Wissenstechnologie Vi 08 09

Wissenstechnologie WS 08/09

Michael Granitzer
IWM TU Graz & Know-Center
Know Center

Lecture 6: T iple Sto es Sparql,
Lect e 6 Triple Stores, Spa ql
Semantic Retrieval
http://kmi.tugraz.at
http://kmi tugraz at http://www.know-center.at
http://www know center at
This work is licensed under the Creative Commons Attribution 2.0 Austria License.
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Today

Ontology Modelling & SW Frameworks

Triple Stores
• Basic RDBMS scheme
• Property tables & vertical Partitioning
• Performance Comparisons

SPARQL
• Definition
• Simplex & Complex Queries
• Some examples on Endpoints

vs.
Information Retrieval vs Semantic Retrieval
• Basics of IR
• „Semantic“ Retrieval
• Practical Examples (Freebase, Cugil etc.)
2

WS 08/09 Wissenstechnologie @ kmi.tugraz.at

Ontology Modelling & SW
Frameworks

Ontology Modelling
Triple Stores

SPARQL

vs.
vs OOP Information Retrieval vs.
Semantic Retrieval

Similar to design in Object Oriented Programming
Classes, objects and members
Capture th operational properties
C t the ti l ti

public interface Course {
public void enroll()
bli id ll()
}

Ontology Modelling: Capture the structural properties

owl:participates owl:Course
owl:Student

3


Frameworks

Ontology Modelling
Triple Stores

SPARQL

vs.
vs RDBMS Information Retrieval vs.
Semantic Retrieval

Similar in designing a database system
Higher expressiveness in OWL Aggrement on the domain not
only referential integrity
Not focused on special indexing structures or on querying only
Ontologies should be application independent
Consistency checks
Semantic Integration via Ontologies

4
Product File System Employee Text ...
Database Database Database http://kmi.tugraz.at


Frameworks

Ontology Modelling
Triple Stores

SPARQL

Goals Information Retrieval vs.
Semantic Retrieval

Goals
Share common understanding among people or software
Enable reuse of knowledge
Make domain assumptions explicit
Separate domain knowledge from operational knowledge
Analyze domain knowledge
Main Application Areas
Semantic harmonization of heterogeneous data sources
Structuring the content of a portal
Enhance search and retrieval
5
…


Frameworks

Ontology Modelling
Triple Stores

SPARQL

Aspects to model Information Retrieval vs.
Semantic Retrieval

Defining classes in the ontology

Arranging classes in a taxonomy
g g y

Defining slots/properties for classes and their values

Define logical constraints on classes/properties

Assign instances

6


Frameworks

Ontology Modelling
Triple Stores

SPARQL

Three simple rules Information Retrieval vs.
Semantic Retrieval

1. There
1 “There is no one correct way to model a domain there
domain—
are always viable alternatives. The best solution almost
always depends on the application that you have in
mind and the extensions that you anticipate ”
anticipate.
2. “Concepts in the ontology should be close to objects
(physical or logical) and relationships in your domain of
interest. These are most likely to be nouns (objects) or
verbs (relationships) in sentences that describe your
domain.
domain ”
3. “Ontology development is necessarily an iterative
process
process”

7


Frameworks

Ontology Modelling
Triple Stores

SPARQL

Noy s
Noy‘s and McGunnise 7 Steps Information Retrieval vs.
Semantic Retrieval

1.
1 Determine the domain and scope of the ontology
2. Consider reusing existing ontologies
3 E
3. Enumerate i
t important terms in the ontology
t tt i th t l
4. Define the classes and the class hierarchy
5. Define the properties (slots) of classes
6. Define the facets of the slots
7. Create/Import instances

8


Frameworks

Semantic Web Frameworks
Triple Stores

SPARQL

Motivation Information Retrieval vs.
Semantic Retrieval

Protege as modelling GUI
For „Semantic Web Applications“ we want also to
A t
Automatically i
ti ll import/map i t
t/ instances
Manage large number of triples
Combine different schemas
Query for specific triples
Harmonize different metadata schemas
Database requirements for graphs
Reasoning
9


Frameworks

Triple Stores

SPARQL

Overview Information Retrieval vs.
Semantic Retrieval

Three „major Java based Open Source frameworks
„major“
Jena
Sesame
Protege Java API
Functionality
Java API for managing OWL, RDF and RDFS (optional DAML+OIL)
Import/Export of different formats
Persistence via own data store, different database and file system
backend
Querying, Graph manipulation and restricted reasoning capabilities
Web API
10


Frameworks

Triple Stores

SPARQL

Jena Architecture Information Retrieval vs.
Semantic Retrieval

SPARQL

RDF/XML

Jena: Implementing the
Semantic Web Recommendations – 2003
http://www.hpl.hp.com/techreports/2003/H
11
PL-2003-146.html


Frameworks

Triple Stores

SPARQL

Main Differences Information Retrieval vs.
Semantic Retrieval

Jena
Reference implementation
Not directly focused towards web access and scalability
Protege
Modelling GUI
Sesame
Focused towards remote access and scaleability
Flexible Layer architecture for different storage backends
Others: Virtuoso 3Store Kowari OpenAnzo
Virtuoso, 3Store, Kowari,

12


Frameworks

Triple Stores
Triple Stores

SPARQL

Overview
Information Retrieval vs.
Semantic Retrieval

Basic data model is RDF (i e OWL, RDFS)
(i.e. OWL
RDF forms an directed graph
How do we manage large graphs
In Memory Adjacency Matrix
On secondary storage

– Special Indices
– Use relational database management systems
13


Frameworks

Triple Stores Triple Stores

SPARQL

„Normalized Table Model of RDF
Normalized“
Semantic Retrieval

Subject Predicate Object

http://book.at/isbn123 author http://fussball.de/G. Müller

http://book.at/isbn123 price €15

http://book.at/isbn123 Title Ein Leben für die Tore

http://fussball.de/G. Müller Name Gerd Müller

author
http://book.at/isbn123 http://fussball.de/G. Müller

name
price title

14
Ein Leben für die Tore Gerd Müller
€15 http://kmi.tugraz.at


Triple Stores
Query in an unoptimized RDBMS

Select r3.o as Title from rdf
Query: Titles of books from the personwith name where
r1, rdf r2, rdf r3
Gerd Müller? r1.s = r2.o AND
R2.s = r3.s AND
r1.o = ‘Gerd Müller’ AND
Subject (s) Predicate Object (o)
r1.p = ‘Name’ AND
(p)
r2.p = ‘author’ AND
http://book.at/isbn123 author http://fussball.de/G. Müller R3.p = ‘Title’
p

http://book.at/isbn123 price €15

http://book.at/isbn123 Title Ein Leben für die Tore

http://fussball.de/G.
http://fussball de/G Name Gerd Müller
Müller

15


Frameworks

Triple Stores
Triple Stores

SPARQL

The Sesame Mapping as example
Semantic Retrieval

y
See Hak Soo Kim, Hyun Seok Cha, Jungsun Kim, Jin Hyun Son,, Development of the Efficient OWL Document Management
g y p g
16
System for the Embedded Applications, Springer 2005, http://www.springerlink.com/content/8mfxeh0glq5xj00m/


Frameworks


SPARQL

Indexing Techniques
Semantic Retrieval

Use specialised indices for graphs
Bitmap indices in Virtuoso
http://virtuoso.openlinksw.com/wiki/main/Main/VOSBitmapIndexing

Index different combinations of the S,P,O Table
P,S,O
O,P,S
O,S,P
S,O,P

17


Frameworks

Triple Stores
Triple Stores

SPARQL

A first Analysis
Semantic Retrieval

Normalised view on a graph: one large table
Generic and flexible, but
Large self j i t f rather simple queries. RDBMS are
L lf joints for th i l i
usually not optimized for this
Large memory overhead in query processing due to
self joints
Requires lot of index lookups and/or full table scans
Large storage overhead
In
I general: fl ibilit vs. performance
l flexibility f
How to improve? 18


Frameworks


SPARQL

Further improvements
Semantic Retrieval

p y p y g
Property tables: flattened representation by finding
sets of properties which are used together
Subject-Property Matrix Materialized Join Views
(SPMJVs) from Oracle
Ch
Chong, E. I., Das, S., E d
E I D S Eadon, G and S i i
G., J. 2005 A ffi i t SQL b d
d Srinivasan, J 2005. An efficient SQL-based
RDF querying scheme. In Proceedings of the 31st international Conference on
Very Large Data Bases, ACM

19
Abadi, D. J., Marcus, A., Madden, S. R., and Hollenbach, K. 2007. Scalable semantic web data management using vertical partitioning. In Proceedings of the 33rd
international Conference on Very Large Data Bases (Vienna, Austria, September 23 - 27, 2007). Very Large Data Bases. VLDB Endowment, 411-422. http://kmi.tugraz.at


Frameworks


SPARQL

Property Tables
Semantic Retrieval

++: Faster querying within a property tables due to reducing
subject-subject self joins
--: Requires intelligent selection of the properties in the table
More property colums lead to more null values in the table
and therefore to larger space overhead
Lesser property colums lead to more property tables
more joins over lesser property tables
--: Multi valued properties are hard to manage (e.g. a book has
: (e g
several authors)

Subject Title Author Year
ID1 “Intro to RDF” Granitzer 2006
ID1 “Intro to RDF” Tochtermann 2006 20


Frameworks


SPARQL

Vertical Partitioning
Semantic Retrieval

Partition database according to properties – one table per
property
Abadi, D. J., Marcus, A., Madden, S. R., and Hollenbach, K. 2007. Scalable semantic web data management using vertical partitioning. In Proceedings of the
33rd international Conference on Very Large Data Bases (Vienna, Austria, September 23 - 27, 2007). Very Large Data Bases. VLDB Endowment, 411-422.

Tables are sorted by subject allows fast merge sort joins

21


Frameworks


SPARQL

Vertical Partitioning
Semantic Retrieval

++: Use of simple fast merge joints
simple,
++: Multi valued attributes are supported
++: No a-priori clustering decision is necessary
a priori
++: Smaller tables. Only those properties accessed have to be
read from disk
--: Insert may be slower due to access to multiple tables
--: Queries over multiple properties span over multiple tables

22


Frameworks

Triple Store
Triple Stores

SPARQL

Performance of Open Source Solutions
Semantic Retrieval

Portwin & Parvatikar (2006) Scaling Jena in a Commercial Environment:
The Ingenta MetaStore Project
LEGHIGH Dataset with domain universities
~200 million triples, 11 Millionen OWL Statements, 4.3 millionen
documents
Kowari: 1 billion triple, load 20k Triple/s for Wikipedia data set

Unoptimized
Simple query take milliseconds
With inference queries take several seconds to minutes
depending on the complexity
Optimization for Inference: for RDFS entailment is to
expand the graph by making implicit edges explicit
more storage but faster access 23


Frameworks

Triple Store Triple Stores

SPARQL

Performance of Oracle
Semantic Retrieval

BioMed literature database (UniProt data set)
80 million triples
5 d t ( 25 T i l 17 M i
~5 GB RDF/XML data (~2,5 GB Triple; 1,7 GB Mapping;
4,8 GB Indices)
Queries take milliseconds to secondes
Subject-property matrix materialized views provide
optimization potential of roughly ~30%
30%
Chong, E. I., Das, S., Eadon, G., and Srinivasan, J. 2005. An efficient SQL-based RDF querying scheme. In
Proceedings of the 31st international Conference on Very Large Data Bases, ACM

24


Frameworks

Triple Store
Triple Stores

SPARQL

Performance Summary
Semantic Retrieval

http://esw.w3.org/topic/LargeTripleStores
Problem: Comparison among performance numbers available
Trade-off Generic vs Performance
Trade off vs.
Optimization potential is available
Currently not as fast as specialised RDBMS but more flexible
RDBMS,

25


Frameworks

SPARQL
Triple Stores

SPARQL

SPARQL Protocol and RDF Query Language Information Retrieval vs.
Semantic Retrieval

Different languages similar to SQL in RDBMS
SerQL, RDF, SPARQL
SPARQL currently proposed recommendation of the W3C
But what does querying a graph mean?
Basically
Specify a sub-graph with variable nodes
Find all patterns in the graph matching the sub-graph

? author
Gerd Müller Select ?x, ?y
where ?x <author> “Gerd Müller”.
title ?x <title> ?y.
?y

? 26


Frameworks

SPARQL
Triple Stores

SPARQL

Example Information Retrieval vs.
Semantic Retrieval

Daten:
http://example.org/book/book1
http://purl.org/dc/elements/1.1/title
quot;SPARQL Tutorialquot; .

Abfrage:
SELECT ?title
WHERE {
<http://example.org/book/book1>
<http://purl.org/dc/elements/1.1/title>
?title .
}

Ergebnis:
title
quot;SPARQL Tutorialquot;

27


Frameworks

SPARQL
Triple Stores

SPARQL

Example Information Retrieval vs.
Semantic Retrieval

Data:
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
_:a foaf:name quot;Johnny Lee Outlawquot; .
_:a foaf:mbox <mailto:jlow@example.com> .
_:b foaf:name quot;Peter Goodguyquot; .
:b
_:b foaf:mbox <mailto:peter@example org> .
<mailto:peter@example.org>
Query:
PREFIX foaf: http://xmlns.com/foaf/0.1/
SELECT ?name ?mbox
WHERE {
?x foaf:name ?name .
?x foaf:mbox ?mbox
}
Result:
Res lt
name mbox
quot;Johnny Lee Outlawquot; mailto:jlow@example.com
quot;Peter Goodguyquot; <mailto:peter@example.org>

28


Frameworks

SPARQL
Triple Stores

SPARQL

Simple Query Elements Information Retrieval vs.
Semantic Retrieval

Determine the Namespace: PREFIX
Determine the return format
SELECT: Table output format similar to SQL Results
CONSTRUCT: Allows to construct a graph as return value
ASK: Returns only true/false depending of the result exists or not
DESCRIBE: return possible properties/ressources for a particular
query. Used for browsing.
Specify the selection criteria with the WHERE Clause
Specify a non-recursive sub-pattern with triples and placeholders (?
Or $)
Perform Grouping and Filter Operations
Modifiers: ORDER BY, LIMIT, OFFSET, DISTINCT
29


Frameworks

SPARQL
Triple Stores

SPARQL

Blank Nodes Information Retrieval vs.
Semantic Retrieval

ID of Blank Nodes is unique within one query and indicate only the
existence of a blank node not it‘s absolute value
Blank nodes are identified by an automatically generated URI
Consider the results of a query

≡ ≠
Subject Value Subject Value Subject Value
_:a
a “zum”
“ m” _:x “zum”
“ ” _:z “zum”
_:b “Beispiel” _:y “Beispiel” _:z “Beispiel”

Blank nodes may be renamed and are structural elements only

30


Frameworks

SPARQL
Triple Stores

SPARQL

Complex Queries Information Retrieval vs.
Semantic Retrieval

Combination of groups of simple graph expressions in the WHERE
clause
OPTIONAL clause: Subgraph pattern may not exist
Example for querying book titles from Springer
if an author exists, it will be listed if not the title is returned
without a author

SELECT ?title ?author
WHERE
{ ?buch ex:pulishedFrom http://springer.com/Verlag .
p p // p g / g
? Buch ex:Title ?title .
OPTIONAL {?buch ex:Autor ?author }.
}

31


Frameworks

SPARQL
Triple Stores

SPARQL

Semantic Retrieval

Specifying alternative sub graph patterns: UNION
Logical OR or union of two separat queries

WHERE
{?buch
{?b h ex:Autor ? th .} UNION
A t ?author }
{?buch ex:Creator ?author .}
}

„Select all books with a title published by Springer which have an
author or an creator assigned“
Note: ?author in the different groups are independent of each other

32


Frameworks

SPARQL
Triple Stores

SPARQL

Semantic Retrieval

Considering special datatypes: FILTER and XML Datatypes
Specify the data type of a literal
WHERE
„1998 xsd:integer
?buch ex:publishedIn „1998“^^xsd:integer
}
FILTER specifies boolean expressions for filtering results
E.g. Specify the data type range using FILTER
(see Chapter 7 in Semantic Web Grundlagen)

WHERE
?buch ex:publishedIn ?year .
FILTER(?year >2000) 33
}


Frameworks

SPARQL
Triple Stores

SPARQL

Real World Examples in DBPedia Information Retrieval vs.
Semantic Retrieval

PREFIX p: <http://dbpedia.org/property/>
PREFIX rdf: <http://www.w3.org/1999/02/22 rdf syntax ns#>
<http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT * WHERE {
?album p:artist ?band.
?album rdf:type <http://dbpedia.org/class/yago/Album106591815>.
OPTIONAL {?album p:cover ?cover}.
OPTIONAL {?album p:name ?name}.
{ p } PREFIX p: <http://dbpedia.org/property/>
OPTIONAL {?album p:released ?dateofrelease}. PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
CONSTRUCT {
} ORDER BY DESC(?name) LIMIT 20 OFFSET 19
?album p:itIsDone ?dateofrelease .
?band p:isBand quot;truequot; .
} WHERE {
?album p:artist ?band.
?band
?album rdf:type <http://dbpedia.org/class/yago/Album106591815>.
OPTIONAL {?album p:cover ?cover}.
OPTIONAL {?album p:name ?name}.
OPTIONAL {?album p:released ?dateofrelease}.
} ORDER BY ?name LIMIT 20 OFFSET 19

PREFIX p: <http://dbpedia.org/property/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
DESCRIBE ?album
WHERE {
?album p:artist
<http://dbpedia.org/resource/The_Allman_Brothers_Band>.
?album rdf:type <http://dbpedia.org/class/yago/Album106591815>. 34
}


Frameworks

SPARQL
Triple Stores

SPARQL

Summary Information Retrieval vs.
Semantic Retrieval

Similar to SQL
Allows easier expression of joins without knowing the
underlying database schema
y g
Allows to return not only tables, but also more complexe
output formats like graphs etc.
Datatypes of a variable not always clear

http://www.w3.org/TR/rdf-sparql-query/
http://thefigtrees.net/lee/sw/sparql-faq
htt //th fi t t/l / / l f
Hitzler, Krötsch, Rudolph, Sure, Semantic Web – 35
Grundlagen,
Grundlagen Chapter 7


Frameworks

Semantic vs. Information Retrieval
Triple Stores

SPARQL

Overview Information Retrieval vs.
Semantic Retrieval

Central Question: What is semantic retrieval?
Define information retrieval
Wh
Where i semantic missing?
is ti i i ?
How can we use Semantic Web technologie to increase
semantic?

36


Frameworks

Triple Stores

SPARQL

Definition of IR Information Retrieval vs.
Semantic Retrieval

Salton (1968): „
( ) „Information retrieval is a field concerned with the
structure, analysis, organization, storage, searching, and retrieval of
information.“

“Information retrieval ( ) i fi di
“ f i i l (IR) is finding material (
i l (usually d
ll documents) of an
) f
unstructured nature (usually text) that satisfies an information need
from within large collections (usually stored on computers). “
Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval,
Cambridge University Press 2008
Press.

Main focus of IR is how to deal with uncertainty and incomplete
information
Representation of documents is ambiguous
Query formulation is ambiguous and usually incomplete
“Unstructured” information
Usually the perfect answer, so far a perfect answer exists, can not be
delivered
37


Frameworks

Triple Stores

SPARQL

IR vs Data Retrieval from Rijsbergen 1979
vs. Information Retrieval vs.
Semantic Retrieval

Data retrieval Information retrieval

Matching Exact match Partial (best) match

Inference Deduction
Ded ction Induction
Ind ction

Model Deterministic Probabilistic

Classification Monothetic Polythetic

Query language Artificial Natural

Query specification Complete Incomplete

Items wanted Matching
g Relevant

Error response Sensitive Insensitive
38


Frameworks

Triple Stores

SPARQL

Semantic Retrieval

“What is the Gross domestic product of Austria?
What Austria?”

39


Frameworks

Triple Stores

SPARQL

Semantic Retrieval

“What is the Gross domestic product of Austria?
What Austria?”
Select GDP from GDP_table where country_name=“Austria”
€ 270.8 bn
However,
not all information is available in databases
Queries are hard to formulate for the average users as well
as for non domain experts
The l th d i
Th more complex the domain and the information need,
d th i f ti d
the harder to formulate a correct query

40


Frameworks

Triple Stores

SPARQL

Basic Retrieval Workflow Information Retrieval vs.
Semantic Retrieval

Retrieval
Documents Document Representation Model M
D Dr

Ranking
Function R
Information Need Query
IN Q

41
See also Baeza Yates & Ribeiro Neto, (1999),“Modern Information Retrieval”


Frameworks

Triple Stores

SPARQL

The Vector Space Model Information Retrieval vs.
Semantic Retrieval

Document Representation Dr: Documents are represented as
bag-of-words (i.e. a set of words)
Query Q: Query is a set of keywords
Retrieval Model M:
Set of words are converted to vectors d and q
Use different heurisitc to calculate the importance of a word
Ranking Function R:
Cosine Si il it C l l t th angle b t
C i Similarity: Calculate the l between d and q
d

d1:= “Boy p y chess”
y plays
d2:= “Boy plays bridge”
42


Frameworks

Triple Stores

SPARQL

An analysis of the vector space model Information Retrieval vs.
Semantic Retrieval

Query and documents are represented in terms of their words
Importance of words depend on their occurrence
Syntactic matching between documents and queries
y g q
No synonyms are considered (e.g. Money == Cash)
No homonyms are considered (e.g. Apache Web Server)
No mereonyms are considered (e.g. tire is part of a car)
No relationships between terms are considered

43


Frameworks

Triple Stores

SPARQL

So where can we include semantic? Information Retrieval vs.
Semantic Retrieval

p q y
Increase the semantic of the document representation Dr and the query
Q
Add metadata (e.g. tags, dublin core etc.)
Use more sophisticated preprocessing (e g language models word
(e.g. models,
sense disambiguation)
Allow users to express information needs in more detail or estimate
the context of a user (e.g. specify metadata, profiling)
Formal representation of DR and Q using semantic web languages
like OWL
see Tran, Bloehdorn, Cimiano, Haase (2007), „Expressive Ressource Description for Ontology-Based
Information Retrieval“
Retrieval

However, if we have a perfect formal representation we still need to
transform natural language queries to this model for the average user
Requires a special user interface – not possible for the generic case
Natural Language Understanding – currently unsolved
44


Frameworks

Triple Stores

SPARQL

Semantic Retrieval

p
Document representation

C:myDocument.doc

Ex:containsConcept
Ex:Concept

http://en.wikipedia.org/wiki/ApacheWebServer
http://en wikipedia org/wiki/ApacheWebServer rdf:type

ex:term

„ Apache“ „ Apache Server“ „ Apache Web Server“

Query
Select * WHERE {?x ex:contains
http://en.wikipedia.org/wiki/ApacheWebServer} 45


Frameworks

Triple Stores

SPARQL

Semantic Retrieval

Iterative refinement of the information need:
Keyword Query: „Apache“

http://en.wikipedia.org/wiki/ApacheWebServer
http://en.wikipedia.org/wiki/ApacheHelicopter
ttp //e ped a o g/ / pac e e copte
http://en.wikipedia.org/wiki/AmericanNatives
Select * WHERE {?x ex:containsConcept http://en.wikipedia.org/wiki/ApacheWebServer}

C:myDocument.doc C:myDocument2.doc

Ex:containsConcept Ex:containsConcept

p p g p
http://en.wikipedia.org/wiki/ApacheWebServer http://en.wikipedia.org/wiki/ApacheTribes

ex:term
46
„ Apache“ „ Apache Server“ „ Apache Web Server“ http://kmi.tugraz.at


Frameworks

Triple Stores

SPARQL

Semantic Retrieval

Increase the cabability of the retrieval model M and ranking
function R
Latent Semantic Indexing/Concept Indexing
Automatically determine the concepts contain in a
document set
Include a-priori knowledge (e.g. Thesaurus, Word Net)
Learn ranking functions based on a users feedback (e.g. via
machine learning)
Use formal knowledge in form of ontologies and reasoning
capabilities

47


Frameworks

Triple Stores

SPARQL

Semantic Retrieval

Example
Vector Space Model

– D={Apache=0.8, http=0.5, server=0.3}
D {Apache 0.8, http 0.5, server 0.3}
– Q={Jetty=0.8, java=0.7, web=0.4}
– Ranking Value=0.0

Introduce a „better“ retrieval model by using a domain
ontology:

„Apache“ and „Jetty“
Ex:WebServer
can be related to each
ex:isA other using the domain
Ex:ApacheServer Ex:JettyServer ontology
ex:term ex:term Ranking Value > 0 48
„ Apache Web Server“ „ Apache“ „ Jetty Server“ „ Jetty“ http://kmi.tugraz.at


Frameworks

Triple Stores

SPARQL

Semantic Retrieval

Improve the presentation of results
Clustering of search results
Display different facets of the result set
p y
Different representation of results
Display facts instead of documents
Supports refining the user to define their information need
Search as iterative approach

49


Frameworks

Triple Stores

SPARQL

Semantic Retrieval

50


Frameworks

Triple Stores

SPARQL

Dimensions of Semantic Information Retrieval vs.
Semantic Retrieval

Semantically structured vs. unstructured document and query
representation
Semantic expressiveness increases with increased structure
Queries are hard to formalize. Support for the average user
is required
Labour intensive creation of the document representation
Extension of the retrieval model
Runtime complexity of reasoning in case of semanticlly
p y g y
structured Dr and Q
Scaleability (also an issue for more complex statistical
methods)

51


Frameworks

Triple Stores

SPARQL

Dimensions of Semantic Information Retrieval vs.
Semantic Retrieval

p
Semantic enhancement of result presentation
„Low hanging fruit“
Does not require a formalized knowledge base
Formalized knowledge vs. statistical approaches
Bottom Up vs. Top Down
Statistical approaches can provide sophisticated retrieval model
model,
which do not require formalized, modelled knowledge
Statistical approaches depend on the fact that all required
information is within the data set and can be extracted
Statistical models are usually not shareable between systems
Knowledge base may not model all facts contained in the data set
(
(e.g. W dN t d
WordNet does k thi
know nothing about the Apache Web Server)
b t th A h W bS )

52


Frameworks

Triple Stores

SPARQL

Example Freebase www freebase com
Freebase, www.freebase.com Information Retrieval vs.
Semantic Retrieval

Open database of the worlds information
Contribution by the community
Linked with other free resources like Wikipedia
p
Web API
Own Query Language: Metaweb Query Language
Regarding semantic search
Structural document representation
Keyword queries in combination with intelligent interfaces
for infromation need refinement
Fact based representation

53


Frameworks

Triple Stores

SPARQL

Example Freebase Information Retrieval vs.
Semantic Retrieval

54


Frameworks

Triple Stores

SPARQL

Example Cuil www cuil com
Cuil, www.cuil.com Information Retrieval vs.
Semantic Retrieval

Internet search engine with 120 billion pages

Not based on popularity of sites, just on content and topics of
p p y ,j p
content

Document representation and query is unstructured

Extended Retrievalmodel i th b k
E t d dR t i l d l in the background b
d based on
d
categorical knowledge and statistical methods (Clustering)
http://www.news.com.au/technology/story/0,25642,24089734-5014239,00.html

Enhanced interface
55


Frameworks

Triple Stores

SPARQL

Example Yahoo! Search Monkey Information Retrieval vs.
Semantic Retrieval

Use structured data to improve presentation of search results
http://developer.yahoo.com/searchmonkey/#

56


Summary

Triple Store
Generic /Flexibility vs. Performance/Space
Usually RDBMS with a large triple table
Alternative: special graph indexing structures
SPARQL
Simple query for RDF by providing a graph pattern
Recommended by W3c
Semantic Search
Document & query representation
Retrieval model & Interfaces
Semantic vs. Statistic
57
It is the next step, but not such a big one


Next Week

Guest Lectures (Anwesenheitspflicht) from experts
Werner Klieber: Semantic Web Services (30‘)
A tif L tif Open Linked Data (30‘)
Aatif Latif: O Li k d D t
Fleur Jeanquartier: Bringing the Semantic Web closer
to the User (30‘)
(30 )

58


That‘s it for today…

Thanks for your attention

Questions/comments?

mgranitzer@tugraz.at

59


License

This work is licensed under the Creative Commons
Attribution 2.0 Austria License.
To view a copy of this license, visit
http://creativecommons org/licenses/by/2 0/at/
http://creativecommons.org/licenses/by/2.0/at/.

Contributors:
Mathias Lux
Peter Scheir
Klaus Tochtermann
Michael Granitzer
60


Wissenstechnologie Vi 08 09

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