Closing Some Doors for the Open Semantic Web WIMS 2012 Jeff Z. Pan - - PDF document

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“Closing” Some Doors for the Open Semantic Web

WIMS 2012

Jeff Z. Pan

Department of Computing Science University of Aberdeen, UK

Intelligent Systems and the Semantic Web

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Smart Software vs. Smart Data

John McCarthy defines Artificial Intelligence as

• science and engineering of
• making intelligent machines
• 1. Smart software: e.g., finding

insights and patterns

• 2. Smart data: data annotated with

linkable and sharable ontological vocabulary

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Semantics Makes Data Smarter

Three key steps:

• 1. Map data into RDF format
• 2. Annotate RDF data with

vocabulary defined in OWL

• ntologies
• TBox: def. of vocabulary
• ABox: annotated data
• 3. Merge the annontated data

and query with SPARQL

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[Diagram credit: Ivan Herman]

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Linked Open Data

[Photo source: talis.com]

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Schema.org

Official OWL ontology: http://schema.org/docs/schemaorg.owl HTML microdata: http://www.w3.org/TR/microdata/

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Annotating deep database data with open vocabulary

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Can We Reuse Closed Data As Open Data?

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Open vs. Closed World Assumptions

OWA (ontologies only cover key aspects of the world )

• Is Pepper Salad SpicyFood?
• UNKNOWN

CWA (complete information about the world)

• Is Pepper Salad SpicyFood?
• No, because
• "SpicyFood={Curry Chicken,

Spicy Grilled Shrimp}"

Food Note Curry Chicken Spicy Salmon Fillet Spicy Grilled Shrimp Spicy Pepper Salad

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Explicit CWA vs. Implicit CWA

Is Spicy Grilled Shrimp the only SpicyFood?

• SpicyFood={Spicy

Grilled Shrimp}?

• No, because of

background knowledge:

• "MinorSpicyFood" is

SpicyFood

CWA should support necessary reasoning

Food Note Curry Chicken Minor Spicy Salmon Fillet Spicy Grilled Shrimp Spicy Pepper Salad Vege

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In general, it uses an OWA setting

• Assuming ontologies only cover

key aspects of the world

Local Closed World Assumption (LCWA)

For certain parts, it allows CWA

• Assuming one has complete

knowledge about the part of the world

• Implicit CWA should be allowed

LCWA is more general

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Combining Open and Closed World Assumptions: Existing Solutions and Standards

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SPARQL

Does not close ontological vocabulary. Walk around: Realised by testing for the absence of a pattern in a graph

Get all food not known to be spicy

Problem: curry chicken is inlcuded in the answer set (if we use SPARQL without reasoning)

SELECT ?dish WHERE { ?dish rdf:type Food . FILTER NOT EXISTS { ?dish rdf:type SpicyFood} }

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DBox [Seylan et al., 2009]

Accommodate a DB component in an ontology

• TBox: schema axioms
• ABox: data axioms
• DBox: fixes the extensions of

DBox predicates

Faithful encoding of database

• usually with unique name

assumption (UNA)

• Does not allow implicit CWA

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Epistemic Operators

Used in e.g. MKNF (Minimal Knowledge and Negation as Failure) [Motik and Rosati, 2010]

• The K operator: things we know
• K Vege: the concept of all known Vege in the ontology
• The not operator: Negation as Failure
• not A is equivalent to ¬(K A)
• Example

PepperSalad: not(Spicy) meaning PepperSalad is not evidently (not known to be) Spicy

MKNF increases the complexity of reasoning

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Negation as failure Box (NBox) [Ren et. al, 2010]

• 1. To allow inference w.r.t. the closed classes

and properties

• O=(T, A, N)
• N ={SpicyFood, VegeFood} is the NBox in O
• 2. To provide restricted forms of the K and not
• perators for non-monotonic reasoning
• so that it does not increase the complexity of

reasoning for OWL 2 DL

15 SpicyFood VegeFood Minor Spicy Food

• rder

Vegetarian Guest Food some some

Jeff Yuan Jek Yuting Chicken Pepper Salmon Shrimp

NBox Reasoning

(T, A, N) |= x:¬B iff (T, A) |≠ x:B

• E.g., Salmon is neither VegeFood, nor

SpicyFood

¬B is equivalent to not B B is equivalent to K B Using classical reasoning to retrieve instance of predicates

• E.g., Pepper is VegeFood

Using nominals to close predicates

• E.g., VegeFood = {Pepper Salad}

Adding axioms back to ontology for incremental reasoning

• Yuting orders Pepper!
• rder

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Challenges for NBox Reasoning

Challenge 1: Ontologies with nominals are harder to reason with

• Using approximate reasoning technologies [Ren et. al,

2010b] to reduce to a tractable DL

• Identify safe consitions for tractable DLs, such as EL

and DL-Lite [Lutz et al. 2012]

Challenge 2: Incremental reasoning is usually difficult for expressive DLs

• EL supports tractable incremental reasoning services!

[Ren and Pan, 2011]

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Example: Approximate Reasoning [Ren et al. 2010]

Additional completion Rules (on top of the EL ones), e.g.

• Handling complement
• E.g. B subClassOf C => ¬C subClassOf ¬B
• Handling cardinality
• E.g. A subClassOf >= 3 r. B => A subClassOf >= 2 r. B
• Soundness preserving and tractable

ALL r B A C ALL D Some r nB A nC Some D B C X1 X2

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15/06/2012 ¡ 10 ¡ Approximate reasoning [AAAI2007, AAAI2010, AAAI2012]

TrOWL: a tractable semantic reasoning infrastructure

Parallel reasoning [JIST2011] Stream reasoning [CIKM2011] Local closed world reasoning (NBox) [JTS]

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• Fine-grained integration of closed

data and open data

• Connecting Semantic Intranets

(Islands) to the Semantic Web

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Benefits of Local Closed World Assumption in NBox

[Photo credit: http://www.tapeka.com]

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Take Home: Open the Doors for Data Integration We Need to Know How to Close Some Doors When Needed

[Photo source: eatmyzombie.com, www.dan-dare.org]

Where to find more information

• [For key references] Jeff Z. Pan. “Closing Some Doors for the

Open Semantic Web”. In Proceeding of 2nd International Conference on Web Intelligence, Mining and Semantics. 2012.

• [For further discussions] Jeff Z. Pan. Local Closed World

Reasoning in OWL 2. Tutorial inThe First Joint International Semantic Technology Conference (JIST2011). Hangzhou, China, 4 Dec, 2011.

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“Closing” Some Doors for the Open Semantic Web

WIMS2012

Thank you

… questions?