Reasoning in the Semantic Web Framework

1
Reasoning in the Semantic Web Framework
2
Reasoning in the Semantic Web Framework

• Logic layer Outline
• Inference Outline
• Example from Query- and Reasoning-Engines(SiLRI)
  A Query and Inference Service for RDF  
• News from W3C
• References

SiLRI simple Logic-based RDF Interpreter (a
pure java-based language)
3
Reasoning in the Semantic Web Framework

• Logic Layer in Berners_Lees Architecture

• Logic Layer Outline
• Inference Outline
• Example from Query- and Reasoning-Engines
  (SiLRI)  
• News from W3C
• References

4
Reasoning in the Semantic Web Framework

• Some important points for Logic Layer
• Any rule system can export, generally cannot
  import
• No one standard engine - inference capabilities
  differ
• Many engines exist ( e.g. HOL for High-Order
  Logic , and SPASS for First-Order Logic, etc)
• Any system can validate proofs

• Logic Layer Outline
• Inference Outline
• Example from Query- and Reasoning-Engines
  (SiLRI)  
• News from W3C
• References

5
Reasoning in the Semantic Web Framework

• Logic layer Outline
• Inference Outline
• Example from Query- and Reasoning-Engines(SiLRI)
  A Query and Inference Service for RDF  
• News from W3C
• References

SiLRI simple Logic-based RDF Interpreter
6

• Logic Layer Outline
• Inference Outline
• General Logic based IE
• First-Order Logic based IE
• Higher-Order Logic based IE
• Problem Solving Methods
• Example from Query- and Reasoning-Engines
  (SiLRI)  
• News from W3C
• References

Reasoning in the Semantic Web Framework

• Inference Outline two Approaches for
• Inference Engines

Inference Engines
Specialized Algorithms (Problem Solving Methods)
General Logic Based Inference Engines
First-Order Logic based IE
Higher-Order Logic based IE
First-Order Syntax
First Order Semantics
Higher-Order Semantics
Higher-Order Syntax
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Inference Engines
Predicate logic syntax and semantics are both
first-order. Jens is nice. Fred is nice
Syntax Variable x,y Constants (functions of
arity 0) a,b predicate symbol P(x,y) ? first
order syntax. Semantics Basic set UA Jens,
Fred, Tom , ... Structur A(UA, IA)(Jens,
Fred, Tom, IA) Interpretation PA x x UA
and x is nice QA x x UA and x is
not nice RA (x,y) x,y UA and x
and y are both nice aA Jens, bA
Fred x (P(x) Q(x) R(x,a)) x (
P(x) Q(x) R(x,b)) x P(x) Q(x)
? first order semantics.

• General Logic based IE
• First-Order Logic
• Predicate Logic
• Description Logic
• Horn Logic
• SPASS
• Higher-Order Logic
• Problem Solving Methods

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Inference Engines

• General Logic based IE
• First-Order Logic
• Predicate Logic
• DescriptionLogic
• Horn Logic
• SPASS
• Higher-Order Logic
• Problem Solving Methods

• Description Logic
• Allows for the specifcation of a terminological
  hierarchy using a restricted set of first order
  formulas.
• nice computational properties
• - the inference services are restricted to
  subsumption and classification

9
Inference Engines

• General Logic based IE
• First-Order Logic
• Predicate Logic
• DescriptionLogic
• Horn Logic
• SPASS
• Higher-Order Logic
• Problem Solving Methods

• Horn-logic
• Def. a formula is called horn formula, if F in
  CNF (conjunctive normal form) and each dis-
  junction element in F contains at most one
  positive literal .
• Example
• (A v B v C) ? ( D v E ) ? F is horn
• (A v B v C) ? ( D v E ) ? F not horn
• SiLRI as an example of base systems , a pure
  Java-
• based system.

10
Inference Engines

• Available automated First Order Theorem Provers
  are SPASS, etc.
• Input for the prover is a first-order formula
• If valid(formular), a Proof will be found
• If notValid(formular), Prover run forever without
  any final result
• Problem
• Adam is a human.
• All humans are mortal.
• gt (3) Adam is mortal.

• General Logic based IE
• First-Order Logic
• Predicate Logic
• DescriptionLogic
• Horn Logic
• SPASS
• Higher-Order Logic
• Problem Solving Methods

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Inference Engines

• First step ( the Problem -gt First-Order Logic
  Formulae)
• Human(Adam)
• x Human(x) Mortal(x)
• Mortal(Adam)
• Second step ( First-Order Logic Formulae in
  Syntax-gt Input File for SPASS )
• Used Syntax from
• Common Syntax of the DFG-Schwerpunktprogramm
  Deduktion Version1.3 Reiner Hähnle, Fakultät
  für Informatik, Universität Karlsruhe
• Manfred Kerber, School of Computer Science, The
  University of Birmingham, England Christoph
  Weidenbach, Max-Planck-Institut für Informatik,
  Im Stadtwald, Saarbrücken

• General Logic based IE
• First-Order Logic
• Predicate Logic
• Description Logic
• Horn Logic
• SPASS
• Higher-Order Logic
• Problem Solving Methods

12
Inference Engines
Second step( Inputfile) begin_problem(Adam1).
list_of_descriptions. name(firstSPASSExample
). author(Adam). status(unsatisfiabl
e). description( Adam is mortal and since
all humans are mortal, he is mortal too. ).
end_of_list. list_of_symbols.
functions(Adam,0). predicates(Human,1),(Mor
tal,1). end_of_list. list_of_formulae(axioms)
. formula(Human(Adam),1).
formula(forall(x,implies(Human(x),Mortal(x))),2)
. end_of_list. list_of_formulae(conjectures).
formula(Mortal(Adam),3). end_of_list. end_pr
oblem.

• General Logic based IE
• First-Order Logic
• Predicate Logic
• DescriptionLogic
• Horn Logic
• SPASS
• Higher-Order Logic
• Problem Solving Methods

13
Inference Engines

• Second step (Syntax)
• Used Syntax (1)
• optional arbitrarily often at
  least once
• symbol_list list_of_symbols. 
• functionsfun_sym(fun_sym,arity)
  fun_sym(fun_sym,arity).
•   predicatespred_sym (pred_sym,arity)
•   , pred_sym (pred_sym,arity) .
•    ...
•  end_of_list.

• General Logic based IE
• First-Order Logic
• Predicate Logic
• Description Logic
• Horn Logic
• SPASS
• Higher-Order Logic
• Problem Solving Methods

problem begin_problem(identifier). description
logical_part settings end_problem.
logical_part declaration_list symbol_list
formula_list ...
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Inference Engines

• Second step (Syntax)
• Used Syntax (2)
• formula_list list_of_formulae(origin_type).
• formula(term,label).
•   end_of_list.
• origin_type axioms conjectures
• Label identifier
• termquant_sym(term_list,term)symbol
  symbol(term,term)
• term_list term,term
• quant_sym forall exists identifier
• Symbol equal true false or and not
  implies implied equiv identifier
• More details at
• http//spass.mpi-sb.mpg.de/webspass/help/syntax/in
  dex.html

• General Logic based IE
• First-Order Logic
• Predicate Logic
• Description Logic
• Horn Logic
• SPASS
• Higher-Order Logic
• Problem Solving Methods

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Inference Engines

• General Logic based IE
• First-Order Logic
• Predicate Logic
• DescriptionLogic
• Horn Logic
• SPASS
• Higher-Order Logic
• Problem Solving Methods

1. Input file gt WebSPASS interface. http//spass
.mpi-sb.mpg.de/webspass/index.html 2. With the
option DocProof gt the output also contains
the proof
16
Inference Engines

• Higher-Order Syntax (predicates applied to
  predicates)
• Definition
• Example
• Bush and Putin have some common interest ? ?i
  (i(Bu) ? i(Pu))
• Higher-Order Semantics
• Example
• Predicates(functions)
• equal (p,q) is true, if and only if these
  symbols are
• interpreted via the same relation
• (function).

• General Logic based IE
• First-Order Logic
• Higher-Order Logic
• Syntax and Semantics
• HOL
• Problem Solving Methods

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Inference Engines

• General Logic based IE
• First-Order Logic
• Higher-Order Logic
• Syntax and Semantics
• HOL
• Problem Solving Methods

• HOL (an available Inference Engines for Higher
  Order Logics)
• What can HOL do?
• Supports interactive theorem proving in
  High-Order Logic
• Interfaced to ML(a build-in meta-language for
  manipolating the theorem prover and HOL terms)
• The primary application area of HOL
• One of th most widely used systems worldwide
• Is available without charge

18
Inference Engines

• General Logic based IE
• First-Order Logic
• Higher-Order Logic
• Syntax and Semantics
• HOL
• Problem Solving Methods

• HOL (an available Inference Engines for Higher
  Order Logics)
• Who is the Deviser?
• HOL(1980s) Automated Reasoning Group, the
  Computer Laboratory,Uni. Of Cambridge (UK)
• HOL used to Higher Order Logic Keith Hanna, Uni.
  Of Kent at Kanterbury
• LCF(Logic for Computable Functions) (1970s)
  Robin Milner, Automated Reasoning Group, the
  Computer Laboratory, Uni. Of Cambridge(UK)

19
Inference Engines

• General Logic based IE
• First-Order Logic
• Higher-Order Logic
• Syntax and Semantics
• HOL
• Problem Solving Methods

• HOL an available Inference Engines for Higher
  Order Logics
• Three Versions( HOL98, HOL90, and HOL88 )

20
Inference Engines

• General Logic based IE
• First-Order Logic
• Higher-Order Logic
• Syntax and Semantics
• HOL
• Problem Solving Methods

• HOL (an available Inference Engines for Higher
  Order Logics)
• Principles of HOL System
• What is a theory
• The purpose of the HOL system
• Theorem of theories
• Only well-formed theories
• New theories can be extended from initial HOL
  theories
• How can it be used in Semantic Web
  http//www.ftp.cl.cam.ac.uk/ftp/hvg/hol98/taupo-6-
  description.pdf (227 pages) or
• in the book Introduction to HOL (CUP, 1993).

21
Inference Engines

• How HOL system runs with emacs
• Starting HOL
• from within the Emacs editor.
• Download hol-emacs package and install it.
• Edit/create .emacs file to know about HOL
• Create init.ml to initialize for HOL-Emacs
• Start emacs by typing emacs
• Edit myfirst.ml
• In emacs, M-x run-hol to start HOL. (see
  HOL-Emacs manual in the web site
  http//lal.cs.byu.edu/lal/hol-emacs/hol-emacs.html
  )
• The HOL expression(34 ) in interaction buffer
  or in a text file
• Tell emacs to communicate the expression to
  HOL(C-c n or C-c p))
• HOL send the results in the interaction buffer
• OK..
• goal proved

• General Logic based IE
• First-Order Logic
• Higher-Order Logic
• Syntax and Semantics
• HOL
• Problem Solving Methods

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Inference Engines

• General Logic based IE
• First-Order Logic
• Higher-Order Logic
• Syntax and Semantics
• HOL
• Problem Solving Methods

• How HOL system runs with emacs
• Starting HOL
• Interacting with HOL interaching with ML
  interpreter
• ML Overview
• http//lal.cs.byu.edu/lal/holtut/ml-overview.htm
  l

23

• Logic Layer Outline
• Inference Outline
• General Logic based IE
• Problem Solving Methods
• Example from Query- and Reasoning-Engines   
• News from W3C
• References

Inference Engines

• Problem Solving Methods (PSMs)
• small algorithms
• Perform inferences within expert systems
• Specify inference actions and define the data-
  and control flow between subtasks
• Every new task needs a new PSM

24
Reasoning in the Semantic Web Framework

• Logic layer Outline
• Inference Outline
• Example from Query- and Reasoning
  -Engines(SiLRI) A Query and Inference
  Service for RDF  
• News from W3C
• References

SiLRI simple Logic-based RDF Interpreter
25
Example from Query- and Reasoning-Engines(SiLRI)
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

26
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

• Introduction
• isomorphic representations
• 3-Tuples (Triples)
• Acyclic Directed Labeled Graph
• XML Transfer Encoding

27

• Introduction
• Triples
• Acyclic Directed Labeled Graph
• XML Transfer Encoding
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

A Query and Inference Service for RDF

• 3-Tuples (Triples)
• language, http//foo.com/Welcome.html, en
• language, http//foo.com/Bienvenue.html, fr
• variant, http//foo.com/Welcome.html,
  http//foo.com/Bienvenue.html

28

• Introduction
• Triples
• Acyclic Directed Labeled Graph
• XML Transfer Encoding
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

A Query and Inference Service for RDF
Acyclic Directed Labeled Graph
http//www.foo.com/Welcome.html
http//www.foo.com/Bienvenue.html
variant
language
language
en
fr
29

• Introduction
• Triples
• Acyclic Directed Labeled Graph
• XML Transfer Encoding
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

A Query and Inference Service for RDF
ltrdfRDF xmlnsrdf"http//www.w3.org/TR/WD-rdf
-syntax" xmlnsa"http//www.sche
ma.org/usefulpredicates/"gt ltrdfDescription
about"http//www.foo.com/Welcome.html"gt
ltalanguagegtenlt/alanguagegt ltavariant
rdfresource"http//www.foo.com/Bienvenue.
html" /gt lt/rdfDescriptiongt
ltrdfDescription about"http//www.foo.com/Bienven
ue.html"gt ltalanguagegtfrlt/alanguagegt
lt/rdfDescriptiongt lt/rdfRDFgt
XML Transfer Encoding
30

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

A Query and Inference Service for RDF

• Introductions
• A knowledge representation format not enough to
  enable all users to process RDF effectively
• Standard quary language and tools should be
  enable the creation of RDF-aware applications
• A query language and system for XML also be
  applicable to RDF
• a query language based on XML not easily cope
  with aggregation
• RDF introduces several alternative ways

31
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

• Requirements
• support the data model of RDF (resources,
  properties, values)
• accessibile to the Scheme definitions for the
  vocabularies used in any given block of RDF data.
• defined in terms of the abstract model, with the
  syntactic representation(s) a secondary concern
• the RDFS specification includes features which
  require basic inferencing facilities in the
  storage/query system
• two other inference tasks
• RDF data can be checked for consistency against
  any contraint resources
• the Scheme itself can be used to derive new
  information

32
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

• RDF and Logic-based Languages
• Triple(statements) as the equivalent of ground
  facts in a logic based language
• Web applications require a fairly efficient
  implementation
• To specify complex integrity constraints requires
  the careful selection of an appropriate semantics
  
• A semantic dealing with Negation
• A semantic dealing with non-stratified semantics
• RDF inference engine should be easily usable in
  the Web
• RDF syntax is verbose for some concrete
  representation of the query language, so F-Logic
• Some examples between RDF/RDFS and F-logic

33
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

• RDF and Logic-based Languages
• Simplest primitive notations for Frame-Logic
• RDF and corresponding Frame-logic expression
• RDFS and corresponding Frame-logic expression
• Complex RDF and corresponding Frame-logic
  expression
• Complex Frame-logic query

34
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

• RDF and Logic-based Languages
• Simplest primitive notations for Frame-Logic

A
A
35

• RDF and Logic-based Languages
• RDF and corresponding Frame-logic expression

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

The creator of the resource
http//www11.in.tum.de/lehrstuhl/personen/schlich
ter is Schlichter ltrdfRDFgt
ltrdfDescription rdfabout"http//www11.in.tum.de
/ lehrstuhl/personen/schlichter"gt
ltdcCreatorgtSchlichterlt/dcCreatorgt
lt/rdfDescriptiongt lt/rdfRDFgt Representation in
F-logic http//www11.in.tum.de/lehrstuhl/perso
nen/schlichter Creator-gtgtSchlichter"
36

• RDF and Logic-based Languages
• RDFS and corresponding Frame-logic expression

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

ltrdfRDF ltrdfsClass rdfsID"Employee"gt
ltrdfssubClassOf rdfresource"http//ontology.org
/human-ontologyPerson"/gt lt/rdfsClassgt
ltrdfsClass rdfsID"Researcher"gt
ltrdfssubClassOf rdfresource"Employee"/gt
lt/rdfsClassgt ltrdfProperty ID"cooperatesWith"gt
ltrdfsdomain rdfresource"Researcher"/gt
ltrdfsrange rdfresource"Researcher"/gt
lt/rdfPropertygt lt/rdfRDFgt Representation in
F-logic Employee Person Researcher
EmployeecooperatesWithgtgtResearcher
37

• RDF and Logic-based Languages
• Complex RDF and corresponding Frame-logic
  expression

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

ltrdfRDFgt ltrdfDescription rdfabout"http//www.
w3.org/Home/Lassila"gt ltdcCreatorgt
ltrdfDescription rdfabout"http//www.w3.org/
staffId/85740"gt
ltvNamegtOra Lassilalt/vNamegt
ltvEmailgtlassila_at_w3.orglt/vEmailgt
lt/rdfDescriptiongt lt/dcCreatorgt
lt/rdfDescriptiongt lt/rdfRDFgt Representation in
F-logic "http//www.w3.org/Home/Lassila"Creator-
gtgt quot "http//www.w3.org/staffId/85740"
Name-gtgt"Ora Lassila" Email-gtgtlassila_at_w3.orglt
.
38
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example

• RDF and Logic-based Languages
• Complex Frame-logic query

Give me all Resources and Employees of the W3C,
such that Person is a creator of the resource and
this person is not directly related to Nokia
Research or is somehow related to the Department
of Informatics from TUM . FORALL Res,Pers lt-
ResCreator-gtPersEmployeesaffiliation-gtgt
"http//www.w3.org"
AND FORALL Prop,T EmployeePropgtgtT
//Declaration AND ( NOT PersProp-gtgt"http/
/www.research.nokia.com". OR
PersProp-gtgt"http//www.in.tum.de")
39
A Query and Inference Service for RDF

• Introduce
• Java chosen as an implementation platform(e.g. A
  java servlet RDF Query Server)
• Architecture of RDF Inference Engine

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Introduce
• Architecture of RDF Inference Engine
• Example

40

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Introduce
• Architec-ture of RDF Inference Engine
• Example

A Query and Inference Service for RDF

• Architecture of RDF Inference Engine

RDF Specification
SiRPAC
Triples
KIF Queries
Core Inference- Engine
F-Logic Axioms/Queries
F-Logic Translator (e.g. KIF)
Query Answers
Datalog Axioms/Queries
Datalog Translator
41
A Query and Inference Service for RDF
1. SiRPAC RDF-Translator http//www.w3.org/RDF/I
mplementations/SiRPAC/ 2. KIF(Knowledge
Interchange Format) Mediator F-Logic
Translator http//citeseer.nj.nec.com/KIF/ 3.
SiLRI Simple Logic-Based RDF Interpreter)

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Introduce
• Architec-ture of RDF Inference Engine
• Example

The creator of the resource
http//www11.in.tum.de/lehrstuhl/personen/schlich
ter is Schlichter ltrdfRDFgt
ltrdfDescription rdfabout"http//www11.in.tum.de
/ lehrstuhl/personen/schlichter"gt
ltdcCreatorgtSchlichterlt/dcCreatorgt
lt/rdfDescriptiongt lt/rdfRDFgt
42
A Query and Inference Service for RDF

• Current Use of Classification Schemes in Existing
  Search Services (Biz/ed, SOSIG, DDC, UDC)
• Biz/ed (Business and Economics) Subset of the
  DDC (Dewey Decimal Classification)
• SOSIG (Social Science Resouces) Subset of the
  UDC (Universal Decimal Classification)

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example Classification Scheme Mapping

43
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example Classification Scheme Mapping

• Current Use of Classification Schemes in Existing
  Search Services (Biz/ed, SOSIG, DDC, UDC)
• What is the DDC?
• DDC (Dewey Decimal Classification, Melvil Dewey,
  1873)
• What is the UDC?
• UDC (Universal Decimal Classification, Paul Otlet
  and Henri LaFontaine, 1900s)

44
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example Classification Scheme Mapping

• Current use of Classification Schemes in existing
  search services (DDC, UDC, etc.)

Comparison of the two Schemes For example
45
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example Classification Scheme Mapping

• Example Classification Scheme Mapping
• Current use of classification Schemes in existing
  search services (DDC, UDC, etc.)

More informations at http//www.sosig.ac.uk/desire
/class/mapping.html
46
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example Classification Scheme Mapping

• Rules used
• Relation about supply rules describing how to
  infer that some web resource is about some
  classification scheme concept

FORALL O,Vsubject(O,V) lt- O "http//www.desire.o
rg/vocab/classmapsubject" -gtgtV. FORALL 
Concept1, Concept2, Concept3  broader_term(Concep
t1,Concept3) lt- //broader_term(C1,C2) the
topic C2 is broader than the C1
broader_term(Concept1,Concept2) AND
broader_term(Concept2,Concept3).
47
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example Classification Scheme Mapping

• Rules used
• FORALL Resource lt- about (Resource, UDC658.8)

// "A resource is about a concept if a resource
has a subject which is that concept..."FORALL
Resource, Concept   about (Resource,Concept) lt- 
subject(Resource,Concept) OR        //
"...or is about a synonym of that concept,
EXISTS X               (
subject(Resource,X) AND synonym(X,Concept)
)           OR               (
subject(Resource,X) AND synonym(Concept,X)
).      OR //"or is about a concept that is
a broader term than that concept
(subject(Resource,X) AND broader_term(Concept,X)).
48
A Query and Inference Service for RDF

• RDF Data used
• http//purl.org/net/rdf/UDCsubset/
• http//purl.org/net/rdf/DDCsubset/

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example Classification Scheme Mapping

ltrdfDescription about "ftp//rtfm.mit.edu/pub/us
enet-by- hierarchy/sci/environment/"gt       
ltssubject resource "http//purl.org/net/rdf
/udcsubset/c504"/gtlt/rdfDescriptiongt ltrdfDescri
ption about "ftp//rtfm.mit.edu/pub/usenet-by-
hierarchy/sci/psychology/misc/"gt      
ltssubject resource "http//purl.org/net/rdf/ud
csubset/c159.9"/gtlt/rdfDescriptiongt
49
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example Classification Scheme Mapping

• RDF Data used
• Data about the relationships between
  classification concepts is also supplied.

synonym("http//purl.org/net/rdf/ddcsubset/c658.8"
,"http//purl.org/net/rdf/udcsubset/c658.8")....
broader_term("http//purl.org/net/rdf/ddcsubset/c
338.7","http//purl.org/net/rdf/udcsubset/c338").
broader_term("http//purl.org/net/rdf/ddcsubset/c
657.4","http//purl.org/net/rdf/udcsubset/c657").
50
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example Classification Scheme Mapping

• Results

• To summarize the inference engine was loaded
  with the following
• several thousand classifications in UDC
  vocabulary from the SOSIG database (in RDF)
• several thousand classifications in DDC
  vocabulary from the Biz/ed database (in RDF)
• statements about the hierachical relationships
  and human labels for these concepts
• rules providing for simple inferencing

51
A Query and Inference Service for RDF

• Example Query

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example Classification Scheme Mapping

QueryFORALL Classification   lt- about
("http//www.stir.ac.uk/marketing/",Classification
).ResultsClassification
"http//purl.org/net/rdf/DDCsubset/c378"Classific
ation "http//purl.org/net/rdf/DDCsubset/c658.8"
Classification "http//purl.org/net/rdf/UDCsubs
et/c658.8"
52
A Query and Inference Service for RDF

• Introduction
• Requirements for RDF Query Languange and
  Inference System
• RDF and Logic-based Languages
• Inference Engine Architecture
• Example Classification Scheme Mapping

• Example Query

subject( "http//www.stir.ac.uk/marketing/",
"http//purl.org/net/rdf/DDCsubset/c378
) subject( "http//www.stir.ac.uk/marketing/",
"http//purl.org/net/rdf/DDCsubset/c65
8 ) title( "http//www.stir.ac.uk/marketing/",
"University of Stirling, Department of
Marketing ).
53
Reasoning in Semantic Web

• Logic layer Outline
• Inference Outline
• Example from Query- and Reasoning-Engines
• News from W3C
• References

54
Reasoning in Semantic Web

• The Next Web 30 Apr 2002
• http//www.businessweek.com/magazine/content/02_
  09/b3772108.htm
• DARPA is also funding research at MIT, headed by
  Berners-Lee but separate from the W3C, aimed at
  creating new AI tools for tomorrow's Web. One
  result would be Semantic Web logic language
  (Swell). Another goal is to marry the Semantic
  Web with MIT's Oxygen project, which aims to make
  various digital systems as easy to use as
  breathing, thanks to advanced machine-learning
  tricks and new AI software. ...
• DARPA The Pentagon's Defense Advanced
  Research Projects Agency
• AI Artificial Intelligence
• Swell Semantic Web Engine Logical
  Language

• Logic Layer Outline
• Inference Outline
• Example from Query- and Reasoning-Engines  
• News from W3C
• References

• SiLRI simple Logic-based RDF Interpreter

55
Reasoning in Semantic Web

• Logic Layer Outline
• Inference Outline
• Example from Query- and Reasoning-Engines   
• News from W3C
• References

• Semantic Web Development
• http//www.w3.org/2000/01/sw/DevelopmentProposa
  l
• SWeLL (Semantic Web Engine Logic
  Language )
• a language, a part of DAML family - for
  expressing and transferring high-level logical
  statements (rules, axioms) and proofs. ...

56
Reasoning in Semantic Web

• Logic layer Outline
• Inference Outline
• Example from Query- and Reasoning-Engines 
• News from W3C
• References

57
Reasoning in Semantic Web
1. Semantic Web ,       http//www.w3.org/20
01/sw/ 2. The Semantic Web An Introduction
,http//infomesh.net/2001/swintro/ 3. Aaron
Swartz The Semantic Web in Breadth ,
http//logicerror.com/semanticWeb-long 4.
Kapitel "SiLRI-simple Logic-based RDF
Interpreter" in S.Staab, Vorlesung
"Intelligente Systeme im WWW", Universität
Karlsruhe http//www.aifb.uni-karlsruhe.de/L
ehrangebot/Sommer2001/IntelligenteSystemeImWWW/
5. SiLRI download site , http//www.ontoprise
.de/com/start_downlo.htm 6. Inference Engines
for the Semantic Web , http//www.semanticweb
.org/inference.html 7. The SPASS Home
Page, http//spass.mpi-sb.mpg.de 8. Common
Syntax of the DFG-Schwerpunktprogramm Deduktion
Version1.3 ,

http//spass.mpi-sb.mpg.de/webspass/help/syntax/in
dex.html 9. The HOL System DESCRIPTION ,
http//www.ftp.cl.cam.ac.uk/ftp/hvg/hol98/taupo-
6-description.pdf

• Logic Layer Outline
• Inference Outline
• Example from Query- and Reasoning-Engines  
• News from W3C
• References

58
Reasoning in Semantic Web
10. Introduce to HOL (CUP,1993) ,
http//www.dcs.gla.ac.uk/tfm/Papers/HOLbook.html
11. The HOL-Emacs Manual ,
http//lal.cs.byu.edu/lal/hol-emacs/hol-e
macs.html 12. ML Overview ,
http//lal.cs.byu.edu/lal/holtut/ml-overview.html
13. KIF(Knowledge Interchange Format) Mediator
F-Logic Translator, http//citeseer.nj.nec.com/KI
F/ 14. Building the Semantic Web by Edd
Dumbill March 07, 2001
http//www.xml.com/pub/a/2001/03/07/buildingsw.htm
l 15. A Programmers Introduction to Predicate
Logic Technical Report UMCIS-1994-02 H.Conrad
Cunningham, University Mississippi 38677 USA,
Revised Jan.1996 16. The Web's Weaver Looks
orward from Tim Berners-Lee ,
www.businessweek.com/technology/content/mar2002/
tc20020327_2745.htm 17. The Next Web from Tim
Berners-Lee , http//www.businessweek.com/m
agazine/content/02_09/b3772108.htm 18. Rules
and Facts Inference engines vs Web, Tim
Berners-Lee, 1998, last change 16/01/2001,
http//www.w3.org/DesignIssues/Rules.html 19.
Mapping Classification Schemes
http//www.sosig.ac.uk/desire/class/mapping.html

• Logic Layer Outline
• Inference Outline
• Example from Query- and Reasoning-Engines   
• News from W3C
• References

59
THE END