Information Agents(& the Semantic Web)

Martin Beer,

School of Computing & Management Sciences,

Sheffield Hallam University, Sheffield,

United Kingdom

m.beer@shu.ac.uk

Outline• Semantic Web: How can it help?

– B2B Perspective– m-Commerce Perspective

• Information Agents – A way to Share Information in an organization– Ideas behind Semantic Web are not all

new– What are they – An Example Application – BT Jasper

Vision

• The Web’s Paradox:– Power: Massive amount of content– Weakness: Inability to harness all this content

• Vision: Make Web content and services “machine-understandable”– Support significantly higher levels of automation

• Agents and other intelligent technologies

Syntactic Approaches Won’t Do

• Domain-specific ‘syntactic’ tags:– All parties have to agree upfront on a common

terminology– Makes it difficult to introduce new terms and

concepts• Or modify the meaning of some terms

– Doesn’t necessarily guarantee that everyone has the exact same understanding of what each concept means

Semantic Web

• So, let’s use ontologies and semantic markup• But…

– How will we get people/organizations to annotate Web content?

• The old chicken & egg problemb• Easy-to-use editing tools

– …And how will we solve the many other technical problems that need to be addressed?

• e.g. ontology translation/equivalence, etc.

The Opportunity is Right Now!

• Creating compelling services and functionality is the only way to bring industry onboard

• Government programs in the EU and the US have agreed to provide around $/€ 50-100M in seed money

• The window of opportunity is short…perhaps 3 or 4 years– Focus on the easy pickings first!

Some Promising Areas

• B2B Interoperability• Mobile Internet Services• …and many more:

– B2C, e.g.• One-stop online travel services• Customer protection & online dispute resolution

– Medical domain

The Emerging Internet Economy

0

2000

4000

6000

8000

2000 2003 2005

Worldwide B2B Sales Transactions in Billions of US Dollars

Source: Gartner – March 2001 Includes sales of all goods and services for which the order taking process was completed via the internet - i.e. excludes proprietary networks

Dynamic Supply Chains

Purchasing Production Sales Distribution

MaterialsManagement

ManufacturingManagement

Distribution CustomersSuppliers

SuppliersInternal

Supply ChainCustomers

Buyers/Sellers

Buyers/Sellers

Buyers/Sellers

Buyers/Sellers

InventoryControl

e-Markets/Exchanges

Functional Silos

EnterpriseIntegration

Dynamic Internet-enabled

Supply Chain

Supply ChainIntegration

Beyond Just Procurement…

• All activities will increasingly be carried out across dynamic webs of companies– Design, production, distribution, maintenance, etc.

• Inter-enterprise collaboration• Dynamic partnerships

• Objective: Always work with the best partner• A company’s competitiveness is determined

by its ability to interoperate with others– …Semantic B2B interoperability

Semantic B2B: An Example

Diagnosis of airbag thatincidentally inflated

RFQ for the design of achild-proof airbag for a new

car model

ElectronicsManufacturers

Car Seat Manufacturers

Exchangee-Service

Center Event Description

Specs & Price Reqts.

CollaborativeDesign

Quote& SensorDesign

CollaborativeDiagnosis

Beyond Inter-Enterprise Collaboration

• Disaggregate large enterprise solutions– e.g. large ERP solutions

• Towards interoperation of best-of-breed modules– Both static & dynamic models

• e.g. “Dynamic ASP” model based on semantic markup

• Potential Benefits:– Lower costs

• More competition & You only buy what you need

– Best functionality– Lower consulting fees?

• Integration, maintenance, etc.

Dynamic B2B Interoperability

• Company services and products described with semantic annotations

• Services can be combined in an arbitrary fashion – subject to semantic service descriptions– e.g. Use Company X’s CAD tool, Company Y’s

manufacturing facility, Company Z’s logistics system, etc.

– Companies advertise their services in directories and/or e-marketplaces just like they advertise their products today

Emerging Vision

CoreBusinessPartner

CoreBusinessPartner

CoreBusinessPartner

SupportingASP

Market-drivenPartnership

SupportingASP

SupportingASP

SupportingASP

SupportingASP

Semantic Markup•Service Capability•Rate•etc.

Mobile Internet Services• The emerging mobile internet

– Towards a billion mobile phone users• Also PDAs, pagers, wearable computers, etc.

– Most devices to become internet-enabled within a few years

– More accurate location tracking functionality to become widespread

Context Awareness

• Device limitations

• Time critical nature of many usage scenarios

• …Require personalization & context-awareness

Challenges

• Capture user context while minimizing user input

• Match user’s context with available services (push and pull)– Be useful rather than annoying– Scale across a broad range of services

• interoperability

– Capture user’s permission/privacy requirements• Including sharing of contextual information

– With whom, under which conditions, etc.

• User acceptance is the ultimate criterion

Context-Aware Campus Services

• Motivation:– Campus as “everyday life microcosm”

• Objective:– Enhance campus life through context-aware services

accessible over the WLAN

• Approach:– Involve stakeholders in the design (e.g. students)– Exploit location, calendar and other sources of contextual

information– Develop & evaluate ontologies, incl. permission profiles– Evaluate overall acceptance & extrapolate

Carnegie Mellon’s Mobile Context-Aware Campus Services

In collaboration with the Aura consortium

Example: A Calendar Ontology

• Taxonomy of Activities– Attending class, studying, taking an exam,

socializing, etc.

• Actors– Self, classmates, teacher, etc.

• Permissions & Default Preferences– e.g. “when in class, I don’t like to be disrupted by

promotional messages”– …which can be selectively overridden by the user

Agent-Based Matchmaking

• Matches user’s contexts and services– Both push and pull scenarios

• Push scenarios subject to permission profile as defined in the user’s current context

• Pull: Queries are customized based on the user’s current context

How Internet Agents Work

• The services proposed are not new

• They are already provided (in parts) by Internet Agents– typically embedded within an internet

browser– use a host of internet management tools

such as Spiders and search engines to gather information

How Internet Agents Work

S pider

M i te

D B M S

W A I S

U R L S earch

L y cos W ebC raw ler N orthS tar R obot

U ser I nform ationA gent P rogram

L ocal cache

W orld W ide W eb

S pider

M i te

D B M S

W A I S

U R L S earch

L y cos W ebC raw ler N orthS tar R obot

U ser I nfo rm ationA gent P rogram

L ocal cache

W orld W ide W eb

The Internet Softbot(Etzioni & Weld 1994)

• user makes a high-level menu-based request e.g. “send the budget memos to Mitchell at CMU”

• softbot uses search and inference knowledge to determine how to satisfy the request in the internet

• softbot tolerates ambiguity, omissions and errors in user’s request

Internet Agents:Applications

A gen t R ole Sou rce

J asper m anages inform ation sharing am onga com m uni ty o f users; stores,retrieves and sum m arisesinform ation, and inform s otherJ asper agents o f i nform ation usefulto them found on the W W W

D av ies & W eek es(1995)

W ebw atcher inform ation fi l tering A m strong et a l .(1995)

A gent R ole Sou rce

J asper m anages inf orm ation sharing am onga com m uni ty o f users; stores,retrieves and sum m ari sesinform ation, and inform s otherJ asper agents o f inform ation usefulto them found on the W W W

D av ies & W eek es(1995)

W ebw atcher inform ation fi l tering A m strong et a l .(1995)

Exploiting Metainformation• Hotlist: title and URL• Hotlist of 100+ items is not the answer!• Jasper: title, URL, keywords, summary,

date, annotation, ...• Trade-off: go beyond hotlists without

copying remote information• Use a richer set of meta-information to

index on remote information

Jasper Agents

• One on each user’s WWW browser

• Holds a personal profile on each user

• Adapts profile with usage

• Shares information with other users

• Information organiser - interest groups, keyword retrieval

Storing important information

• Now where did I see ...?• User asks agent to store interesting

information• Jasper stores a summary & keywords locally• Summary used later to decide whether to

retrieve remote information• Keywords used for retrieval

Storage

• User requests Jasper agent to store a page• Agent automatically extracts keywords &

summary• User can

– supply an annotation– post page to a Jasper interest group

• Meta-information stored in Jasper’s page store

Storage - indexing

• stopping - deletion of common words

• stemming - suffix stripping

• document/term matrix M constructed: M(i,j) = n

• Term (keyword) i occurs n times in document j

StorageSummaryKeywordsLocation (URL)AnnotationInterest GroupUserDate

User Profile

JPS files(meta-info)

Term-1

Term-2User Profile

Term-n

User Profile

Sharing Information

• I really must show John...• When information is stored, agents

examines other users’ profiles• User with relevant interests alerted

automatically

Sharing Information– On storing a page, agent checks other

users’ profiles– Profile treated as a query - page scored

against profile– coordination level matching

score(d) = n(keys in d)/n(keys in q)

– Agent generates email message to selected users

– URL, annotation and keywords relevant to that user are mailed

Information Sharing• Key to future work-

styles

– virtual businesses

– distributed teams

• New revenue streams, opportunities from new work-styles

Agent Learning

• On storage, page matched against user’s profile

• If no match, agent suggests new keywords extracted from information– Most commonly occurring terms– User can accept/reject/add– User’s profile evolves over time

Retrieval - 1

• via keywords, user and dateShow me all the pages stored by Tom

about VRML this month

• coordination level matchingscore(d) = n(keys in d)/n(keys in q)

• contents-addressable information• information can be relevant in >1 contexts• directory/filename structures are ill-

equipped

Retrieval - 2

• What’s New– latest pages stored– those which match profile well– most recent pages not matching profile

• Interest Groups– shared lists of links

Proactive searching

• Jasper can exploit user profile to search

• Clustering of keywords into related groups

sim(i,j) = 2 * ndocs(i,j) / (ndocs(i) + ndocs(j))

• Automatic searching - Jasper proactively suggests new pages to user

Proactive Searching - 2

• Profile: {internet, information, SMART, clustering, agent, intelligent, CORBA, IDL, DCE}

corba dce idl internet .....

corba 1

dce 0.62 1

idl 0.92 0.50 1

internet 0.03 0.03 0.03 1

....• Complete-link clustering -> dendogram

Proactive Searching - 3

Proactive Searching - 4

• {dce, corba, idl}• {SMART clustering}• {internet information}• {intelligent agent}

• Appropriate thresholds, clustering techniques• Test on a range of profiles

Document Clustering– Clustering of documents into related groups sim(i,j) =

2 * nterms(i,j) / (nterms(i) + nterms(j))

– VRML front-end to Jasper store– emphasis on organistion and display rather than

search:• idea of scope of collection• query formulation is not an issue• document similarities are clearer

– part of a larger initiative - virtual shared spaces

Summary

• Jasper - an information agent for WWW– use of meta-information - shared,

enhanced bookmarks

– information sharing and organisation

– adaptive - user profile learning

– proactive - automatic searching

– WWW - an information sharing (not only serving) medium

Jasper Agents

• One on each user’s WWW browser

• Holds a personal profile on each user

• Adapts profile with usage

• Shares information with other users

• Information organiser - interest groups, keyword retrieval

Jasper Information Agent

Store&retrieve

Share

Profile

SummaryKeywordsLocation (URL)AnnotationInterest Group

•Telecoms•ATM•ISDN•Broadband Services

Search

WWW

Internet Agents:Key Challenges

• for static agents the key challenge is keeping their indexes up-to-date

• hence future internet agents are likely to be mobile

• other challenges similar to those for interface and mobile agents

Conclusions

• Sharing Information is vital for any organisation

• These lectures attempt to show how this can be achieved effectively with agent technology

• We are in the area where agents merge with the Semantic Web

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