class statistics
2

Structured queries
Example DBpedia2
Preﬁxes
General information
Basic statistics
void:triples 43620475 xsd:integer ;
void:entities 2222456 xsd:integer ;
void:properties 1063 xsd:integer ;
void:distinctSubjects 9495865 xsd:integer ;
void:distinctObjects 13636604 xsd:integer ;
Class statistics
2

Structured queries
Example DBpedia2
Preﬁxes
General information
Basic statistics
void:propertyPartition [
void:property dbpo:aSide ;
void:distinctObjects 1554 xsd:integer
] , [
void:property dbpo:abbreviation ;
void:distinctObjects 1096 xsd:integer
] , [
...
];
Class statistics
2

Structured queries
Example DBpedia2
Preﬁxes
General information
Basic statistics
Class statistics
void:classPartition [
void:class dbpo:Activity ;
void:entities 1234 xsd:integer
] , [
void:class dbpo:Actor ;
void:entities 37898 xsd:integer
] , [
...
]
2

Structured queries
Source selection
Goal
Approaches
Naive
Keyword indexes
URI indexes
Frequent paths
VoiD statistics
Histograms
. . .

Structured queries
Histogram-based indexing
Index complete triples
Index (s,p,o) in combinations capturing correlation
Based on multidimensional histograms
Identify relevant sources for triple patterns
Identify relevant sources for joins

Structured queries
Histograms
Transform triple statements into numerical space (hash functions)
(ex:BudSpencer ex:actedIn ex:m1234) → (323, 232, 124)
Insert into the matching bucket
Lookup: transform triple pattern into numerical space
(ex:BudSpencer ex:actedIn ?m)

Structured queries
Histograms
s
o

Structured queries
Histograms
s
o
s
o 15
15
16
1
0 0
0 0
0

Structured queries
Histograms
s
o
s
o 15
15
16
1
0 0
0 0
0
s
o 15
15
16
1
0 0
0 0
0

Structured queries
Histograms
Alternative to histograms (QTree or clustering) [11]:
s
o A1
A2 A
B
C
B1
B2
s
o A1
A2 A
B
C
B1
B2
s
o

Structured queries
Join cardinality estimation and source selection
Determine buckets for 1st triple pattern
Determine buckets for 2nd triple pattern
Determine buckets that overlap in the join dimension (e.g., subject)
Estimate cardinality based on the degree of overlap
1st BGP
2nd BGP
2nd BGP
subject
object

Summary: querying Linked Open Data
Querying Linked Open Data (LOD)
Most natural way of looking up Linked Data
Keyword search for Linked Open Data
Several search engines available coming in diﬀerent ﬂavors
SPARQL query processing
Relies on powerful and available sources
Statistics require additional cooperation

References I
[1] Daniel J. Abadi, Adam Marcus, Samuel Madden, and Kate Hollenbach. SW-Store:
a vertically partitioned DBMS for Semantic Web data management. VLDB J.,
18(2):385–406, 2009.
[2] S¨oren Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak,
and Zachary G. Ives. DBpedia: A nucleus for a Web of open data. In ISWC, 2007.
[3] Christian Bizer. Topology of the Web of Data, 2012. Keynote LWDM 2012.
http://www.wiwiss.fu-berlin.de/en/institute/pwo/bizer/research/
publications/Bizer-TopologyWoD-LWDM2012-BEWEB2012.pdf?1361005355.
[4] Gianluca Demartini, Peter Mika, Thanh Tran, and Arjen P. de Vries. From Expert
Finding to Entity Search on the Web, 2012. Tutorial ECIR 2012.
http://diuf.unifr.ch/main/xi/EntitySearchTutorial.
[5] Katja Hose and Ralf Schenkel. WARP: Workload-Aware Replication and
Partitioning for RDF. In DESWEB’13, 2013.
[6] Jiewen Huang, Daniel J. Abadi, and Kun Ren. Scalable SPARQL Querying of Large
RDF Graphs. PVLDB, 4(11):1123–1134, 2011.

References II
[7] Thomas Neumann and Gerhard Weikum. RDF-3X: a RISC-style engine for RDF.
PVLDB, 1(1):647–659, 2008.
[8] Andreas Schwarte, Peter Haase, Katja Hose, Ralf Schenkel, and Michael Schmidt.
Fedx: Optimization techniques for federated query processing on linked data. In
ISWC 2011, pages 601–616, 2011.
[9] Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. Yago: a core of
semantic knowledge. In WWW, 2007.
[10] Metaweb Technologies. The freebase project. http://freebase.com.
[11] J¨urgen Umbrich, Katja Hose, Marcel Karnstedt, Andreas Harth, and Axel Polleres.
Comparing data summaries for processing live queries over linked data. World Wide
Web, 14:495–544, 2011.

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Searching the Web of Data (Tutorial)

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