1 / 18

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

Semantic rules and inference make a comeback, watch out query!. AGU FM10 IN44B-01. Peter Fox (RPI) pfox@cs.rpi.edu Tetherless World Constellation. Semantic Web Layers. http://www.w3.org/2003/Talks/1023-iswc-tbl/slide26-0.html, http://flickr.com/photos/pshab/291147522/. Ontology Spectrum.

byrd
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Semantic rules and inference make a comeback, watch out query! AGU FM10 IN44B-01 Peter Fox (RPI) pfox@cs.rpi.edu Tetherless 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 Thesauri “narrower term” relation Selected Logical Constraints (disjointness, inverse, …) Frames (properties) Formal is-a Catalog/ ID Informal is-a Formal instance General Logical constraints Terms/ glossary Value Restrs. 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

  4. 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)

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. 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

  10. 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)

  11. Use case - Semantic Advisor 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 nm Temporal Range: Begin Date: Jan 01 2008 End Date: Jan 31 2008 Visualization Function: Lat –Lon map Time-averaged About your selected parameters: 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 Continue process to display image Return to selection page Multi-sensor Data Synergy Advisor (NASA), Leptoukh, Lynnes, Zednik, et al.

  12. 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.

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

  14. 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

  15. 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)

  16. 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.

  17. 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.

More Related