Intelligent Information Systems on the Web and in the Aether

1
Intelligent Information Systems on the Web and
in the Aether

• Tim Finin
• University of Maryland Baltimore County
• Joint work with Scott Cost, Benjamin Grosof
  (MIT), Anupam Joshi, Jim Mayfield (JHU), Charles
  Nicholas, Yun Peng, Yelena Yesha many students.
• MAY 2002
• This work was partially supported by DARPA
  contract F30602-97-1-0215, NSF grants CCR007080
  and IIS9875433 and grants from IBM, Fujitsu and
  HP.

recommend
2
Overview

• The Problem building intelligent information
  systems
• The Semantic web as part of the solution
• Some work at UMBC
• Comments and Conclusions

3
The problem

• Ive been engaged in research aimed at developing
  intelligent information systems for thirty years.
• The problem is hard, progress is slow, but the
  incremental results are worth it.
• Its a task for many generations to come.
• Todays environment is very different than that
  in 1971.

4
They way we were

• AN IBM 360 circa 1971

5
They way we will be
6
Whats new?

• Internet. Virtually of the computers in the world
  have been connected.
• Scale. Every day many more computing and
  communication devices are joining.
• Power. Raw computing power continues to climb.
• Wireless. New technologies (GSM, 802.11,
  Bluetooth, UWB?, IR, etc) are creating a
  pervasive, ubiquitous computing environment
• Web. The web is like Dennetts "universal acid,
  a mythical chemical that eats through -- and thus
  transforms -- everything in its path.

7
IDM Challenges

• The environment makes new demands and offers new
  challenges, enough to keep all of us busy, e.g.
• Very open environments
• Large and diverse community of service and
  content providers
• Lots of relative autonomy
• Dynamic ad hoc networks
• Systems with widely varying resources --
  bandwidth, connectivity, cpu, memory, disk,
  power, software, knowledge, intelligence, etc.

8
Research topics

• Concepts which can address these challenges
  include
• Multiagent systems
• Information and knowledge sharing through common
  representation languages, ontologies and
  protocols
• Automatic service description, discovery,
  composition
• Negotiation for services and information
• Trust based models for authorization, credibility
  and security
• Social and norm governed behavior
• Delegation and degrees of autonomy
• Coordination and teamwork models

9
Semantic Web

• Ill argue that the semantic web provides a good
  approach, language and tools to support the
  development of intelligent information systems in
  this environment.
• This isnt obvious, since the SW seems grounded
  in the traditional wired web.
• But, the principles which drive it are the right
  ones for agents as well as pervasive computing.
• And, by grounding agents in web technology, they
  may make it out of the lab.
• Next overview of Semantic Web

10
Origins of the Semantic Web

• Tim Berners-Lees original 1989 WWW proposal
  described a Web of relationships among named
  objects that unified many info. management
  tasks.
• Guha designed MCF at Apple (94)
• XMLMCFgtRDF (96)
• RDFOOgtRDFS (99)
• RDFSKRgtDAMLOIL (00)
• W3Cs SW activity (01)
• W3Cs OWL (02?)

• http//www.w3.org/History/1989/proposal.html

11
W3Cs Semantic Web Goals

• Focus on machine consumption
• "The Semantic Web is an extension of the current
  web in which information is given well-defined
  meaning, better enabling computers and people to
  work in cooperation." -- Berners-Lee, Hendler and
  Lassila, The Semantic Web, Scientific American,
  2001
• The current Web stores things whereas the
  semantic Web does things.

12
Semantic Web does what?

• Concept-based search
• ? keyword-based search
• Semantic navigation ? link-based navigation
• Personalization
• ? one size fits all
• Query answering
• ? document retrieval
• Services
• ? CGI calls, but service-description languages,
  negotiation, service composition, etc

13
Why is this hard?
This is what a web page looks like to a machine
And understanding natural language is not as hard
as understanding the images!
after Frank van Harmelen and Jim Hendler
14
OK, so HTML is not helpful
Could we tell the machine what the different
parts of the text represent?
after Frank van Harmelen and Jim Hendler
15
XML to the rescue?
Some XML fans claim this could be done by adding
meaningful tags to parts of the text
after Frank van Harmelen and Jim Hendler
16
XML ? machine accessible meaning
But to your machine, the tags still look like
this.
after Frank van Harmelen and Jim Hendler
17
Schemas take a step in the right direction
Schemas help.
by relating common termsbetween documents
lt CV gt
private
after Frank van Harmelen and Jim Hendler
18
But other people use other schemas
Someone else has one like this.
after Frank van Harmelen and Jim Hendler
19
The semantics isnt there
lt CV gt
which dont fit in
private
after Frank van Harmelen and Jim Hendler
20
Ontologies can help

• An ontology defines the terms used to describe
  and represent an area of knowledge.
• Ontologies are used by people, databases, and
  applications that need to share domain
  information (a domain is just a specific subject
  area or area of knowledge, like medicine, tool
  manufacturing, real estate, automobile repair,
  financial management, etc.). Ontologies include
  computer-usable definitions of basic concepts in
  the domain and the relationships among them ...
• They encode knowledge in a domain and also
  knowledge that spans domains.
• In this way, they make that knowledge reusable.
• Working Draft, Web Ontology Working Group.

21
Ontologies can help
Thesauri narrower term relation
Disjointness, Inverse,part of
Frames (properties)
Formal is-a
Catalog/ID
General Logical constraints
Formal instance
Informal is-a
Value Restriction
Terms/ glossary
SimpleTaxonomies
ExpressiveOntologies
After Deborah L. McGuinness (Stanford)
22
By providing external referents to merge on
SW languages addmappings and structure.
nme
CV
CV
work
vate
CV
educ
educ
after Frank van Harmelen and Jim Hendler
23
TBLs semantic web vision
The Semantic Web will globalize KR, just as the
WWW globalize hypertext -- Tim Berners-Lee
you arehere
24
Semantic web languages today

• Today there are just two semantic web languages
• DAML Darpa Agent Markup Languagehttp//www.daml
  .org/
• RDF Resource Description Frameworkhttp//www.w3
  .org/RDF/
• and one under development by the W3C
• OWL Ontology Web Languagehttp//www.w3.org/2001
  /sw/
• with more to come, IMHO

25
RDF is the first SW language
Graph
XML Encoding
ltrdfRDF ..gt lt.gt lt.gt lt/rdfRDFgt
RDF Data Model
Good For HumanViewing
Good for MachineProcessing
Triples
stmt(docInst, rdf_type, Document) stmt(personInst,
rdf_type, Person) stmt(inroomInst, rdf_type,
InRoom) stmt(personInst, holding,
docInst) stmt(inroomInst, person, personInst)
Good For Reasoning
26
Simple RDF Example
http//umbc.edu/finin/talks/idm02/
dcTitle
Intelligent Information Systemson the Web and
in the Aether
dcCreator
bibAff
http//umbc.edu/
bibemail
bibname
finin_at_umbc.edu
Tim Finin
27
XML encoding for RDF
ltrdfRDF xmlnsrdf"http//www.w3.org/1999/02/22-r
df-syntax-ns" xmlnsdc"http//purl.org/dc/el
ements/1.1/" xmlnsbib"http//daml.umbc.edu/o
ntologies/bib/"gt ltdescription about"http//umbc.e
du/finin/talks/idm02/"gt ltdctitlegtIntelligent
Information Systems on the Web and in the
Aetherlt/dcTitlegt ltdccreatorgt
ltdescriptiongt ltbibNamegtTim
Fininlt/bibNamegt ltbibEmailgtfinin_at_umbc.edult/
bibEmailgt ltbibAff resource"http//umbc.ed
u/" /gt lt/descriptiongt lt/dcCreatorgt lt/descr
iptiongt lt/rdfRDFgt
ian_at_goo.org
28
N triple representation

• RDF expressions can also be encoded as a set of
  triples.
• ltsubjectgt ltpredicategt ltobjectgt .
• lthttp//umbc.edu/finin/talks/idm02/gt
  lthttp//purl.org/dc/elements/1.1/Titlegt
  "Intelligent Information Systems on the Web and
  in the Aether" .
• _j10949 lthttp//daml.umbc.edu/ontologies/bib/Name
  gt "Tim Finin" .
• _j10949 lthttp//daml.umbc.edu/ontologies/bib/Emai
  lgt "finin_at_umbc.edu" .
• _j10949 lthttp//daml.umbc.edu/ontologies/bib/Affgt
  lthttp//umbc.edu/gt .
• _j10949 lthttp//www.w3.org/1999/02/22-rdf-syntax-
  nstypegt ltDescriptiongt .
• lthttp//umbc.edu/finin/talks/idm02/gt
  lthttp//purl.org/dc/elements/1.1/Creatorgt
  _j10949 .
• lthttp//umbc.edu/finin/talks/idm02/gt
  lthttp//www.w3.org/1999/02/22-rdf-syntax-nstypegt
  ltDescriptiongt .
• Note the generated ID for the anonymous node

29
Triple Notes

• RDF triples have one of two forms
• ltURIgt ltURIgt ltURIgt
• ltURIgt ltURIgt ltquoted stringgt
• Triples are also easily mapped into logic
• ltsubjectgt ltpredicategt ltobjectgt
• ltpredicategt(ltsubjectgt,ltobjectgt)
• With type(ltSgt,ltOgt) becoming ltOgt(ltSgt)
• Example
• subclass(man,person)
• sex(man,male)
• domain(sex,animal)
• man(adam)
• age(adam,100)
• Triples can be easily stored and managed in a
  DBMS

Note were not showing the actual URIs
for clarity
30
RDF Schema (RDFS)

• RDF Schema adds taxonomies for classesand
  properties
• subClass and subProperty
• and some metadata.
• domain and rangeconstraints on properties
• Several widely usedKB tools can importand
  export in RDFS

Stanford Protégé KB editor
31
RDFS supports simple inferences

• An RDF ontology plus some RDF statements may
  imply additional RDF statements.
• This is not true of XML.
• Example
• subproperty(mother,parent)
• domain(parent,person)
• range(parent,person)
• mother(eve,cain)

Implies parent(eve,cain) person(eve)
person(cain)
32
RDF is being used

• RDF is being used in a number of W3C
  specifications
• CC/PP (Composite Capabilities/Preference
  Profiles, http//www.w3.org/Mobile/CCPP/)
• P3P (Platform for Privacy Preferences Project,
  http//www.w3.org/P3P/)
• Other web standards
• RSS 1.0 (Rich Site Summary)
• RDF calendar ( iCalendar in RDF)
• Other systems
• Mozilla

33
RDF is not enough, but a good foundation

• RDF lacks expressive adequacy for many tasks
• Only range/domain constraints (on properties)
• No properties of properties (transitive, inverse
  etc.)
• No equivalence, disjointness, coverings, etc.
• No necessary and sufficient conditions
• No rules, axioms, logical constraints
• DAMLOIL extends RDF
• Layering makes partial knowledge available to
  applications which only understand RDF
• NB Building on RDF has some disadvantages

34
Were going down a familiar road

• KR trends
• 55-65 arbitrary data structures
• 65-75 semantic networks
• 75-85 simple frame systems
• 85-95 description logics
• 95-?? logic

• Web trends
• 95-97 XML as arbitrary structures
• 97-98 RDF
• 98-99 RDFS (schema) as a frame-like system
• 00-01 DAMLOIL
• 02-?? DAML-L

Only much faster!
35
DAMLOIL as a Semantic Web Language

• DAML Darpa Agent Markup Language
• DARPA program with 17 projects an integrator
  developing language spec, tools, applications for
  SW.
• OIL Ontology Inference Layer
• An EU effort aimed at developing a layered
  approach to representing knowledge on the web.
• Process
• Joint Committee US DAML and EU Semantic Web
  Technologies participants
• DAMLOIL specs released 01/01 03/01
• See http//www.daml.org/
• W3C SW activity started 08/01.

36
A Simple DAML Example

• ltrdfsClass about"Animal"/gt
• ltrdfsClass about"Plant"gt
• ltdamldisjointFrom
  resource"Animal"/gt
• lt/rdfsClassgt
• Note the mixture of rdf (plant and animal are
  classes) and DAML (plant and animal are disjoint)

37
DAMLOIL ? RDF

• DAMLOIL ontology is a set of RDF statements
• DAMLOIL defines semantics for certain statements
• Does NOT restrict what can be said
• Ontology can include arbitrary RDF
• But no semantics for non-DAMLOIL statements
• Adds capabilities common to description logics
• cardinality constraints, defined classes (gt
  classification), equivalence, local restrictions,
  disjoint classes, etc.
• More support for ontologies
• Ontology imports ontology
• But not (yet) variables, quantification, and
  general rules

38
DAML in One Slide
DAML is built on top of XML and RDF

• ltrdfRDF xmlnsrdf "http//w3.org/22-rdf-syntax-n
  s"
• xmlnsrdfs"http//w3.org/rdf-schema"
• xmlnsdaml"http//daml.org/damloilgt
• ltdamlOntology rdfabout""gt
• ltdamlimports rdfresource"http//daml.org/d
  amloil"/gt
• lt/damlOntologygt
• ltrdfsClass rdfID"Person"gt
• ltrdfssubClassOf rdfresource"Animal"/gt
• ltrdfssubClassOfgt
• ltdamlRestrictiongt
• ltdamlonProperty rdfresource"hasParent"/gt
  
• ltdamltoClass rdfresource"Person"/gt
• lt/damlRestrictiongt
• lt/rdfssubClassOfgt
• ltrdfssubClassOfgt
• ltdamlRestriction damlcardinality"1"gt
• ltdamlonProperty rdfresource"hasFather"/gt
  
• lt/damlRestrictiongt lt/rdfssubClassOfgt
  lt/rdfsClassgt
• ltPerson rdfabouthttp//umbc.edu/finin/"gt

It allows the definition, sharing, composition
and use of ontologies
DAML is a frame based knowledge representation
language
It can be used to add metadata about anything
which has a URI.
URIs are a W3C standard generalizing URLs
everything has URI
39
DAML-S

• DAML-S is an ontology for describing properties
  and capabilities of web services
• Desiderata
• Ease of expressiveness
• Enables automation of service use by agents
• Enables reasoning about service properties and
  capabilities
• Also appropriate for describing services in a
  mobile/pervasive computing environment
• See http//daml.org/services/

40
DAML-S components

• Service profile (what it does)
• For service registration, discovery and matching.
• High-level description of service and provider
  with a (human readable) description of service, a
  specification of functionalities provided and
  other functional attributes.
• Service model (how it works)
• For service invocation, composition,
  interoperation, monitoring.
• A service has inputs, outputs, preconditions and
  effects.
• Composite processes are build using sequence,
  if-then-else, fork, etc.
• Service grounding (how to access)
• Specification of service access information
  (communication protocols, transport mechanisms,
  etc.) which could be via SOAP, HTTP forms, Java
  RMI, RPC, etc.

41
SW is work in progress

• There are important language aspects which need
  more work rules, queries, etc.
• Many tools need to be created
• E.g., Protégé plug-in for DAMLOIL
• Applications need to be explored
• The W3C is developing a new SW language
• OWL Ontology Web Language

42
W3C Web OntologyWorking Group

• The WOWG is working to create arecommendation
  for the "Web Ontology Language" OWL
• 51 Members from 30 W3C Organizations
• Companies Agfa, Daimler-Chrysler, EDS, Fujitsu,
  Hewlett-Packard, IBM, Intel, IVIS, Lucent,
  Network Inference, Nisus, Nokia, Philips, Stilo,
  Sun, Unisys
• Public Sector DISA, Electricite de France,
  Intelink, INTAP, MITRE, NIST
• Research projects/Labs DFKI, FZI, Ibrow group,
  Stanford, U. Bristol, U. Maryland, U.
  Southhampton
• Invited Experts Medical, Digital Library,
  Defense, Technical
• CoChairs Jim Hendler, University of
  Maryland/MIND Guus Schreiber, Univ of
  Amsterdam/Ibrow
• http//www.w3.org/2001/sw/WebOnt/

43
OWL Goals

• The WOWG has identified the following goals in
  developing OWL
• Shared ontologies
• Ontology evolution
• Ontology interoperability
• Inconsistency detection
• Balance of expressivity and scalability
• Ease of use
• XML syntax
• Internationalization

44
KR meets the Web

• One way to think about the semanticweb is that
  we are creating a knowledge representation
  language for the Web.
• This is more than just selecting an appropriate
  KR language and selecting an XML encoding.
• The Web as an information system has many
  significant properties.
• Highly distributed
• Many content providers
• Dynamic
• Evolving
• Inconsistent

45
Semantic Web Principles

• Everything is on the web
• People, places, times, things all have URIs
• Partial information is assumed
• The web privileges scalability over integrity and
  theres always more and new stuff to find
• Trust models are critical
• Its not all true
• Support information evolution
• Content and consensus is dynamic
• Minimalist design
• Make the simple things simple, and the complex
  things possible. Standardize no more than is
  necessary.
• Common data model
• To support interoperability and knowledge sharing

Adapted from Eric Miller, W3C
46
Some UMBC applications

• (1) Semantic web and agents (ITTalks)
• (2) Information retrieval on the SW
• (3) Service discovery and composition in ad hoc
  mobile environments
• (4) Distributed trust

47
(1) ITALKS

• ITTALKS is a database driven website of IT
  related talks at UMBC andother institutions. The
  database contains information on
• Seminar events
• People (speakers, hosts, users, )
• Places (rooms, institutions, )
• Web pages with DAML markup are generated
• The DAML markup supports agent-based services
  relating to these talks.
• Users get talk announcements based on the
  interests, locations and schedules.

http//ittalks.org/
48
(No Transcript)
49
humanview
50
machineview
51
ITTALKS Architecture
ApacheTomcat
52
ITTALKS Ontologies

• Weve defined and use the following ontologies,
  all at http//daml.umbc.edu/ontologies/
• calendar-ont.daml calendar and schedule info
• classification.daml ACM CCS topics
• person-ont.daml people and their attributes
• place-ont.daml talk locations
• profile-ont.daml user modeling info
• talk-ont.daml talks info
• topic-ont.daml topics and interests

53
Two Advanced Capabilities

• Ill briefly describe two advanced capabilities
  facilitated by DAML
• Classifying talk topics and user interests using
  DAML ontologies
• Using DAML as a communication language among
  software agents

54
What are talks about?

• Topic hierarchies provide indexing terms
• ACM CCS topic hierarchy
• Open Directory
• Encoded as DAML ontologies
• These allow users to specify interests as well as
  browse the database of talks by topic
• Automatic classification of talks (based on title
  and abstract) and users (based on his web pages,
  CV, papers, etc.)
• Discovery of mapping rules between CCS to OD
  ontologies using IR techniques

55
Classifying Talks
uses
CMU Bowstatistical text analysis tools
ACM CCS classifier
uses
Training corpus
topics
e.g.5K ACMabstracts
56
Mapping between topic ontologies
T1
t1foo
CMU Bowstatistical text analysis tools
T1?T2mapper
T2
(t2bar, 0.8), (t2qux, 0.7),
57
Interactive ontology mapper

• Users create maps between ontologies with URIs to
  text describing classes properties.
• Automates mapping process, taking into account
  hierarchical relationships and user-specified
  landmark mappings.
• Text classification used to compute similarities
  between pairs of classes or properties.
• A probabilistic approach used to combine
  hierarchical information.

Used in XTalks to enable mappings
between Alternative topic ontologies in DAMLOIL
58
DAML and Agents

• Much multi-agent systems work is grounded in
  Agent Communication Languages (e.g., KQML, FIPA)
  and associated software infrastructure such as
  the DARPA Grid
• The paradigm has been peer-to-peer message
  oriented communication mediated by brokers and
  facilitators.
• The DAML program invites different paradigms
  which will require some changes in ACLs and their
  associates software systems.
• Agents publish beliefs, requests, and other
  speech acts on web pages.
• Agents discover what peers have published on
  the web.
• The software agent research community is very
  interested in the semantic web and DAML

59
10
1
mapquest
ITTALKS app
18
11
ITTALKSagent
Travelagent
Communicationprotocol
FIPA ACL
17
users daml profile
9
API
2
12
Calendaragent
Useragent
8
16
5
13
14
3
7
6
4
15
XSB DAMLOILReasoner
BrokerAgent
AgentNameServer
MS Outlook
Common agent infrastructure
MS Outlook
60
How does DAML Help?
DAMLOIL provided a uniform language which
met Many needs in developing a complex
application.
61
XTalks Personal Agent
External World
Xtalks Plugin
Mapquest Plugin
Buddy List Plugin
External Plugins
XSB
yajxb
Personal Agent Infrastructure
Plugin Manager
User Interface
User Model
Rule Engine Interface
COM Bridge
Jess
JADE platform

• XPA is a configurable personal agent which
  accepts FIPA messages from XTalks and other
  instances of XPAs as well as applications, e.g.
  MS Outlook.

62
Xtalks agents
1 Xtalks Announcement 2 User Agent
Solicitation
Xtalks Interface
3 Buddy List 4 Travel Planning
Mapquest Agent
FIPA Request Response Protocol
Xtalks System
Xtalks System
Scenarios 1,2
Periodic querying
Personal Agent (2)
Xtalks Agent
FIPA Request Response Protocol
Scenarios 3,4
Personal Agent (3)
Personal Agent (1)
63
Damlator translation engine

• Extensible engine for DAML-encoded Semantic Web
  pages translation and caching
• Currently supported output formats
• For humans GIF and PNG
• For agents DAML, NTriples, Prolog terms
• Caching supports scalability and efficiency
• Incorporated as an Apache-module
• Faster, application/user independent and
  system-wide availability
• Accessed via http//host/_at_SeeAs_at_/original/path/to/
  file.daml
• Similar to W3C RDF Validation Service
• Uses Jena RDF/XML Parser, Apache Xerces, ATT
  GraphViz
• Available from http//www.ittalks.org/download/

64
How does DAML Help?
DAMLOIL provided a uniform language which
met Many needs in developing a complex
application.
65
(2) Integrating Retrieval and Inference

• Problem How do we do information retrieval over
  documents and queries which combine free text and
  semantic web markup?
• IR systems and KB systems use different models
• One Solution (1) index both the text and markup
  and then (2) use existing IR systems to find
  documents that match queries
• Issues (1) How do we index markup? (2) When and
  where do we do inferencing over the markup?
• Applications (1) Improved recall and precision
  for IR systems, (2) Retrieving documents for
  question answering.

66
Student Event Scenario

• UMBC sends out descriptions of 50 events a week
  to students.
• Each student has a standing query used to route
  event messages.
• A student only receives announcements of events
  matching his interests and schedule.
• Use LMCOs AeroText system to automatically add
  DAMLOIL markup to event descriptions.
• Categorize text announcements into event types
• Identify key elements and add DAML markup
• Use JESS to reason over the markup, drawing
  ontology supported inferences

67
Event Ontology

• A simple ontology for University events
• Includes classes, subclasses, properties, etc.
• Can include instance data, e.g., UMBC, NEC,
  Fairleigh Dickenson, etc

68
IR Engine

• Were experimenting with two IR engines JHUs
  Haircut and UMBCs SIRE, using a similar process
  for both
• Convert DAML markup to RDF triples
• Infer additional triples which follow from model
• (S,type,O) (0,subclass,O2) gt (S,type,O2)
• Use domain specific rules to infer additional
  triples
• for a movie, retrieve genre property from IMDB
• Generate 7 indexing terms from each (S,P,O)
  triple
• SPO, SP, SO, PO, S, P, O
• Index free text and resulting triple terms

69
HOWLIR FRAMEWORK
Movie
Event information in plain text
AeroText Java
Expand Event Description
Agents
Sport
WEB
DAML/RDF
Generate RDF Triples
Event Categories
Markup
Talk
RDF Triples
. . .
Trip
Inference with DAMLJessKB
Expanded RDF Triples Free Text
Must
Query User Interface
Filter query on event property constraints
OK
JHUHAIRCUT IR Engine
Structured
Query
Must not
Events
Final Results
Inference on results
Results User Interface
70
DOCUMENT
ltDOCgt ltDOCNOgt'http//gentoo.cs.umbc.edu/howlir/ann
ouncements/charitycharity_001 lt/DOCNOgt ltTEXTgt'UMB
C Blood Drive!! Office of Student Life launches
its annual Blood Drive for the Red Cross on Mon,
Nov 20 in the UC Ballroom from 10am - 4pm.
lt/TEXTgt ltTRIPLEgt triple(charity_001)(
'http//gentoo.cs.umbc.edu/howlir/announcements/ch
aritycharity_001_place', 'http//daml.umbc.edu/o
ntologies/event_ontBuilding', 'University
Center'). triple(charity_001)(
'http//gentoo.cs.umbc.edu/howlir/announcements/ch
aritycharity_001', 'http//daml.umbc.edu/ontolog
ies/event_ontOrganizer', 'Office of Student
Life'). triple(charity_001)( 'http//gentoo.cs.um
bc.edu/howlir/announcements/charitycharity_001_da
te', 'http//daml.umbc.edu/ontologies/event_ontD
ay_of_week', 'Monday'). lt/TRIPLEgt lt/DOCgt
71
QUERY
ltQuerygt ltrequiredgt triple(query_001)(
'http//daml.umbc.edu/ontologies/queryquery_001,
'http//daml.umbc.edu/ontologies/event_ontMovie
_Name' 'Oceans Eleven'). lt/requiredgt ltallowedgt lt
/allowedgt ltdisallowedgt triple(query_001)( 'http//
daml.umbc.edu/ontologies/queryquery_001,
'http//daml.umbc.edu/ontologies/event_ontOrganiz
er SEB'). lt/disallowedgt lt/Querygt
72
Results?

• Doing experiments now to measure recall and
  precision over a small collection of 1500 event
  announcements and 12 queries.
• Compare
• Only free text
• Free text base triples but no inferencing
• Free text triples inferred triples
• We expect to see improved precision and recall

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(3) Enhancing BluetoothsService Discovery
Protocol

• Bluetooths SDP is very simple
• Services and attributes represented by
  UUIDswhich are 128 bit numbers!
• No registration, aggregation, multicasting, event
  notification
• Enhanced SDP uses DAMLOIL
• We assume at least one resource rich device in
  the ad hoc network to serve as a matchmaker
• Services and attributes described in DAML using a
  standard ontology
• All available information from service and
  attribute descriptions used for matching
• Reasons to obtain closest match

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(4) Delegation Based Model for Distributed Trust

• We are developing a delegation based model for
  distributed authorization and trust for use in
  both wired and wireless scenarios.
• Trust depends on
• policies credentials delegation actions
  proofs of permissions and obligations.
• Agents make speech acts about and reason over
  these properties and relations
• Grounded in an ontology represented in DAML

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Other UMBC SW work

• Context aware computing
• Service composition in pervasive computing
  environments
• Intelligent opportunistic data caching in mobile
  computing environments
• Using DAML-S in FIPAs directory facilitator
• Ontology mapping
• Better reasoning tools

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Conclusions

• Some thoughts
• Solving the symbol grounding problem
• Rethinking agent communication
• How do we get there

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The symbol grounding problem

• An argument against human-like AI is that its
  impossible unless machinesshare our perception
  of the world.
• A solution to this symbol groundingproblem is
  to give robots with humaninspired senses.
• But the world we experience is determined by our
  senses, and human and machine bodies may lead to
  different conceptions of the world (e.g. Nagels
  What Is It Like To Be a Bat? )
• Maybe the Semantic Web is a way out of this
  problem?

MITs Cog
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Solving the symbol grounding problem

• The web may become a common world that both
  humans and machines can understand.
• Confession the web is more familiar and real to
  me than much of the real world.
• Physical objects can be tagged with low cost
  (e.g., 0.05) transponders or RFIDs encoding
  their URIs
• See HPs Cooltown projecthttp//cooltown.com/

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Rethinking the agent communication paradigm

• Much multi-agent systems work is grounded in
  Agent Communication Languages (e.g., KQML, FIPA)
  and associated software infrastructure.
• This paradigm was articulated 1990, about the
  same time as the WWW was developed.
• Our MAS approach has not yet left the laboratory
  yet the Web has changed the world.
• Maybe we should try something different?
• The communication MAS paradigm has been
  peer-to-peer message oriented communication
  mediated by brokers and facilitators -- an
  approach inherited from client-server systems.

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Rethinking the agent communication paradigm

• A possible new paradigm?
• Agents publish beliefs, requests, and other
  speech acts on web pages.
• Brokers search for and index published
  content
• Agents discover what peers have published on
  the web and browse for more details
• Agents speak for content on web pages by
• Answering queries about them
• Accepting comments and assertions about them

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How do we get there from here?

• This semantic web emphasizes ontologies their
  development, use, mediation, evolution, etc.
• It will take some time to really deliver on the
  agent paradigm, either on the Internet or in a
  pervasive computing environment.
• The development of complex systems is basically
  an evolutionary process.
• Random search carried out by tens of thousands of
  researchers, developers and graduate students.

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Climbing Mount Improbable

• The sheer height of the peak doesn't matter, so
  long as you don't try to scale it in a single
  bound. Locate the mildly sloping path and, if you
  have unlimited time, the ascent is only as
  formidable as the next step. -- Richard
  Dawkins, Climbing Mount Improbable, Penguin
  Books, 1996.

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The Evolution of Useful Things

• The Evolution of Useful Things, Henry Petroski,
  1994.
• Prior to the 1890s, papers were held together
  with straight pens.
• The development of spring steel allowed the
  invention of the paper clip in 1899.
• It took about 25 years (!) for the evolution of
  the modern gem paperclip, considered to be
  optimal for general use.

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So, we should

• Start with the simple and move toward the complex
• E.g., from vocabularies to FOL theories
• Allow many ontologies to bloom
• Let natural evolutionary processes select the
  most useful as common consensus ontologies.
• Support diversity in ontologies
• Monocultures are unstable
• There should be no THE ONTOLOGY FOR X.
• The evolution of powerful, machine readable
  ontologies will happen over multiple human
  generations
• Incremental benefits will more than pay for effort

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For more information

• On RDF
• http//www.w3.org/RDF/
• On DAML
• http//www.daml.org/
• On W3Cs semantic web activity
• http//www.w3.org/2001/sw/
• On the semantic web
• http//semanticweb.org/
• On our work at UMBC
• http//research.ebiquity.org/

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