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Syllabus
Assigns
Notes
Labs
Project
Resources
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Advisor(s)
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Topics and Homepages of
Teams
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Abstracts and URL Starting
Points
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Nick Bassiliades,
Efstratios Kontopoulos,
Harold Boley
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Team 1 From POSL to d-POSL: Making the Positional-Slotted
Language Defeasible
Usman Ali,
Daniel Latimer,
Hanin Almutairi
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The Positional-Slotted Language (POSL) provides a
human-oriented syntax for a subset of RuleML/XML. POSL is specified in EBNF
and as an ANTLR grammar. A POSL<-->RuleML converter relieves users
from writing RuleML in XML. Some rule engines (including OO jDREW) use POSL
for rule and query editing. Defeasible RuleML (d-RuleML) permits the
prioritization of rules and the presence of non-monotonicity. The current
POSL 0.91 shall be upgraded to POSL 1.0 (crucial difference: separator
between a variable and its type changed from ":" to
"^^", e.g. from ?product:EBizProd to ?product^^EBizProd) and
extended (with IRI namespace prefixes, built-in calls, etc.), in sync with
RuleML 1.0.. On top of this, Defeasible POSL (d-POSL) shall be upgraded
from 0.91 to 1.0. An expert, Efstratios (Stratos) Kontopoulos, and software
tools are available as part of a collaboration between RuleML and Aristotle
University Thessaloniki.
POSL 1.0 and d-POSL 1.0 will be made available, open source, e.g. via the
RuleML website.
POSL:
0.85: http://ruleml.org/submission/ruleml-shortation.html
(contains many features, although
not up-to-date)
0.91: www.cs.unb.ca/~boley/cs6795swt/poslintweb-talk.pdf
http://ruleml.org/oojdrew/download.html (POSL.g and
POSLParser-Java.g)
1.0: http://ojs.academypublisher.com/index.php/jetwi/article/view/0204343353
(POSL
grammar in appendix)
d-POSL:
www.cs.unb.ca/~boley/talks/RuleResponderAgentsVO-2010-09-10.ppt (slide 43)
http://lpis.csd.auth.gr/systems/resources.html (links to code for the d-POSL editor)
http://www.sciencedirect.com/science/article/pii/S0950705110001735
POSL-RuleML converter (in Java
Web Start):
http://www.ruleml.org/posl/converter.jnlp
http://ruleml.org/oojdrew/demo.html
ANTLR: http://www.antlr.org
Source
code of previous project:
http://lpis.csd.auth.gr/systems/dr-device/d-POSL.rar
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Alexandre Riazanov,
Harold
Boley
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Team 2 Converting PSOA
RuleML to TPTP Format for FOL-Level Implementation
Gen Zou,
Reuben Peter-Paul
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Positional-slotted, object-applicative (psoa) terms
generalize and integrate W3C RIF’s F-logic-like positional and
slotted (named-argument) terms as well as its frame terms and class
memberships. The syntax and semantics of psoa terms and rules over them are
defined as PSOA RuleML in the style of RIF-BLD. The semantics blends slot
distribution, as in F-logic and RIF (as
well as tuple distribution), with integrated psoa terms, as in POSL and
RuleML. The planned two-part PSOA RuleML implementation (1) converts PSOA
RuleML’s RIF-BLD-like syntax to TPTP format, and (2) reads and
executes the TPTP with the VampirePrime reasoner. To address part (1),
building on Alexandre Riazanov’s existing converters from RIF and OWL to TPTP, a converter from PSOA RuleML to
TPTP shall be conceived. An appropriate representation of psoa terms and
rules in First Order Logic (FOL) shall be devised. On this basis, the RIF-to-TPTP converter shall be developed into a
(restricted) implementation of the PSOA-to-TPTP converter. It shall be
explored using the examples in the PSOA RuleML paper, extended to a more
complete psoa test suite. The test suite can also be used to probe part (2)
of the evolving PSOA RuleML implementation.
The PSOA-to-TPTP converter and psoa test suite will be
made available, open source, e.g. via the RuleML website.
PSOA RuleML (with various examples):
www.cs.unb.ca/~boley/talks/SemanticsPsoaRules-talk-UNB2011.pdf
http://www.cs.unb.ca/~boley/papers/SemanticsPsoaRules.pdf
TPTP format:
http://www.cs.miami.edu/~tptp
RIF-to-TPTP converter
and VampirePrime reasoner (experimental):
http://riazanov.webs.com/software.htm
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Tara Athan,
Harold Boley
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Team 3 A Benchmark
Suite for RuleML
XYZ
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RuleML
comprises a family of sublanguages, starting with Datalog and other Derivation
Rules and proceeding to FOL and beyond. Collections of test and use cases
have been developed across RuleML versions for various purposes, including
for validation, transformation, and execution. The current examples of
rulebases (including test queries) in Derivation RuleML shall be developed
for, or complemented by, test cases to achieve a comprehensive Benchmark
Suite. In the spirit of RuleML 1.0 being a ‘Rosetta Stone’
release, these RuleML 1.0 instance documents shall be formatted in an easy-to-read
standard (pretty-print-indented) manner such that they can be validated in
both XML Schema and Relax NG. Hence they shall be double-validated with XSV
(XML Schema) and Validator.nu (Relax NG). The Benchmark Suite shall be
executed in any rule engine compliant to RuleML, e.g. in OO jDREW or Prova.
Results of these validations and executions shall be documented (including
with a few screenshots).
The Benchmark Suite will be made available, open source,
via the RuleML website.
RuleML
1.0:
www.cs.unb.ca/~boley/talks/RuleML-Overarching-Talk.pdf
RuleML
Systematics:
http://wiki.ruleml.org/index.php/RuleML_Systematics
Examples
(grouped according to sublanguages):
http://ruleml.org/1.0/exa
The Wine Ontology (for
benchmarking RuleML and POSL tools):
http://ruleml.org/usecases/wineonto
Earlier benchmarks (showing OO jDREW speedup):
http://wiki.ruleml.org/index.php/OO_jDREW:Benchmarks
Prova:
http://www.prova.ws
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Tara Athan,
Harold Boley
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Team 4 Normalizers
for RuleML 1.0 in
XSLT 2.0
Ao Cheng,
Nada Alsalmi,
Thea Gegenberg,
Leah Bidlake,
Emily Wilson
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RuleML 1.0 rules and queries are marked up (serialized) in an
‘object-oriented’ flavor of XML, where XML trees alternate
elements that act as Classes/Types (Nodes) or methods/roles (edges).
There are various equivalent ways of such (Node-edge-…
-Node-edge-Node-)‘striped’ XML serialization, even apart from
logical equivalences. For example, the <Implies> node (the
main element for rules) normally contains an <if> and a <then>
edge (subelements for the rule premise and conclusion, respectively). But,
when written in this natural ‘if-then’ sequence, the
left-to-right order of XML subelements can be relied upon, and the stripe
of <if> and <then> edges may be omitted. Normalizers for
RuleML/XML are under development for transforming such
‘stripe-skipped’ serializations back to ‘fully striped’
ones. The current XSLT-based normalizers shall be used for a suite of
systematic sample transformations over rules and queries from any knowledge
domain, and shall be further developed. For example, transform a
subset of RuleML instances that are almost normal in a particular manner:
a) some stripes skipped, but elements in the canonical order;
b) stripes all present, but some elements out of canonical order.
The results will be made available, open source, via the
RuleML website.
XSLT:
http://www.xfront.com/rescuing-xslt.html
http://www.cafeconleche.org/books/bible3/chapters/ch15.html
http://www.w3.org/2005/08/online_xslt
Previous normalizer:
http://ruleml.org/1.0/#XSLT-Based%20Normalizer
Normalizer
issues:
http://wiki.ruleml.org/index.php/Relax_NG#Normalizer_Issues
Partial XSLT normalizer for RuleML 0.91:
http://ruleml.org/0.91/xslt/normalizer/091relaxed-to-normal.xslt
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Bruce Spencer,
Tara Athan,
Ben Craig,
Harold Boley
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Team 5 Extending OO jDREW for
RuleML 1.0
Christian
Fabbricatore,
Markus Zucker,
Nasser Albunian,
Khalid Almutairi,
Omar Alsaiari
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The
Object Oriented Java Deductive Reasoning Engine for the Web (OO jDREW)
currently executes queries over rules for a subset of RuleML 0.91. This
shall be extended for RuleML 1.0 in Java. A detailed description is
available separately.
The rule engine will be made available, open source, via
the jDREW website.
OO
jDREW as a reference implementation of RuleML:
www.jdrew.org/oojdrew/OOjDREW-RefImp-talk.ppt
http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?action=shwart&index=an&req=8914311&lang=en
jDREW introduction:
http://www.cs.unb.ca/~boley/cs6795swt/cs6795swt-jDREW.pdf
http://clip.dia.fi.upm.es/Conferences/Colognet/ITCLS-2002/PAPERS/BruceSpencer.pdf
OO
jDREW homepage:
http://www.jdrew.org/oojdrew
OO
jDREW wiki:
http://wiki.ruleml.org/index.php/Main_Page#OO_jDREW
OO
jDREW on SourceForge:
http://sourceforge.net/projects/oojdrew/?_test=beta
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Tara Athan,
Harold Boley
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Team 6 Generating
a Library of RuleML 1.0 Schemas
XYZ
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The
RuleML 1.0 schemas in Relax NG consist of ‘backbone’ of named
schemas enriched by a large number of parameter-defined schemas
customizable via a schema generator, MYNG. This tool, written by Tara Athan
in PHP, presents itself as a dashboard-like GUI, where users enter
parameters of ten categories/facets describing what their RuleML 1.0
sublanguage ought to express. It then generates the desired schema in the
Relax NG Compact (RNC) schema syntax, uniquely identified by a
(query-parameterized) URL. For example, while there is an XSD-specified
RuleML 1.0 sublanguage of Datalog with all relations restricted to being
binary (bindatalog), the tool-supported schema parameterization permits the
RNC-specified RuleML 1.0 to customize all sublanguages (not only Datalog)
with such a binary restriction. The tool shall be used to systematically
generate relevant RuleML 1.0 sublanguages in RNC, which shall then be used
to validate sample instances. Based on this, a RuleML 1.0 Schema Library of
interesting RNC specifications shall be built, tested, and illustrated with
examples from any knowledge domain.
The Schema Library will be made available, open source,
via the RuleML website.
RuleML
schema modularization (including bindatalog):
http://ruleml.org/modularization
Paper
on Customization of RuleML in Relax NG:
www.cs.unb.ca/~boley/papers/RuleMLinRelaxNG.pdf
Relax
NG and PHP-implemented schema-parameterization tool:
http://wiki.ruleml.org/index.php/MYNG
http://wiki.ruleml.org/index.php/MYNG#PHP_Parameterized_Schema
http://ruleml.org/1.0/myng
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Harold
Boley,
Zhili
Zhao
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Team 7 Rule Enhancement of
Personal Health Records
Shirin Ghorbani,
Peijian Ju,
Mahta Moattari,
Jianbo Zheng
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A Personal Health Record (PHR) is a collection of health
information maintained by an individual about themselves. A Web-based PHR
can enhance structured (e.g., relational) health data and unstructured
(e.g., English) documents with semi-structured Social Semantic Web
knowledge representations for health information. In particular, Web rule
enhancement can express an individual’s knowledge about their
allergies, drug interactions, prophylactic measures, etc. Rules for the
formation of patient support groups can also express profile knowledge
about communication constraints such as time, location, age range, gender,
and number of participants. The PatientSupporter instantiation of Rule
Responder is an early prototype of such rule enhancement in a multi-agent setting.
It explores the medical subarea of sports injuries structured by a
partonomy of affected body parts. Drawing also on other Rule Responder
instantiations such as SymposiumPlanner, PatientSupporter shall be upgraded
for RuleML 1.0, its vocabulary of properties shall be refined, its profile
knowledge shall be combined with other PHR knowledge, and it shall be
explored in a different medical subarea.
The resulting PatientSupporter2 will be made available, open source, via
the RuleML website.
PHRs:
http://en.wikipedia.org/wiki/Personal_health_record
PatientSupporter description (including slides and paper
with references):
http://ruleml.org/PatientSupporter/documentation.html
PatientSupporter
implementation:
http://ruleml.org/PatientSupporter/RuleResponder.html
Rule Responder:
http://ruleml.org/RuleResponder
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Michalis Vafopoulos,
Zhili
Zhao,
Harold
Boley
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Team 8 BuyerRelations: Buyer-Centric
Product Filtering with GoodRelations/eClass and RuleML
Ramanpreet Singh,
Marine Feer,
Sen Wang,
Jingjing Li,
Chirag Sharawat
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Seller-centric
product recommendation rules have been realized in various RuleML projects,
and an early Rule Responder design for buyer-centric (eLearning) product
filtering rules is available. GoodRelations is a lightweight, generic
Semantic Web vocabulary for expressing all typical aspects of offers about
products and services on the Web. BuyerRelations shall bring Rule Responder
and GoodRelations together as follows: BuyerRelations shall be an
instantiation of Rule Responder for GoodRelations offer filtering that is
built, tested, and illustrated in any (non-eLearning) product or service
domain. Offers and products/services shall be described, respectively, with
the GoodRelations vocabulary and the eClassOWL ontology. Buyers in the
chosen domain shall be organized as a Rule Responder virtual organization.
Incoming offers from sellers shall be pre-filtered by BuyerRelations’
Organizational Agent (OA), which shall dispatch good candidate offers to
the potentially most interested BuyerRelations Personal Agent (PA), which
assists a human buyer. This PA shall use its local RuleML rule base to
decide whether the offer may be interesting for its human owner, perhaps
with modifications, sending its decision result back to the OA, hence to
the seller.
BuyerRelations will be made available, open source, via
the RuleML website.
Product
filtering rules:
http://www.learningresponder.yolasite.com
WellnessRules:
http://ruleml.org/WellnessRules
PatientSupporter:
http://ruleml.org/PatientSupporter
Rule Responder Guide:
http://ruleml.org/RuleResponder/RuleResponderGuide
GoodRelations:
http://www.heppnetz.de/projects/goodrelations
http://www.heppnetz.de/projects/goodrelations/primer
eClassOWL:
http://www.heppnetz.de/projects/eclassowl
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