semantic web powering enterprise and web applications
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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
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
5
6
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
48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010.
Technology Landscape 2013, Dayton OH. May 26, 2010
12
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
14
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]
16
• 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.
17
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
48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010.
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
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
2004 SEMAGIX All rights reserved.
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
2004 SEMAGIX All rights reserved.
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
48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010.
Semantic Web in Action
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
48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010.
Semantic Web in Action
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)
ASEMR - Demonstration
See demonstration at:http://knoesis.org/library/demos
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
48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010.
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
See demonstration at:http://knoesis.org/library/demos
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48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010.
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
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
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