Semantic Web powering Enterprise and Web Applications

Semantic Web powering Intelligent Enterprise and Web Applications

Amit P. ShethLexisNexis Ohio Eminent Scholar

Ohio Center of Excellence in Knowledge enabled Computing (Kno.e.sis) Wright State University, Dayton, OH

48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010.

Technology Landscape 2013, Dayton OH. May 26, 2010

http://knoesis.org/

Ohio Center of Excellence on

Knowledge-Enabled Computing (Kno.e.sis)

Structured text (Scientific

publications / white papers)

Experimental Results Clinical Trial Data

Public domain knowledge (PubMed)

Metadata Extraction/Semantic Annotations

Domain Models/

Knowledge

Meta data / Semantic Annotations

Biomedical Knowledge Discovery,Knowledge Management & Visualization

Massive amounts of data

Search and browsing

Patterns / Inference / Reasoning

2D-3D & Immersive Visualization, Human Computer Interfaces

Impacting bottom line

Knowledge discovery

Migraine

Stress

Patient

affects

isaMagnesium

Calcium Channel Blockers

inhibit

SEMANTICS, MEANING PROCESSING

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Kno.e.sis’ leadership in semantic processing will contribute to basic theory about computation and cognitive systems, and address pressing practical problems associated with productive thinking in the face of an explosion of data.

Kno.e.sis intends to lead a march from information age to meaning age.

Kno.e.sis Vision

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Human Sciences & Health Care

Advanced Data Management

Defense/Aerospace R & D

Application to Regional Industry Cluster

daytaOhio – a WCI

• Visualization and Data Mgt Infrastructure

• Consulting and Technology Transfer

Kno.e.sis+Faculty Strengths• Cognitive Science & Human Factors• Data Analysis/Mining/Visualization• Info. & Knowledge Mgmt• Web 3.0 (Semantics, Services, Sensors)• Virtual Worlds, Social Computing• High Performance/Cloud Computing• Bioinformatics/Biomedicine, Healthcare

Academic Research and Infrastructure

Globally Competitive Careers and Economic Development

Dayton Region Companies

Woolpert SAIC

REI Tech, Aptima LexisNexis

WPAFB Directorates

Human Effectiveness Sensor

Knowledge Workers, Products, Services and Applications

Tech^Edge

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Significant Infrastructure

NMR

Whole-Body Laser Range Scanner

VERITAS

stereoscopic 3D visualization

AVL

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Exceptional Regional Collaboration

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• At least 6 active projects with AFRL/WPAFB• Human Effectiveness Directorate• Sensors Directorate

Exceptional National Collaboration

• Univ. of Georgia, Stanford, Purdue, OSU, Ohio U., Indiana U. UC-Irvine, Michigan State U., Army, W3C

• Microsoft, IBM, HP, Google

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• U. Manchester, TU-Copenhagen, TU-Delft, DERI (Ireland), Max-Planck Institute, U. Melbourne, U Queensland, NICTA-Australia, CSIRO, DA-IICT (India)

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Exceptional International Collaboration

Semantic Web powering Intelligent Enterprise and Web Applications

Amit P. ShethLexisNexis Ohio Eminent Scholar

Ohio Center of Excellence in Knowledge enabled Computing (Kno.e.sis) Wright State University, Dayton, OH


Technology Landscape 2013, Dayton OH. May 26, 2010

http://knoesis.org/

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Evolution of the Web

Web of pages - text, manually created links - extensive navigation

2007

1997

Web of databases - dynamically generated pages - web query interfaces

Web of resources - data = service = data, mashups - ubiquitous computing

Web of people - social networks, user-created casual content - Twine, GeneRIF, Connotea

Web as an oracle / assistant / partner - “ask the Web”: using semantics to leverage text + data + services - Powerset

Sem

antic

Tec

hnol

ogy

Use

d

OUTLINE

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• Semantic Web –key capabilities and technlologies

• Real-world Applications demonstrating benefit of semantic web technologies

• Exciting on-going research

Introduction

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123of

Semantic Web

Introduction [1]

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• Ontology: Agreement with a common vocabulary/nomenclature, conceptual models and domain Knowledge

• Schema + Knowledge base • Agreement is what enables interoperability• Formal description - Machine processability is

what leads to automation

Introduction [2]

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• Semantic Annotation (Metadata Extraction): Associating meaning with data, or labeling data so it is more meaningful to the system and people.

• Can be manual, semi-automatic (automatic with human verification), automatic.

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From Syntax to Semantics

Shallow semantics

Deep semantics

Expr

essi

vene

ss,

Rea

soni

ng

Introduction [3]

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• Reasoning/Computation: semantics enabled search, integration, answering complex queries, connections and analyses (paths, sub graphs), pattern finding, mining, hypothesis validation, discovery, visualization

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Characteristics of Semantic Web

SelfDescribing

Machine &HumanReadable

Issued bya TrustedAuthority

Easy toUnderstand

ConvertibleCan beSecured

The Semantic Web:XML, RDF & Ontology

Adapted from William Ruh (CISCO)

SW Stack: Architecture, Standards

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a little bit about ontologies

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e.g. Open Biomedical Ontologies

Open Biomedical Ontologies, http://obo.sourceforge.net/

Many Ontologies Available

From simple ontologies

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Drug Ontology Hierarchy (showing is-a relationships)

owl:thing

prescription_drug

_ brand_na

me

brandname_unde

clared

brandname_comp

osite

prescription_drug

monograph_ix_cla

ss

cpnum_ group

prescription_drug

_ property

indication_

property

formulary_

property

non_drug_

reactant

interaction_proper

ty

property

formulary

brandname_indivi

dual

interaction_with_prescriptio

n_drug

interaction

indication

generic_ individua

l

prescription_drug_ generic

generic_ composit

e

interaction_ with_non_ drug_react

ant

interaction_with_monograph_ix_class

to complex ontologies

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N-Glycosylation metabolic pathway

GNT-Iattaches GlcNAc at position 2

UDP-N-acetyl-D-glucosamine + alpha-D-Mannosyl-1,3-(R1)-beta-D-mannosyl-R2 <=>

UDP + N-Acetyl-$beta-D-glucosaminyl-1,2-alpha-D-mannosyl-1,3-(R1)-beta-D-mannosyl-$R2

GNT-Vattaches GlcNAc at position 6

UDP-N-acetyl-D-glucosamine + G00020 <=> UDP + G00021

N-acetyl-glucosaminyl_transferase_VN-glycan_beta_GlcNAc_9N-glycan_alpha_man_4

A little bit about semantic metadata extractions and annotations

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WWW, EnterpriseRepositories

METADATA

EXTRACTORS

Digital Maps

NexisUPIAP

Feeds/Documents

Digital Audios

Data Stores

Digital Videos

Digital Images. . .

. . . . . .

Create/extract as much (semantics)metadata automatically as possible;

Use ontlogies to improve and enhanceextraction

Metadata Creation

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Automatic Semantic Metadata Extraction/Annotation

Significant presence

• Life Science (biomedical)• Health Care (clinical)• Defense & Intelligence• Web


Semantic Web in Action

Financial Services Risk Management

(a platform for building ontology-driven information system)

Ontology

ContentSources

Sem

i-St

ruct

ured

CA

ContentAgents

Stru

ctur

edU

nst r

u ctu

red

Documents

Reports

XML/Feeds

Websites

Email

Databases

CA

CA

KnowledgeSources

KA

KS

KS

KA

KA

KS

KnowledgeAgents

KSMetabase

Semantic Enhancement Server

Entity Extraction, Enhanced Metadata,

AutomaticClassification

Semantic Query ServerOntology and Metabase

Main Memory Index

Metadata adapter

Metadata adapter

Existing Applications

ECM EIPCRM

© Semagix, Inc.

Semagix Freedom Architecture

04/11/2023

2004 SEMAGIX All rights reserved.

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Global Bank

• Aim• Legislation (PATRIOT ACT) requires banks to identify ‘who’ they are doing

business with

• Problem• Volume of internal and external data needed to be accessed• Complex name matching and disambiguation criteria• Requirement to ‘risk score’ certain attributes of this data

• Approach• Creation of a ‘risk ontology’ populated from trusted sources (OFAC etc);

Sophisticated entity disambiguation• Semantic querying, Rules specification & processing

• Solution• Rapid and accurate KYC checks• Risk scoring of relationships allowing for prioritisation of results• Full visibility of sources and trustworthiness


Watch list Organization

Company

Hamas

WorldCom

FBI Watchlist

Ahmed Yaseer

appears on Watchlistmember of organization

works for Company

Ahmed Yaseer:• Appears on Watchlist

‘FBI’

• Works for Company ‘WorldCom’

• Member of organization ‘Hamas’

The Process


Global Investment Bank

Example of Fraud Prevention application used in financial services

User will be able to navigate the ontology using a number of different interfaces

World Wide Web content

Public Records

BLOGS,RSS

Un-structure text, Semi-structured Data

Watch ListsLaw

Enforcement Regulators

Semi-structured Government Data

Scores the entity based on the content and entity relationships

EstablishingNew Account

Focused relevantcontent organizedby topic(semantic categorization)

Automatic ContentAggregationfrom multiple content providers and feeds

Related relevant content not explicitly asked for (semantic associations)

Competitive research inferred automatically

Automatic 3rd party content integration

Equity Research Dashboard

Equity Research Dashboard with Blended Semantic Querying and Browsing



Defense & Intelligence

An Ontological Approach to Assessing IC Need to Know

Sponsored by ARDAWork performed at LSDIS Lab, Univ. of Georgia

March 2005

6/21/2004

Security and Terrorism Part of SWETO Ontology

6/21/2004

Schematic of Ontological Approach to the Legitimate Access Problem

Semagix Freedom

Semagix Freedom

6/21/2004

Graph-based creation: A Context of Investigation

26,489 entities34,513 (explicit) relationships

Add relationship to context

6/21/2004

Show me the stuff …

See demonstration at:http://knoesis.org/library/demos



Supporting Clinical Decision Making

Clinical Decision Making

• Status: In use today• Where: Athens Heart Center• What: Use of Semantic Web technologies for

clinical decision support

Operational Since January 2006

Goals:• Increase efficiency with decision support

• formulary, billing, reimbursement• real time chart completion• automated linking with billing

• Reduce Errors, Improve Patient Satisfaction & Reporting• drug interactions, allergy, insurance

• Improve Profitability

Technologies:• Ontologies, semantic annotations & rules • Service Oriented Architecture

Thanks -- Dr. Agrawal, Dr. Wingeth, and others. ISWC2006 paper

Active Semantic Electronic Medical Records (ASEMR)

http://iswc2006.semanticweb.org/items/in_use_8.php

ASEMR - Demonstration


0

100

200

300

400

500

600

Month/Year

Charts

Same Day

Back Log

Chart Completion before the preliminary deployment

ASMER Efficiency

0100200300400500600700

Sept05

Nov 05 Jan 06 Mar 06

Month/Year

Charts Same Day

Back Log

Chart Completion after the preliminary deployment

Scooner: Semantic Browser

A tool for knowledge discovery withexamples from Scientific Literature


OVERVIEW

1. Novel Information Exploration Paradigm Text Exploration on the context of relationships Not hyperlinks

2. Demonstrate use of background knowledge Named Entities, Relationships

3. Prototype Implementation Semantic annotations for navigation

4. Aggregation Utilities Saving, bookmarking, publishing etc

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WHY SCOONER?

Query Reformulations Impatient users Recognition over Recall

Constrained navigation Hyperlink dependent - apriori

Fuzzy User Interests Haiti Earthquake – Recovery, Relief, Political Climate, Crime

Current approaches are not as effective for Exploratory Search (Search-and-Sift)

Amit P. Sheth, Cartic Ramakrishnan: Relationship Web: Blazing Semantic Trails between Web Resources. IEEE Internet Computing 11(4): 77-81 (2007)

MOTIVATION

Users are

A priori hyperlink dependent

Semantic Web Standards Entity Identification (Semantic Annotations) Relationship and Triple Identification Explore documents/information via relationships

information seekersInformation documentsis embedded in

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Use Case Scenario

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Search Phrase: Magnesium

Use Case Scenario

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Use Case Scenario

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SUMMARY

Novel Information Exploration Paradigm Semantic Browser support Contextual Navigation Identify Named Entities and Relationships Provide Semantic Annotations Utilities for Aggregation Semantic Trails to Knowledge Discovery


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Semantic Sensor Web

Kno.e.sis CenterWright State University

http://knoesis.org/projects/sensorweb

Sensors are now ubiquitous,

and constantly generating observations about our world

Semantic Sensor Web

However, these systems are often stovepiped,

with strong tie between sensor network and application

Semantic Sensor Web

We want to set this data free

Semantic Sensor Web

With freedom comes new responsibilities ….

Semantic Sensor Web

(1) How to discover, access and search the data?

Web Services

- OGC Sensor Web Enablement (SWE)

Semantic Sensor Web

(2) How to integrate this data together,

when it comes from many different sources?

Shared knowledge models, or Ontologies

- syntactic models – XML (SWE)

- semantic models – OWL/RDF (W3C SSN-XG)

Semantic Sensor Web

Sensor Observation Ontology

Semantic Sensor Web

The SSN-XG Deliverables

• Ontology for semantically describing sensors

• Illustrate the relationship to OGC Sensor Web Enablement standards

• Semantic annotation of OGC Sensor Web Enablement standards

Semantic Sensor Web

http://www.w3.org/2005/Incubator/

http://www.w3.org/

Linked Open Data: a community-led effort to create openly accessible, and interlinked, semantic (RDF) data on the Web.

Semantic Sensor Web

Sensors Dataset• RDF descriptions of ~20,000 weather stations in the United States.

• Observation dataset linked to sensors descriptions.

• Sensors link to locations in Geonames (in LOD) that are nearby.

weather station

near

Semantic Sensor Web

Observations Dataset

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• RDF descriptions of hurricane and blizzard observations in the United States.

• The data originated at MesoWest (University of Utah)

• Observation types: temperature, visibility, precipitation, pressure, wind speed, humidity, etc.

Linked Datasets

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ObservationKB Sensor KB Location KB

(Geonames)

procedure location

procedure location

• ~2 billion triples

• MesoWest

• Dynamic

• 20,000+ systems

• MesoWest

• ~Static

• 230,000+ locations

• Geonames

• ~Static

720F Thermometer Dayton Airport

Example

(3) How to make numerical sensor data meaningful

to web applications and naïve users?

Symbols more meaningful than numbers

- active perception

Semantic Sensor Web

Active Perception:

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• is an iterative, bi-directional feedback loop for collecting and explaining sensor data

Explanation

ExpectationObservation

Attention

Overall Architecture

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DEMOS

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Semantic Sensor Web

Demos at http://wiki.knoesis.org/index.php/SSW• Sensor Discovery On Linked Data

• Semantic Sensor Observation Service (MesoWest)

• Video on the Semantic Sensor Web

SEMANTIC SOCIAL WEB

Ohio Center of Excellence Knowledge-Enabled Computing (Kno.e.sis)

Everyone Wants to talk

…and be heard!

Hundreds and thousands of tweets, facebook posts, blogs about a single event, multiple narratives, strong

opinions, breaking news..76

TWITRIS : Twitter+Tetris

• Our attempt to help you keep up with citizen observations on Twitter– WHAT are people saying, WHEN, from WHERE

• Puts citizen reports in context for you by overlaying it with news, wikipedia articles!

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See demo and live system athttp://twitris.knoesis.org

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How we work with industry

Interns, TrainingSBIR/STTRJoint contractsTech Transfer/licensing

More of Web 3.0 Semantics enhanced

Web, Social, Sensor and Services Computing, and their

applications to health care, life sciences, DoD,

IT/Data management, … athttp://knoesis.org

Semantic Web powering Enterprise and Web Applications

Education

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