Semantic rules and inference make a comeback, watch out query!

AGU FM10 IN44B-01

Peter Fox (RPI) pfox@cs.rpi.eduTetherless World Constellation

2

Semantic Web Layers

http://www.w3.org/2003/Talks/1023-iswc-tbl/slide26-0.html, http://flickr.com/photos/pshab/291147522/

3

Ontology Spectrum

Catalog/ID

SelectedLogical

Constraints(disjointness,

inverse, …)

Terms/glossary

Thesauri“narrower

term”relation

Formalis-a

Frames(properties)

Informalis-a

Formalinstance Value

Restrs.

GeneralLogical

constraints

Originally from AAAI 1999- Ontologies Panel by Gruninger, Lehmann, McGuinness, Uschold, Welty; – updated by McGuinness.Description in: www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.html

Semantic Web Standards*

• Schema - RDFS (2004)

• Ontology - OWL 1.0 (2004), OWL 2.0 (2009)

• Query - SPARQL 1.0 (2008), 1.1 in draft

• Taxonomy - SKOS (2009)

• Rules - RIF (2010)

SPARQL

• SPARQL has 4 result forms:– SELECT – Return a table of results.– CONSTRUCT – Return an RDF graph, based on a

template in the query.– DESCRIBE – Return an RDF graph, based on

what the query processor is configured to return.– ASK – Ask a boolean query.

• The SELECT form directly returns a table• DESCRIBE and CONSTRUCT use the

outcome of matching to build RDF graphs.

5

SPARQL Solution Modifiers

• Pattern matching produces a set of solutions. This set can be modified in various ways:– Projection - keep only selected variables– OFFSET/LIMIT - chop the number solutions (best

used with ORDER BY)– ORDER BY - sorted results– DISTINCT - yield only one row for one

combination of variables and values.

• The solution modifiers OFFSET/LIMIT and ORDER BY always apply to all result forms.

6

Query is popular

• It looks like SQL

• Triple stores and query endpoints are now becoming prevelant and many even conform to SPARQL 1.0 recommendation (1.1 on the way)

• OWL 2 QL is intended to provide an OWL 2 subset

Semantic query limitations

• Query does not know that a triple has been inferred or it has an inference (or rule)

• Query has to contain semantics of the underlying knowledge base

• If the ontology changes queries can break

• Limited to declared knowledge, logic

Rule evolution

• Jena, Jess, RuleML, and SWRL (OWL+RuleML) -> RIF and OWL 2 RL

• RL features– Triple pattern rules– Inconsistency rules– List rules

• Inconsistent pairs rules• Property chain rule• HasKey rule• Forward intersectionOf rule• Simple member rules

– Datatype rules

E.g. Testing class membership

Document(

Prefix(fam http://example.org/family#)

Group (

Forall ?X ?Y (

fam:isFatherOf(?Y ?X) :- And (fam:isSonOf(?X ?Y) fam:isMale(?Y) ?X#fam:Child ?Y#fam:Parent )

)

fam:isSonOf(fam:Adrian fam:Uwe)

fam:isMale(fam:Adrian)

fam:isMale(fam:Uwe)

fam:Adrian#fam:Child

fam:Uwe#fam:Parent

)

)

Conclusion: fam:isFather(fam:Uwe fam:Adrian)10

About your selected parameters:

Parameter A Parameter B Difference alert

Parameter Name : Aerosol Optical Depth at 550 nm

Aerosol Optical Depth at 550 nm

Dataset: MYD08_D3.005 MOD08_D3.005 Diff

Data-Day definition UTC (00:00-24:00Z) UTC(00:00-24:00Z) The same but….

Temporal resolution Daily Daily

Spatial resolution 1x1 degree 1x1 degree

Sensor: MODIS MODIS

Platform: Aqua Terra Diff

EQCT 13:30 10:30 Diff

Day Time Node Ascending Descending Diff

Pre-Giovanni Processes : ATBD-MOD-30 ATBD-MOD-30

Giovanni Processes: Spatial subsetTime average

Spatial subsetTime average

Your Selected Options:

Spatial Area: Longitude ( -30, 150), Latitude (-10,60)Parameters: A: MYD08_D3.005 Aerosol Optical Depth at 550 nm

B: MOD08_D3.005 Aerosol Optical Depth at 550 nmTemporal Range: Begin Date: Jan 01 2008

End Date: Jan 31 2008Visualization Function: Lat –Lon map Time-averaged

Continue process to display image Return to selection page

Known Issues: The difference of EQCT and Day Time Node, modulated by data-day definition, caused the included overpass time difference, which makes the artifact difference. See sample images:

MODIS Terra vs. MODIS Aqua AOD Correlation Included Overpass time Difference

Use case - Semantic Advisor

Parameter A Parameter B Difference alert

Parameter Name : Aerosol Optical Depth at 550 nm Aerosol Optical Depth at 550 nm

Dataset: MYD08_D3.005 MOD08_D3.005 Diff

Data-Day definition UTC (00:00-24:00Z) UTC(00:00-24:00Z) The same but….

Temporal resolution Daily Daily

Spatial resolution 1x1 degree 1x1 degree

Sensor: MODIS MODIS

Platform: Aqua Terra Diff

EQCT 13:30 10:30 Diff

Day Time Node Ascending Descending Diff

Pre-Giovanni Processes : ATBD-MOD-30 ATBD-MOD-30

Giovanni Processes: Spatial subsetTime average

Spatial subsetTime average

Multi-sensor Data Synergy Advisor (NASA), Leptoukh, Lynnes, Zednik, et al.

RuleSet Development

[DiffNEQCT:(?s rdf:type gio:RequestedService),(?s gio:input ?a),(?a rdf:type gio:DataSelection),(?s gio:input ?b),(?b rdf:type gio:DataSelection),(?a gio:sourceDataset ?a.ds),(?b gio:sourceDataset ?b.ds),(?a.ds gio:fromDeployment ?a.dply),(?b.ds gio:fromDeployment ?b.dply),(?a.dply rdf:type gio:SunSynchronousOrbitalDeployment),(?b.dply rdf:type gio:SunSynchronousOrbitalDeployment),(?a.dply gio:hasNominalEquatorialCrossingTime ?a.neqct),(?b.dply gio:hasNominalEquatorialCrossingTime ?b.neqct),notEqual(?a.neqct, ?b.neqct)->(?s gio:issueAdvisory giodata:DifferentNEQCTAdvisory)]

Multi-sensor Data Synergy Advisor (NASA), Leptoukh, Lynnes, Zednik, et al.

Semantic Advisor Architecture

RPI

Multi-sensor Data Synergy Advisor (NASA), Leptoukh, Lynnes, Zednik, et al.

Increasing use of rules for (e.g. metadata) annotation

• Flexible and extensible self describing schemas that don’t have to be nailed down– Allows description (instead of prescription) of my data

set, or the output format of my tool, depending on different vocabularies that may/ will change

• Open world (provenance)– “I need to comment on that experiment” (in MY context)– “That fact is now incorrect because …”

• Data fusion across different data models– cross linked by shared instances and shared concepts

• Global naming scheme mapping– E.g. LSID: Life Science Identifiers

Implications (1)

• Rules give richer semantics and trade-off options between declarative approaches and their implementation

• Some interesting partitioning between where semantics are implemented, i.e.– With query, a lot of semantics gets encoded in the

query itself, especially if it is non-trivial – the semantics can be well separated and become incompatible

– With rules, the semantics are added to the knowledge base and thus more likely to be consistent (or checked for consistency)

Implications (2)

• Integration of rule development, verification, and use into application tools lags those for query

• Improvements still needed for fully materialized ontology/ rule knowledge bases

• Availability of built-ins for rule languages substantially increases logic capabilities but again complicates the choice between declarative and procedural logic

• Late semantic binding!!!

• So… take another look at OWL 2 – RL and RIF!• Thanks.

18

Jena rule example

<ex:Driver rdf:about="http://example.com/John">

<ex:state>New York</ex:state>

<ex:hasTrainingCertificate rdf:datatype="http://www.w3.org/2001/XMLSchema#boolean">true</ex:hasTrainingCertificate>

</ex:Driver>

@prefix rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#

@prefix ex: http://example.com/

@prefix xs: http://www.w3.org/2001/XMLSchema#

[eligibleDriver: (?d rdf:type ex:EligibleDriver)

<-

(?d rdf:type ex:Driver)

(?d ex:state "New York")

(?d ex:hasTrainingCertificate "true"^^xs:boolean)]

Any driver living in New York and having training driver certificate is eligible for insurance.