Presentation at the ESWC 2011 PhD Symposium in May 2011, by Michael Schneider, FZI. Included are backup slides that have not been presented at the event. The corresponding PhD proposal can be found in the ESWC proceedings at <http: />. Alternatively, the PhD proposal can be downloaded from <http: />.
Presentation of the paper "Reasoning in the OWL 2 Full Ontology Language using First-Order Automated Theorem Proving" by Michael Schneider, FZI Karlsruhe, and Geoff Sutcliffe, University of Miami, at the 23rd International Conference on Automated Deduction (CADE 23), August 2011.
This talk was given by FORTH, Greece, at the European Data Forum (EDF) 2012 took place on June 6-7, 2012 in Copenhagen (Denmark) at the Copenhagen Business School (CBS).
Abstract:
Given the increasing amount of sensitive RDF data available on the Web, it becomes increasingly critical to guarantee secure access to this content. Access control is complicated when RDFS inference rules and other dependencies between access permissions of triples need to be considered; this is necessary, e.g., when we want to associate the access permissions of inferred triples with the ones that implied it. In this paper we advocate the use of abstract provenance models that are defined by means of abstract tokens operators to support fine grained access control for RDF graphs. The access label of a triple is a complex expression that encodes how said label was produced (i.e., the triples that contributed to its computation). This feature allows us to know exactly the effects of any possible change, thereby avoiding a complete recomputation of the labels when a change occurs. In addition, the same application can choose to enforce different access control policies or, different applications can enforce different policies on the same data, avoiding the recomputation of the label of a triple. Preliminary experiments have shown the applicability and benefits of our approach.
Presentation of the paper "Reasoning in the OWL 2 Full Ontology Language using First-Order Automated Theorem Proving" by Michael Schneider, FZI Karlsruhe, and Geoff Sutcliffe, University of Miami, at the 23rd International Conference on Automated Deduction (CADE 23), August 2011.
This talk was given by FORTH, Greece, at the European Data Forum (EDF) 2012 took place on June 6-7, 2012 in Copenhagen (Denmark) at the Copenhagen Business School (CBS).
Abstract:
Given the increasing amount of sensitive RDF data available on the Web, it becomes increasingly critical to guarantee secure access to this content. Access control is complicated when RDFS inference rules and other dependencies between access permissions of triples need to be considered; this is necessary, e.g., when we want to associate the access permissions of inferred triples with the ones that implied it. In this paper we advocate the use of abstract provenance models that are defined by means of abstract tokens operators to support fine grained access control for RDF graphs. The access label of a triple is a complex expression that encodes how said label was produced (i.e., the triples that contributed to its computation). This feature allows us to know exactly the effects of any possible change, thereby avoiding a complete recomputation of the labels when a change occurs. In addition, the same application can choose to enforce different access control policies or, different applications can enforce different policies on the same data, avoiding the recomputation of the label of a triple. Preliminary experiments have shown the applicability and benefits of our approach.
The formulation of constraints and the validation of RDF data against these constraints is a common requirement and a much sought-after feature, particularly as this is taken for granted in the XML world. Recently, RDF validation as a research field gained speed due to shared needs of data practitioners from a variety of domains. For constraint formulation and RDF data validation, several languages exist or are currently developed. Yet, none of the languages is able to meet all requirements raised by data professionals.
We have published a set of constraint types that are required by diverse stakeholders for data applications. We use these constraint types to gain a better understanding of the expressiveness of solutions, investigate the role that reasoning plays in practical data validation, and give directions for the further development of constraint languages.
We introduce a validation framework that enables to consistently execute RDF-based constraint languages on RDF data and to formulate constraints of any type in a way that mappings from high-level constraint languages to an intermediate generic representation can be created straight-forwardly. The framework reduces the representation of constraints to the absolute minimum, is based on formal logics, and consists of a very simple conceptual model with a small lightweight vocabulary. We demonstrate that using another layer on top of SPARQL ensures consistency regarding validation results and enables constraint transformations for each constraint type across RDF-based constraint languages.
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My personal portfolio, containing project in retail, interiors and furniture industry.
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You planning a family? then you maybe wondering whether you will be having boy or girl baby. What if I told you that you can choose the gender of your baby using natural and healthy techniques.
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Selecting the gender of your baby is possible if you follow the correct techniques.
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What is at the heart of all the activities you do at work?
1. Observation and Action
2. Story Telling
During this talk I was invited to share with the HCI students at Oregon State University, School of Computer Science real examples of work, prototypes and design that was provoking questions as much as it was answering them.
Two steps include making observations to arrive to insights. Then make an assertion by building. Everything afterwards is test, tweak, test, tweak ...
The formulation of constraints and the validation of RDF data against these constraints is a common requirement and a much sought-after feature, particularly as this is taken for granted in the XML world. Recently, RDF validation as a research field gained speed due to shared needs of data practitioners from a variety of domains. For constraint formulation and RDF data validation, several languages exist or are currently developed. Yet, none of the languages is able to meet all requirements raised by data professionals.
We have published a set of constraint types that are required by diverse stakeholders for data applications. We use these constraint types to gain a better understanding of the expressiveness of solutions, investigate the role that reasoning plays in practical data validation, and give directions for the further development of constraint languages.
We introduce a validation framework that enables to consistently execute RDF-based constraint languages on RDF data and to formulate constraints of any type in a way that mappings from high-level constraint languages to an intermediate generic representation can be created straight-forwardly. The framework reduces the representation of constraints to the absolute minimum, is based on formal logics, and consists of a very simple conceptual model with a small lightweight vocabulary. We demonstrate that using another layer on top of SPARQL ensures consistency regarding validation results and enables constraint transformations for each constraint type across RDF-based constraint languages.
You may think designer babies are a thing of the feture, but you would be wrong. Hundreds of people are already choosing the gender of their babies in a natural and simple way.
My personal portfolio, containing project in retail, interiors and furniture industry.
Principal projects: Armani HK, Armani Ginza, Benetton shopping Rome, private houses, furniture, restaurants, hotels
You planning a family? then you maybe wondering whether you will be having boy or girl baby. What if I told you that you can choose the gender of your baby using natural and healthy techniques.
Are you desperate to have a girl next time? That was the same thing I was hoping for, yet I made it happen.
Selecting the gender of your baby is possible if you follow the correct techniques.
Talk @ Oregon State University SCS 2012-05-29Joshua Zúñiga
What is at the heart of all the activities you do at work?
1. Observation and Action
2. Story Telling
During this talk I was invited to share with the HCI students at Oregon State University, School of Computer Science real examples of work, prototypes and design that was provoking questions as much as it was answering them.
Two steps include making observations to arrive to insights. Then make an assertion by building. Everything afterwards is test, tweak, test, tweak ...
OWL stands for Web Ontology Language
OWL is built on top of RDF
OWL is for processing information on the web
OWL was designed to be interpreted by computers
OWL was not designed for being read by people
OWL is written in XML
OWL has three sublanguages
- OWL Lite , OWL DL , OWL Full
OWL is a W3C standard
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our language for expressing RDF constraints. We introduce
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Communications Mining Series - Zero to Hero - Session 1
eswc2011phd-schneid
1. Reasoning in Expressive Extensions
of the RDF Semantics
Michael Schneider (FZI Karlsruhe, Germany)
ESWC 2011 PhD Symposium
Heraklion (Greece), 31 May 2011
WIR FORSCHEN FÜR SIE
2. RDF Semantics and Semantic Extensions
• RDF Semantics:
– Part of W3C RDF Specification (Hayes, 2004)
– Defines formal meaning of RDF graphs (as a model-theory)
– Includes four increasingly expressive semantics:
Simple Entailment, RDF, RDFS, and D-Entailment
– Characteristics:
• all RDF graphs are valid and have a semantic meaning
• Semantics is defined on the level of RDF triples and sets of triples
• all nodes represent resources (aka individuals)
• Semantic Extensions of the RDF Semantics:
– Semantics that builds on top of RDF(S) or D-Entailment:
• all parts of semantics of weaker language are reused and extended
• Syntax is all RDF graphs
– Example: RDFS is a semantic extension of RDF
– Example: OWL 2 Full is a semantic extension of RDFS (or D) 2
3. Semantic Web Ontology Languages:
Syntactic Flexibility vs. Semantic Expressivity
?
Syntactic Flexibility (RDF)
OWL 2 ? OWL 2
RDFS
RL/RDF Full
?
OWL 2
DL
OWL
Lite
Semantic Expressivity
• Unclear: Differences of OWL 2 Full to OWL 2 DL and OWL 2 RL/RDF?
• Unclear: Implementability of OWL 2 Full (or any expressive RDF extension)?
3
4. OWL 2 Full vs. OWL 2 DL:
Enhanced Syntactic Flexibility in RDF
OWL 2 DL tools typically cannot properly deal with every RDF graph:
Use of RDF(S) Entity Types
dcels:title rdf:type rdf:Property
dcterms:title rdf:type rdf:Property
dcterms:title rdfs:subPropertyOf dcels:title
OWL API 3.2 read/write roundtrip:
re-declaration of both properties
as OWL annotation properties
Result (after read/write roundtrip)
dcels:title rdf:type owl:AnnotationProperty
dcterms:title rdf:type owl:AnnotationProperty
dcterms:title rdfs:subPropertyOf dcels:title 4
6. OWL 2 Full vs. OWL 2 DL & RL/RDF Rules:
Enhanced Modeling & Reasoning Capabilities
• Metamodeling
e.g. reasoning upon zoological hierarchies: Harry → Eagle → Species
• Cyclic relationships
e.g. detection of circular chemical molecules
• Macros, conditional semantics, etc.
e.g. custom entity types
6
7. Usage Scenarios for OWL 2 Full Reasoners
• Complementing RDF entailment-rule reasoners:
– much stronger in terminological reasoning
– RDF rule reasoners advantage: faster, better scalability
– fully compatible with RDFS and OWL 2 RL/RDF rules:
OWL 2 Full reasoner can safely operate in parallel
• Complementing description-logic reasoners:
– basically compatible due to „correspondence theorem“
– robust on weakly-structured data (typical for LOD cloud)
– „trans-DL“ reasoning (metamodeling, cyclic structures, …)
– DL reasoners advantage: better on valid OWL 2 DL input
7
8. Prior Art in OWL Full Reasoning
• Fikes, McGuinness, Waldinger: A First-Order Logic Semantics
for Semantic Web Markup Languages. TR, Stanford, 2002.
– translation of specifications of precursers of OWL and RDF into first-order
logic (FOL) theory, and application of FOL reasoners.
– focus: checking for technical issues in specifications (less on inferencing)
• Hayes: Translating Semantic Web Languages into Common Logic.
TR, Pensacola (Florida), 2005.
– translation of OWL 1 Full into Common Logic
– no report on reasoning experiments
• Hawke: Surnia. 2003. URL: http://www.w3.org/2003/08/surnia
– OWL 1 Full reasoner based on FOL translation using Otter FOL reasoner
– did not perform well on W3C OWL 1 test suite
– ad hoc implementation: does not properly follow specification; many flaws
9. Research Questions
1. What are the distinctive features of OWL 2 Full
compared to other approaches used for
Semantic Web reasoning?
2. To which degree and how can reasoning in
OWL 2 Full be implemented?
9
10. Approach
• „Feature Analysis“ (addresses 1st research question):
– Building up catalogs of distinctive pragmatic features of OWL 2 Full
– „distinctive“: not supported by either OWL 2 DL or OWL 2 RL/RDF rules
– will cover both syntactic (parsing) and semantic (reasoning) aspects:
• syntactic aspect example: disjoint annotation properties (SKOS)
• semantic aspect example: entailments from metamodeling (vs. „punning“)
• „Implementability Analysis“ (addresses 2nd research question) :
– Focus: in-deph investigation of „naive“ FOL translation approach:
• Translation of OWL 2 Full semantics into a first-order logic (FOL) theory
• Translation of RDF graphs into FOL formulae
• Applying FOL reasoners (theorem provers, model finders) for reasoning
• Evaluation:
– Collecting evidence for all identified OWL 2 Full features (empirical)
– Evaluating FOL-based reasoner prototype w.r.t. identified features
10
11. Feature Analysis: First Results
• Created: Catalog of syntactic-aspect features for OWL /1/ Full
– identified 14 feature categories and 90 features
– Example feature: “Anonymous Individuals with Cyclic Relationships”
– Example category: “Unrestricted Use of Blank Nodes“
• Usage: Evaluation of ontology engineering tools in EU Project SEALS
– per identified feature: created one small example ontology („spot test“)
– for each example ontology: analyzed read/write roundtrip for tool under test
• Results:
– OWL DL tools (OWL API 3.1, Protege 4, …) had many difficulties:
• almost all test ontologies were changed during read/write roundtrips
• in many cases, the changes were significant or even severe
– see SEALS deliverable D-10.3, specifically Appendix A for detailed analysis 11
13. Conclusions and Future Work
• OWL 2 Full has many distinguishing features and potential benefits
• OWL 2 Full reasoning generally works with FOL reasoners
but there is a serious efficiency issue due to the large FOL axiomatization
Report on the results of all reasoning experiments (to appear):
Michael Schneider, Geoff Sutcliffe: Reasoning in the OWL 2 Full Ontology Language
using First-Order Automated Theorem Proving. CADE 2011.
• FOL-translation approach is very flexible:
– applies to arbitrary extension of RDF semantics (including complete RDFS)
– enables rule-style extensions (e.g. RIF+OWL-Full combination)
• Future work: finish feature analysis (syntactic and semantic features)
• Future work: address main efficiency issue: method to remove irrelevant axioms
• Future work: investigate query answering (towards SPARQL 1.1) 13
20. Syntactic Aspect Feature Analysis:
Evaluation of OWL API 3.1 (coarse)
• Application of concrete example OWL Full ontologies to OWL API 3.1
• Observation: most test ontologies were modified („repaired“)
• Note: the differences have been analysed in detail (not shown)
HR CR TR TC NT ME DP AP DT LT BN CP LS LR + isomorphic RDF graph
reconstruction
01 - - - - + - - + - - - - - -
- different RDF graph
02 - - - - + - - + - - - - -
03 - - - - + - - - - - - - - X processing error
04 - - - + - - - - - - - -
05 - - - + - - - + - - -
06 - - - - - - - - -
07 - - - - -
08 - - - -
09 X - -
10 - - -
11 - -
12 -
20
21. Syntactic Aspect Feature Analysis:
Evaluation of OWL API 3.1 (fine-grained)
OWL (2) Full Perspective OWL (2) DL Perspective
H C T T N M D A D L B C L L H C T T N M D A D L B C L L
R R R C T E P P T T N P S R R R R C T E P P T T N P S R
0 # - # # + # # + # - - # # # 0 - ! # - - # - + - - ! - - -
1 1
0 # # # # + # # + # - - - # 0 # # / - - # - + - - ! ! -
2 2
0 - / # # + # # # # - - # # 0 ! # # - - # - - - ! ! - -
3 3
0 ! # / + # # # / - - # / 0 - / - - # - - - - - - -
4 4
0 # # / + # # ! + # - / 0 # # - + # - - + - - -
5 5
0 # # # # # - # - - 0 / - # # - ! - - -
6 6
0 # # - - # 0 # # ! - -
7 7
0 ! # - # 0 - # - -
8 8
0 X / # 0 X # -
9 9
1 - / # 1 - # -
0 0
1 / # 1 # -
1 1
1 # 1 #
2 2
21
22. (Entailment Checking)
OWL 2 Full Reasoning
FOL Translation Approach
FOL-Translations of
Semantic Conditions &
FOL-Translation of
Premise Graph
negated
FOL-Translation of
&
&
FOL
Reasoner
(ATP) { TRUE
FALSE
UNKNOWN }
&
Conclusion Graph
Semantic Conditions
FOL-Translation of
model-theoretic OWL 2 Full semantic condition
corresponding FOL formula (TPTP)
FOL-Translation of
RDF Graphs
RDF graph (Turtle) 22
corresponding FOL formula (TPTP)
24. rdfbased-sem Test Suite: Performance
Vampire 0.6 iProver-SInE 0.8
complete axiomset complete axiomset
min 0.01 s 0.05 s
max N/A N/A
max succ 27.57 s 278.71 s
Q1 (1st quantil) 0.03 s 0.09 s
Q2 (median) 0.35 s 0.29 s
Q3 (3rd quantil) 0.56 s 5.21 s
mean succ 0.42 s 14.59 s
StD succ 2.06 s 36.45 s
24
27. Fullish Test Suite: Performance
iProver-SInE 0.8 iProver-SInE 0.8 iProver-SInE 0.8
complete axiomset small axiomsets 1M bulk RDF data
min 0.08 s 0.04 s 21.73 s
max N/A 164.20 s N/A
max succ 123.01 s 164.20 s 63.10 s
Q1 (1st quantil) 0.72 s 0.05 s 21.91 s
Q2 (median) 5.31 s 0.08 s 22.07 s
Q3 (3rd quantil) 89.45 s 0.14 s 22.50 s
mean succ 30.63 s 7.82 s 24.76 s
StD succ 43.41 s 29.82 s 10.02 s 27
28. Model-Finding Experiments
• Task: Detection of consistency of ontologies and non-entailments
• Prerequisite: detection of satisfiability for whole axiomatization
Results (Summary):
• OWL 2 Full:
– No FOL model-finder confirmed satisfiability of axiomatization (timeouts)
– Fortunately: no theorem prover confirmed unsatisfiability!
– Good: all „small-sufficient“ sub-axiomatizations of test cases satisfiable!
• ALCO Full (undecidable fragment of OWL 2 Full [Motik05]):
– Consistency checking for axiomatization successful !
– Non-entailment checking often successful ! (but still some failures)
– Performance: median ~18s with model-finder Paradox
• RDFS (actually: RDFS-EXT, Sec. 4.2 of RDF Semantics):
– Consistency and non-entailment checking always successful !
– pretty fast: ~1/10s for most experiments with model-finder DarwinFM
28
29. Model-Finding Experiments:
Consistency / Non-Entailment Detection
Language: RDFS-EXT Language: ALCO Full
Testsuite: Fullish Testsuite: Fullish
ATP: DarwinFM 1.4.5 ATP: Paradox 4.0
min 0.01 s 8.21 s
max 7.35 s N/A
max succ 7.35 s 89.21 s
Q1 (1st quantil) 0.05 s 13.60 s
Q2 (median) 0.07 s 17.62 s
Q3 (3rd quantil) 0.12 s N/A
mean succ 0.71 s 19.18 s
StD succ 1.86 s 18.99 s
29