Semantic rules and inference make a comeback watch

Semantic rules and inference make a comeback, watch out query! AGU FM 10 IN 44 B-01 Peter Fox (RPI) pfox@cs. rpi. edu Tetherless World Constellation

Semantic Web Layers 2 http: //www. w 3. org/2003/Talks/1023 -iswc-tbl/slide 26 -0. html, http: //flickr. com/photos/pshab/291147522/

Ontology Spectrum Thesauri “narrower Catalog/ term” ID relation Terms/ glossary Informal is-a Selected Formal Frames Logical is-a (properties)Constraints (disjointness, inverse, …) Formal Value instance Restrs. General Logical constraints Originally from AAAI 1999 - Ontologies Panel by Gruninger, Lehmann, Mc. Guinness, Uschold, Welty; – updated by Mc. Guinness. Description in: www. ksl. stanford. edu/people/dlm/papers/ontologies-come-of-age-abstract. html 3

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, Rule. ML, and SWRL (OWL+Rule. ML) -> RIF and OWL 2 RL • RL features – Triple pattern rules – Inconsistency rules – List rules • • • Inconsistent pairs rules Property chain rule Has. Key rule Forward intersection. Of rule Simple member rules – Datatype rules

E. g. Testing class membership Document( Prefix(fam http: //example. org/family#) Group ( Forall ? X ? Y ( fam: is. Father. Of(? Y ? X) : - And (fam: is. Son. Of(? X ? Y) fam: is. Male(? Y) ? X#fam: Child ? Y#fam: Parent ) ) fam: is. Son. Of(fam: Adrian fam: Uwe) fam: is. Male(fam: Adrian) fam: is. Male(fam: Uwe) fam: Adrian#fam: Child fam: Uwe#fam: Parent ) ) Conclusion: fam: is. Father(fam: Uwe fam: Adrian) 10

Use case - Semantic Advisor Spatial Area: Parameters: Your Selected Options: Parameter About your selected parameters: A Parameter Name : Longitude ( -30, 150), Latitude (-10, 60) A: MYD 08_D 3. 005 Aerosol Optical Depth at 550 nm B: MOD 08_D 3. 005 Aerosol Optical Depth at 550 nm Temporal Range: Begin Date: Jan 01 2008 Parameter B Date: Jan 31 2008 Difference alert End Visualization Function: Lat –Lon map Time-averaged Aerosol Optical Depth. A 550 nm Parameter at Parameter Name : Dataset: Temporal resolution MOD 08_D 3. 005 Diff UTC(00: 00 -24: 00 Z) Daily The same but…. Temporal resolution UTC (00: 00 -24: 00 Z) Daily Spatial resolution Daily Data-Day definition Sensor: Spatial resolution Aerosol Optical Depth at 550 nm MYD 08_D 3. 005 Dataset: Data-Day definition Aerosol Optical Depth at 550 nm Aerosol Optical Depth at 550 Difference alert nm Parameter B Platform: EQCT 1 x 1 degree MODIS 1 x 1 degree Aqua The same but…. 1 x 1 Daily degree MODIS 1 x 1 Terra degree Diff Day Time Node Pre-Giovanni Processes : Platform: Ascending 10: 30 MODIS Descending Aqua ATBD-MOD-30 Terra Diff 13: 30 Sensor: Spatial subset Time average 10: 30 Diff MODIS Giovanni Processes: EQCT 13: 30 Diff Known Issues: The difference of EQCT and Day Time Node, modulated by data-day definition, caused the included overpass time difference, which makes the Diff Day Time Node Ascending Descending artifact difference. See sample images: Pre-Giovanni Processes : ATBD-MOD-30 Giovanni Processes: Spatial subset Time average MODIS Terra vs. MODIS Aqua AOD Correlation Continue process to display image Included Overpass time Difference Return to selection page Multi-sensor Data Synergy Advisor (NASA), Leptoukh, Lynnes, Zednik, et al.

Rule. Set Development [Diff. NEQCT: (? s rdf: type gio: Requested. Service), (? s gio: input ? a), (? a rdf: type gio: Data. Selection), (? s gio: input ? b), (? b rdf: type gio: Data. Selection), (? a gio: source. Dataset ? a. ds), (? b gio: source. Dataset ? b. ds), (? a. ds gio: from. Deployment ? a. dply), (? b. ds gio: from. Deployment ? b. dply), (? a. dply rdf: type gio: Sun. Synchronous. Orbital. Deployment), (? b. dply rdf: type gio: Sun. Synchronous. Orbital. Deployment), (? a. dply gio: has. Nominal. Equatorial. Crossing. Time ? a. neqct), (? b. dply gio: has. Nominal. Equatorial. Crossing. Time ? b. neqct), not. Equal(? a. neqct, ? b. neqct) -> 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.

Jena rule example <ex: Driver rdf: about="http: //example. com/John"> <ex: state>New York</ex: state> <ex: has. Training. Certificate rdf: datatype="http: //www. w 3. org/2001/XMLSchema#boolean">tr ue</ex: has. Training. Certificate> </ex: Driver> @prefix rdf: http: //www. w 3. org/1999/02/22 -rdf-syntax-ns# @prefix ex: http: //example. com/ @prefix xs: http: //www. w 3. org/2001/XMLSchema# [eligible. Driver: (? d rdf: type ex: Eligible. Driver) <(? d rdf: type ex: Driver) (? d ex: state "New York") (? d ex: has. Training. Certificate "true"^^xs: boolean)] 18