Here are my notes from Thursday at www.semantic-conference.com

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Thursday, 6/18/2009

Conference Attendance: 1170

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Keynote General Session 8:30AM - 09:30AM

Semantics at The New York Times
Larson, Robert, New York Times Company
Sandhaus, Evan, New York Times Company
Conners, Christine, moderator
Business / Non-Technical
Publishing

What the Times means by Semantics
- Any tool that discovers/leverages metadata to 
  make content more accessible

The Morgue
- File cabinets of clippings, photos,... of Times and other papers
- Reporter seeking info on story would go to morgue and search

The New York TImes Index
- Where info can be found in printed version

NYTimex.com
- MS Word   -> Newspaper CMS -> tagging software -> web cms
  reporters    editors           section, pagen
  links         byline            newsdesk
                type
                biographic
- Topic pages
  Aggregate from NYT and other locations

Future
- Improve workflow
- Digitize 5 million photos from morgue
- tag NYT blogs
- Tag user content
- Allow users to tag

Times APIs
- Times Tag
- Article Search API

New York Times Annotated Corpus
- DVD of every article+metadata 1987-2007
- corpus.nytimes.com
- non-commerical: $300

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Conference Sessions 9:45AM - 10:45AM

Visualizing RDF
Franz - Gruff

Kieth Sutton - silvafug.org - Silicon Valley Flex User Group

www.diagramic.com
EX: www.forbes20.com, www.diagramic.com/v/?p=google

FlexParts (Adobe AIR application)
- Visualizing Freebase data

SEED FOR NEXT YEAR'S CONFERENCE:
- Visualizing RDF with JavaFX

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Conference Sessions 11:00AM - 12:00PM

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SQUIN 
squin.org
query the web of linked data
Uses SINDICE
EX usage: turn2live.com

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Practical OWL 2 RL Reasoning Via Fast Forward Chaining Inference Engines
Matheus, Christopher John, VIStology, Inc.
Technology - Intermediate
Foundational Topics

OWL 2 RL
- Implemented by rule-based forward chaining inference engines
- Implemented as collection of 75+ implication and consistency rules

Unsupported/Restrictions
- owl:cardinality, owl:minCardinality
- owl:qualifiedCardinality owl:qualifiedMinCardinality
- owl:DisjointUnion
- owl:ReflexiveObjectProperty
- owl:topObjectProperty, owl:bottomDatatypeProperty
- owl:topDataProperty, owl:bottomDataProperty
- xsd:real, wsd:rational
- URI cannot be both a class and an individual
- owl:maxCardinality restricted to 0 & 1
- Data ranges restricted to sxd datatypes and intersection of them
- Axioms designed to avoid inferring existence of individuals
  not explicitly mention in knowledge base
- Does not include axiomatic triples (but impls may include them)
  (e.g., characteristics of elements of the language)

Types of inferencing
- Entailment
- Classification (entailment of rdf:type facts)
- Consistency checking

P rdfs:domain C
I P V
-----
I rdf:type C
(just by giving I property P then you can infer its type)


B rdfs:subClassOf A
I rdf:type B
------------
I rdf:type A


parentOf owl:inverseOf childOf
John parentOf Diana
------------------
Diana childOf John


C owl:hasValue V
C owl:onProperty P
I rdf:type C
------------
I P V


C owl:hasValue V
C owl:onProperty P
I rdf:type C
------------
I rdf:type C

C1 owl:disjointWith C2
I rdf:type C1
I rdf:type C2
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INCONSISTENCY

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Ideas for Future Semantic Applications 	
Sarris, Tony, Unisys Corporation - Systems & Technology
Sweeney, Peter, Primal Fusion
Business / Non-Technical
Business & Marketplace

PrimalFusion

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Product Seminars/Field Trips 1:00PM - 5:00PM

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A Programmer's Introduction to Pellet: How to Build Ontology-based Semantic Applications 	
Sirin, Evren, Clark & Parsia LLC
Smith, Michael A, Clark & Parsia LLC
Technology - Advanced
Semantic Applications

clarkparsia.com/pellet/tutorial

What is Pellet? 
- OWL-DL reasoner 
- Supports nearly all of OWL 1 and OWL 2 
- Sound and complete reasoner 
- Written in Java and available from  http://clarkparsia.com/pellet 

Talk Coverage 
- Introduction to reasoning with Pellet 
  Basic reasoning concepts 
  Using Pellet from command-line 
- Programming with Pellet 
  Available APIs and usage 
  Reasoning and query answering 
  Explaining inferences 
- Advanced Pellet features 
  Closed-world data validation with Integrity Constraints

Running Example: POPS 
- Expertise location in a large organization 
  Based on POPS application in NASA 
  Multiple sources containing personnel data: contact 
   information, work history, evidence of skills, 
   publications, etc. 
  Find people that satisfy certain conditions 
- Several challenges 
  Integrate data from multiple sources 
  Ensure data consistency 
  Query with inferencing 
  User interface (not covered in this talk; see jSpace) 

Demo of JSpace/POPS

Building the Example 
- Author ontology schemas 
  Validate and debug schema definitions 
- Connect multiple schemas 
  Simple ontology alignment 
- Validating instance data 
  Identify and resolve inconsistencies in the data 
  Closed world data validation with Pellet Integrity 
  Constraints 
- Reasoning with instance data 
  Answer queries over combined data using Pellet 
  Scalability and performance considerations

OWL in 3 Slides (1) - ENTITIES 
- Class: Person, Organization, Project, Skill, ... 
- Datatype: string, integer, date, ... 

- Individual: Evren, C&P, POPS, ... 
- Literal: "Evren Sirin", 5, 5/26/2008, ... 

- Object Property: worksAt, hasSkill, ... 
- Data property: name, proficiencyLevel, ...

OWL in 3 Slides (2) - EXPRESSIONS 
- Class expressions 
  and, or, not 
  some, only, min, max, exactly, value, Self 
  { ... } 
 
- Datatype definitions 
  and, or, not 
  <, <=, >, >= 
  { ... }  

OWL in 3 Slides (3) - AXIOMS 
- Class axioms 
  subClassOf, equivalentTo, disjointWith 
 
- Property axioms 
  subPropertyOf, equivalentTo, inverseOf, 
  disjointWith, subPropertyChain, domain, range 
 
- Property characteristics 
  Functional, InverseFunctional, Transitive, 
  Symmetric, Asymmetric, Reflexive, Irreflexive 
 
- Individual assertions 
  Class assertion, property assertion, sameAs, 
  differentFrom

Reasoning in OWL 
1. Check the consistency of a set of axioms 
   Verify the input axioms do not contain contradictions 
   Mandatory first step before any other reasoning service 
   Fix the inconsistency before reasoning 
     Any consequence can be inferred from inconsistency 
2. Infer new axioms from a set of axioms 
   Truth of an axiom is logically proven from asserted axioms 
   Infinitely many inferences for any non-empty ontology 
   Inferences can be computed as a batch process or as 
     required by queries

Classification
- build tree of subClass relationships

Realization
- assign individuals to their most specific class membership

Protege 4 does not come with Pellet built in
- A plugin is available
- Use protege "check for plugins"