Relationships at the Heart of
Semantic Web
Amit Sheth
Large Scale Distributed Information Systems (LSDIS) Lab
University O...
The Semantic Web -- a vision with several views:
•·“The Web of data (and connections) with meaning in the
sense that a com...
Semantics for the Web
On the Semantic Web every resource (people,
enterprises, information services, application services,...
Move from Syntax to Semantics in
Information System (a personal perspective)
Semantic Web, some DL-II projects,Semantic We...
Semantics and Relationships
Semantics is derived from relationships. Consider the
linguistics perspective.
“Semantics is t...
Why is this a hard problem?
Are objects/entities equivalent/equal(same)?
How (well) are they related?
• partial and fuzzy ...
Semantics and Relationships
Increasing depth and sophistication in dealing
with semantics by dealing with
(identifying/sea...
Issues - Relationships
• Identifying Relationship (extraction)
• Expressing (specifying, representing)
relationships
• Dis...
Expressing Relationships
• Expressiveness of specification language
– In relational model
– In semantic data model, e.g., ...
Relationship Modeling in Various
Representation Models …
Catalog/ID
General
Logical
constraints
Terms/
glossary
Thesauri
“...
Sampling Issues in Relationships-
outline of this talk
• Simple Relationships – already known
– Representation
– Identific...
Metadata and Ontology:
Primary Semantic Web enablers
Data (Heterogeneous Types/Media)(Heterogeneous Types/Media)
Content I...
Metadata
adapter
Metadata
adapter
Enterprise Content
Applications
SCORE technology
Knowledge
Agent
Monitor
KS
KS
KS
KS
KA
...
Information Extraction
for Metadata Creation
WWW, Enterprise
Repositories
METADATAMETADATA
EXTRACTORSEXTRACTORS
Digital Ma...
Video with
Editorialized
Text on the
Web
Auto
Categorization
Auto
Categorization
Semantic MetadataSemantic Metadata
Automa...
Semantic Annotation
Limited tagging
(mostly syntactic)
COMTEX Tagging
Content
‘Enhancement’
Rich Semantic
Metatagging
Valu...
Automatic Semantic Annotation of Text:
Entity and Relationship Extraction
Extraction
Agent
Enhanced Metadata Asset
Ontology-directed Metadata
Extraction (Semi-structured data)
Web Page
Semantic Metadata
Syntax Metadata
Entity and Semantic
Metadata Extraction
Enabling powerful linking
of actionable information
and facilitating important
semantic applications
such as knowledge
dis...
Focused
relevant
content
organized
by topic
(semantic
categorization)
Automatic Content
Aggregation
from multiple
content ...
Related
Stock
News
Semantic Web –
Intelligent Content
Industry
News
Technology
Products
COMPANY
SEC
EPA
Regulations
Compet...
Syntax Metadata
Semantic Metadata
led by
Same
entity
Human-
assisted
inference
Knowledge-based and
Manual Associations
Blended Semantic Browsing and Querying
(Intelligence Analyst Workbench)
Physical link to Relationship
<TITLE> A Scenic Sunset at Lake Tahoe </TITLE>
<p>
Lake Tahoe is a popular tourist spot and ...
MREF
Metadata Reference Link -- complementing HREF
Creating “logical web” through
Media Independent Metadata based
Correla...
Metadata Reference Link
(<A MREF …>)
• <A HREF=“URL”>Document Description</A>
physical link between document (components)
...
Abstraction Layers
METADATA
DATA
METADATA
DATA
MREF
in RDF
ONTOLOGY
NAMESPACE
ONTOLOGY
NAMESPACE
Model for Logical
Correlation using
Ontological Terms
and Metadata
Framework for
Representing
MREFs
Serialization
(one imp...
height, width
and size
water.gif (Data)
Metadata Storage
water.gif
……mpeg
……ppm
Major component(RGB)Major component(RGB)
B...
An Example RDF Model for MREF
<?namespace href="http://www.foo.com/IQ" as="IQ"?>
<?namespace href="http://www.w3.org/schem...
Domain Specific Correlation
Potential locations for a future shopping mall identified by all
regions having a population g...
TIGER/Line DB
Population:
Area:
Boundaries:
Land cover:
Relief:
Census DB
Map DB
Regions
(SQL)
Boundaries
Image Features
(...
Relationship Discovery
• Problem
Huge volumes of data. Need to find relationships
between two entities in the Semantic Web...
passengerOf
AlQaida
Terrorist
Organization
leaderOf
friendOf
Mohammad
Atta
Example
Osama,
bin laden
Ramzi
Binalshibh
name
...
Semantic Association
Complex relationships which capture
connectivity and similarity of entities in a
knowledge base
– Com...
Representing and analyzing
metadata
• By using a graph data model, Semantic
Associations can be viewed in terms of graph
t...
Example Graph
&r3
&r5
“Reina Sofia
Museun”
&r7
“oil on
canvas”
&r2
2000-02-01
“oil on
canvas”
&r8
“Rodin
Museum”
“image/jp...
&r3
&r5
“Reina Sofia
Museun”
&r7
“oil on
canvas”
&r2
2000-02-01
“oil on
canvas”
&r8
“Rodin
Museum”
“image/jpeg”
2000-6-09
...
Painting
&r3
&r5
“Reina Sofia
Museun”
&r7
“oil on
canvas”
&r2
2000-02-01
“oil on
canvas”
&r8
“Rodin
Museum”
“image/jpeg”
2...
ρ Operators
• The ρ Operator computes Semantic
Associations between two entities.
• Three kinds of Operators are defined.
...
Formalism
∀ρ-pathConnected(x, y): is true if there is a path
– <x, p1, a, p2, b, p3, …. y> in the knowledge base
∀ρ-joinCo...
Complex Relationship Validation
• Arise in several contexts, especially involving
multiple ontologies (hence mappings)
– i...
Complex Relationships -
Cause-Effects & Knowledge discovery
VOLCANO
LOCATION
ASH RAIN
PYROCLASTIC
FLOW
ENVIRON.
LOCATION
P...
Knowledge Discovery - Example
Earthquake Sources Nuclear Test Sources
Nuclear Test May Cause Earthquakes
Is it really true...
Inter-Ontological Relationships
A nuclear test could have caused an earthquake
if the earthquake occurred some time after ...
Knowledge Discovery - Example
When was the first recorded nuclear test conducted?
Find the total number of earthquakes wit...
Knowledge Discovery –
exploring relationship…
For each group of earthquakes with magnitudes in the ranges
5.8-6, 6-7, 7-8,...
Knowledge Discovery - Example…
Find nuclear tests and earthquakes that may have
occurred as a result of the test
KB
InfoQuilt System Core capabilities
• Ability to handle heterogeneous, static or dynamic
content – wrappers & extractors, w...
IScape (Information Scape)
A computing paradigm that allows users to query
and analyze the data available from a diverse
a...
IScape …a simple example
• user’s request
– for semantically related information
(regardless of all forms of heterogeneity...
Ontologies
Disaster
eventDate
description
site => latitude,
longitude
site
latitude
longitude
Natural
Disaster
Man-made
Di...
Knowledge Builder
IScape Builder
IScape Execution
IScape
Plan Plan
Knowledge
IScape
Query
Query
Query
Data retrieved
Final Results
Final Results
IScape 1
NuclearTestsDB(
testSite, explosiveYield, waveMagnitude,
testType, eventDate, conductedBy,
[dc] waveMagnitude > 3...
IScape Processing Monitor
Future
Future work in Semantic Web will
increasingly focus on all dimensions of
relationships, especially complex
relation...
Further Information
• Related Paper: Sheth, Arpinar, Kashyap:
Relationships at the Heart of Semantic Web: Modeling,
Discov...
Relationships at the Heart of Semantic Web: Modeling, Discovering, Validating and Exploiting Complex Semantic Relationships
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Relationships at the Heart of Semantic Web: Modeling, Discovering, Validating and Exploiting Complex Semantic Relationships

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Keynote at: SOFSEM 2002: 29th Annual Conference on Current Trends in Theory and Practice of Informatics, Milovy, Czech Republic, 24-29 November 2002.

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  • EarthQuake: USGS
    Neuclear: Oklohoma Observatory
  • Resource modeling: Locally completeness (you can get all Delta flights from delta.com), data characteristics (all flights from delta.com are Delta flights), binding patterns
  • Query and analyze – a powerful query interface beyond the traditional keyword queries and queries on structured databases as in SQL
  • Ontology has repationships between testsite and lattitude/longitude, and correlation agent
  • Relationships at the Heart of Semantic Web: Modeling, Discovering, Validating and Exploiting Complex Semantic Relationships

    1. 1. Relationships at the Heart of Semantic Web Amit Sheth Large Scale Distributed Information Systems (LSDIS) Lab University Of Georgia; http://lsdis.cs.uga.edu CTO, Semagix, Inc. http://www.semagix.com November 2002 © Amit Sheth Keynote SOFSEM 2002 , Milovy, Czech Republic, Nov 25 2002
    2. 2. The Semantic Web -- a vision with several views: •·“The Web of data (and connections) with meaning in the sense that a computer program can learn enough about what data means to process it.” [B99] •·“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.” [BHL01] •·“The Semantic Web is a vision: the idea of having data on the Web defined and linked in a way that it can be used by machines not just for display purposes, but for automation, integration and reuse of data across various applications. [W3C01] Semantics: The Next Step in the Web’s Evolution
    3. 3. Semantics for the Web On the Semantic Web every resource (people, enterprises, information services, application services, and devices) are augmented with machine processable descriptions to support the finding, reasoning about (e.g., which service is best), and using (e.g., executing or manipulating) the resource. The idea is that self-descriptions of data and other techniques would allow context-understanding programs to selectively find what users want, or for programs to work on behalf of humans and organizations to make them more efficient and productive.
    4. 4. Move from Syntax to Semantics in Information System (a personal perspective) Semantic Web, some DL-II projects,Semantic Web, some DL-II projects, Semagix SCORE, Applied SemanticsSemagix SCORE, Applied Semantics VideoAnywhereVideoAnywhere InfoQuiltInfoQuilt OBSERVEROBSERVER Generation IIIGeneration III (information brokering) 1997...1997... Semantics (Ontology, Context, Relationships, KB) InfoSleuth, KMed, DL-I projectsInfoSleuth, KMed, DL-I projects Infoscopes, HERMES, SIMS,Infoscopes, HERMES, SIMS, Garlic,TSIMMIS,Harvest, RUFUS,...Garlic,TSIMMIS,Harvest, RUFUS,... Generation IIGeneration II (mediators) 1990s1990s VisualHarnessVisualHarness InfoHarnessInfoHarness Metadata (Domain model) MermaidMermaid DDTSDDTS Multibase, MRDSM, ADDS,Multibase, MRDSM, ADDS, IISS, Omnibase, ...IISS, Omnibase, ... Generation IGeneration I (federated DB/ multidatabases) 1980s1980s Data (Schema, “semantic data modeling)
    5. 5. Semantics and Relationships Semantics is derived from relationships. Consider the linguistics perspective. “Semantics is the study of meaning. …We may distinguish a number of legitimate ways to approach semantics: • … • the relationship between linguistic expressions (e.g. synonymy, antonymy, hyperonymy, etc.): sense; • the relationship to linguistic expressions to the "real world": reference. “ Ontologies in KR help capture the above. Quoted part from http://www.ncl.ac.uk/sml/staff/. © 2000 Jonathan West.
    6. 6. Why is this a hard problem? Are objects/entities equivalent/equal(same)? How (well) are they related? • partial and fuzzy match: related, relevant • related in a “context” • degrees: semantic similarity, semantic proximity, semantic distance, …. – [differentiation, disjointedness] • Even is-a link involves different notions: identify, unity, essense (Guarino and Wetley 2002) Semantic ambiguity, also based on incomplete, inconsistent, approximate information/knowledge Many problems have stumbled across these issues e.g., schema integration (in database management area)
    7. 7. Semantics and Relationships Increasing depth and sophistication in dealing with semantics by dealing with (identifying/searching to analyzing) documents, entities, and relationships. Documents Entities RelationshipsFuture Current Past
    8. 8. Issues - Relationships • Identifying Relationship (extraction) • Expressing (specifying, representing) relationships • Discovering and Exploring Relationships • Hypothesizing and Validating Relationships • Utilizing/exploiting Relationships for Semantic Applications (in document search, querying metadata, analysis…)
    9. 9. Expressing Relationships • Expressiveness of specification language – In relational model – In semantic data model, e.g., E-R variants – KR languages – In logic, e.g., description logics – …
    10. 10. Relationship Modeling in Various Representation Models … Catalog/ID General Logical constraints Terms/ glossary Thesauri “narrower term” relation Formal is-a Frames (properties) Informal is-a Formal instance Value Restriction Disjointness, Inverse, part of… After Deborah L. McGuinness (Stanford) and Tim Finin (UMBC)After Deborah L. McGuinness (Stanford) and Tim Finin (UMBC) Simple Taxonomies Expressive Ontologies Wordnet CYCRDF DAML OO DB Schema RDFS IEEE SUOOWL UMLS
    11. 11. Sampling Issues in Relationships- outline of this talk • Simple Relationships – already known – Representation – Identification/Querying: “Which entities are related to entity X via relationship R?” where R is typically specified as possibly a join condition or path expression • Complex relationships – Rho: discovery from large document set with associated metadata and ontologies: “How is X related to Y?” – ISCAPEs: validation/ human-directed knowledge discovery
    12. 12. Metadata and Ontology: Primary Semantic Web enablers Data (Heterogeneous Types/Media)(Heterogeneous Types/Media) Content Independent Metadata (creation-date, location, type-of-sensor...)(creation-date, location, type-of-sensor...) Content Dependent Metadata (size, max colors, rows, columns...)(size, max colors, rows, columns...) Direct Content Based Metadata (inverted lists, document vectors, LSI)(inverted lists, document vectors, LSI) Domain Independent (structural) Metadata (C++ class-subclass relationships, HTML/SGML(C++ class-subclass relationships, HTML/SGML Document Type Definitions, C program structure...)Document Type Definitions, C program structure...) Domain Specific Metadata area, population (Census),area, population (Census), land-cover, relief (GIS),metadataland-cover, relief (GIS),metadata concept descriptions from ontologiesconcept descriptions from ontologies Ontologies ClassificationsClassifications Domain ModelsDomain Models User M ore Sem antics for Relevance to tackle Inform ation O verload!!
    13. 13. Metadata adapter Metadata adapter Enterprise Content Applications SCORE technology Knowledge Agent Monitor KS KS KS KS KA KA KA Knowledge Sources Knowledge Agents KA Toolkit Ontology Metabase Semi- Structured Content Sources Content Sources CA CA CA Content Agent Monitor Content Agents CA Toolkit Databases XML/Feeds Websites Email Reports Documents StructuredUnstructured Databases XML/Feeds Websites Email Reports Semantic Enhancement Server Entity Extraction, Enhanced Metadata, Domain Experts Automatic Classification Classification Committee Semantic Query Server Ontology and Metabase Main Memory Index
    14. 14. Information Extraction for Metadata Creation WWW, Enterprise Repositories METADATAMETADATA EXTRACTORSEXTRACTORS Digital Maps Nexis UPI AP Feeds/ Documents Digital Audios Data Stores Digital Videos Digital Images . . . . . . . . . Key challenge: Create/extract as much (semantics) metadata automatically as possible
    15. 15. Video with Editorialized Text on the Web Auto Categorization Auto Categorization Semantic MetadataSemantic Metadata Automatic Classification & Metadata Extraction (Web page)
    16. 16. Semantic Annotation Limited tagging (mostly syntactic) COMTEX Tagging Content ‘Enhancement’ Rich Semantic Metatagging Value-added Voquette Semantic Tagging Value-added relevant metatags added by Voquette to existing COMTEX tags: • Private companies • Type of company • Industry affiliation • Sector • Exchange • Company Execs • Competitors
    17. 17. Automatic Semantic Annotation of Text: Entity and Relationship Extraction
    18. 18. Extraction Agent Enhanced Metadata Asset Ontology-directed Metadata Extraction (Semi-structured data) Web Page
    19. 19. Semantic Metadata Syntax Metadata Entity and Semantic Metadata Extraction
    20. 20. Enabling powerful linking of actionable information and facilitating important semantic applications such as knowledge discovery and link analysis (user’s task of manually retrieving all the information he needs to know is greatly minimized; he can spend more time making effective decisions) Semantic Metadata Content Tags Company: Cisco Systems, Inc. Classification: Channel Partners, E-Business Solutions Channel Partner: Siemens Network Channel Partner: Voyager Network Channel Partner: Siemens Network Channel Partner: Wipro Group E-Business Solution: CIS-1270 Security E-Business Solution: CIS-320 Learning E-Business Solution: CIS-6250 Finance E-Business Solution: CIS-1005 e-Market Ticker: CSCO Industry: Telecommunication, . . . Sector: Computer Hardware Executive: John Chambers Competition: Nortel Networks Syntactic Metadata Producer: BusinessWire Source: Bloomberg Date: Sept. 10 2001 Location: San Jose, CA URL: http://bloomberg.com/1.htm Media: Text XML content item with enriched semantic tagging, ready to be queried E -B us ines s S olutionOntology Cisco Systems Voyager Network Siemens Network Wipro Group Ulysys Group CIS-1270 Security CIS-320 Learning CIS-6250 Finance CIS-1005 e-Market Channel Partner belongs to - - - Ticker representedby - - - - - - - - - - - - Indus try channelpartnerof - - - - - - - - - - - - Competition competes with provider of - - - - - - - - - - - - E xecutives w orks for - - - - - - - - - - - - S ectorbelongsto Semantic Enhancement Uniquely exploiting real-world semantic associations in the right context Semantic Metadata Extraction (also syntactic) Content Tags Semantic Metadata Classification: Channel Partners, E-Business Solutions Company: Cisco Systems, Inc. Syntactic Metadata Producer: BusinessWire Source: Bloomberg Date: Sept. 10 2001 Location: San Jose, CA URL: http://bloomberg.com/1.htm Media: Text Channel Partners E -Bus ines s S olutionsClassification Content Tags Semantic Metadata Classification: Channel Partners, E-Business Solutions Classification Committee Knowledge-base, Machine Learning & Statistical Techniques Semantic Metadata Enhancement
    21. 21. Focused relevant content organized by topic (semantic categorization) Automatic Content Aggregation from multiple content providers and feeds Related relevant content not explicitly asked for (semantic associations) Competitive research inferred automatically Automatic 3rd party content integration Semantic Application Example – Research Dashboard
    22. 22. Related Stock News Semantic Web – Intelligent Content Industry News Technology Products COMPANY SEC EPA Regulations Competition COMPANIES in Same or Related INDUSTRY COMPANIES in INDUSTRY with Competing PRODUCTS Impacting INDUSTRY or Filed By COMPANY Important to INDUSTRY or COMPANY Intelligent Content = What You Asked for + What you need to know!
    23. 23. Syntax Metadata Semantic Metadata led by Same entity Human- assisted inference Knowledge-based and Manual Associations
    24. 24. Blended Semantic Browsing and Querying (Intelligence Analyst Workbench)
    25. 25. Physical link to Relationship <TITLE> A Scenic Sunset at Lake Tahoe </TITLE> <p> Lake Tahoe is a popular tourist spot and <A HREF = “http://www1.server.edu/lake_tahoe.txt”>some interesting facts</A> are available here. The scenic beauty of Lake Tahoe can be viewed in this photograph: <center> <IMG SRC=“http://www2.server.edu/lake_tahoe.img”> </center> Correlation achieved by using physical links Done manually by user publishing the HTML document
    26. 26. MREF Metadata Reference Link -- complementing HREF Creating “logical web” through Media Independent Metadata based Correlation
    27. 27. Metadata Reference Link (<A MREF …>) • <A HREF=“URL”>Document Description</A> physical link between document (components) • <A MREF KEYWORDS=<list-of-keywords>; THRESH=<real>>Document Description</A> • <A MREF ATTRIBUTES(<list-of-attribute-value- pairs>)>Document Description</A>
    28. 28. Abstraction Layers METADATA DATA METADATA DATA MREF in RDF ONTOLOGY NAMESPACE ONTOLOGY NAMESPACE
    29. 29. Model for Logical Correlation using Ontological Terms and Metadata Framework for Representing MREFs Serialization (one implementation choice)
    30. 30. height, width and size water.gif (Data) Metadata Storage water.gif ……mpeg ……ppm Major component(RGB)Major component(RGB) Blue Content based MetadataContent based Metadata Content Dependent Metadata Correlation based on Content-based Metadata Some interesting information on dams is available here. “information on dams” is defined by an MREF defining keywords and metadata (which may be used for a query).
    31. 31. An Example RDF Model for MREF <?namespace href="http://www.foo.com/IQ" as="IQ"?> <?namespace href="http://www.w3.org/schemas/rdf-schema" as="RDF"?> <RDF:serialization> <RDF:bag id="MREF:12345> <IQ:keyword> <RDF:resource id="constraint_001"> <IQ:threshold>0.5</IQ:threshold> <RDF:PropValue>dam</RDF:PropValue> </RDF:resource> </IQ:keyword> <IQ:attribute> <RDF:resource id="constraint_002"> <IQ:name>majorRGB</IQ:color> <IQ:type>string</IQ:type> <RDF:PropValue>blue</RDF:PropValue> </RDF:resource> </IQ:attribute> </RDF:bag> </RDF:serialization>
    32. 32. Domain Specific Correlation Potential locations for a future shopping mall identified by all regions having a population greater than 500 and area greater than 50 sq meters having an urban land cover and moderate relief <A MREF ATTRIBUTES(population < 500; area < 50 & region-type = ‘block’ & land-cover = ‘urban’ & relief = ‘moderate’)>can be viewed here</A> => media-independent relationships between domain specific metadata: population, area, land cover, relief => correlation between image and structured data at a higher domain specific level as opposed to physical “link-chasing” in the WWW
    33. 33. TIGER/Line DB Population: Area: Boundaries: Land cover: Relief: Census DB Map DB Regions (SQL) Boundaries Image Features (IP routines) Repositories and the Media Types
    34. 34. Relationship Discovery • Problem Huge volumes of data. Need to find relationships between two entities in the Semantic Web. Application areas such as National Security, Intelligence Services, Bioinformatics. Relationship can be of different kinds.
    35. 35. passengerOf AlQaida Terrorist Organization leaderOf friendOf Mohammad Atta Example Osama, bin laden Ramzi Binalshibh name name memberOf name
    36. 36. Semantic Association Complex relationships which capture connectivity and similarity of entities in a knowledge base – Complex • Involve multiple relations – Connectivity • Includes both directed paths and undirected paths Similarity • Specific notion of an isomorphism, based on the similarity of roles of entities.
    37. 37. Representing and analyzing metadata • By using a graph data model, Semantic Associations can be viewed in terms of graph traversals • We can distinguish between different types of Semantic Associations based on structural properties • For example, a path, intersecting paths, isomorphic paths. • We use the RDF Graph Data Model, to model Semantic Associations.
    38. 38. Example Graph &r3 &r5 “Reina Sofia Museun” &r7 “oil on canvas” &r2 2000-02-01 “oil on canvas” &r8 “Rodin Museum” “image/jpeg” 2000-6-09 Ext. Resource String Date Integer String title file_size last_modified mime-type Artist Sculptor Artifact Sculpture Museum String String String fname lname creates exhibited sculpts StringPaintingPainter paints technique material typeOf(instance) subClassOf(isA) subPropertyOf mime-type exhibited technique exhibited title last_modified last_modified title technique exhibited “Rodin” “August” &r6 &r1 fname lname fname lname paints paints creates &r4 “Rembrandt” “Pablo” “Picasso” fname ρ-pathConnected(x, y): is true if there is a path <x, p1, a, p2, b, p3, …. y> in the knowledge base X Y ap1 p2
    39. 39. &r3 &r5 “Reina Sofia Museun” &r7 “oil on canvas” &r2 2000-02-01 “oil on canvas” &r8 “Rodin Museum” “image/jpeg” 2000-6-09 Ext. Resource String Date Integer String title file_size last_modified mime-type Artist Sculptor Artifact Sculpture Museum String String String fname lname creates exhibited sculpts StringPaintingPainter paints technique material typeOf(instance) subClassOf(isA) subPropertyOf mime-type exhibited technique exhibited title last_modified last_modified title technique exhibited “Rodin” “August” &r6 &r1 fname lname fname lname paints paints creates &r4 “Rembrandt” “Pablo” “Picasso” fname X k a ρ-joinConnected(x, y): is true if there two paths P1, P2 such that: P1 = <x, pa, a, pb, b, pc, c, pd…k, pl l, pm, m> and P2 = <y, pu, b, pv,…k, pw, l, py, n> Or P1 = < a, pa, b, pb,…k, pk, l, pl, x > and P2 = < y, pu, b, pv, m, pw, l,…k, p5, l, p6, n > my b n
    40. 40. Painting &r3 &r5 “Reina Sofia Museun” &r7 “oil on canvas” &r2 2000-02-01 “oil on canvas” &r8 “Rodin Museum” “image/jpeg” 2000-6-09 Ext. Resource String Date Integer String title file_size last_modified mime-type Artist Sculptor Artifact Sculpture Museum String String String fname lname creates exhibited sculpts StringPainter paints technique material typeOf(instance) subClassOf(isA) subPropertyOf mime-type exhibited technique exhibited title last_modified last_modified title technique exhibited “Rodin” “August” &r6 &r1 fname lname fname lname paints paints creates &r4 “Rembrandt” “Pablo” “Picasso” fname X Y pa pa a u pc p1 c 1 ρ-isoConnected(x, y) is true if there two paths P1, P2 such that: P1 = <x, pa, a, pb, b, pc, c> and P2 = <y, pu, b, pv, m, pw, l> and x ≅ y, a ≅ b, c ≅ l ……. pa ≅ pu, pb ≅ pv, pc ≅ pw ….
    41. 41. ρ Operators • The ρ Operator computes Semantic Associations between two entities. • Three kinds of Operators are defined. ρ Path : This operator returns all paths between two entities in the data model. ρ Connect : This operator returns intersecting paths, on which the two entities lie. ρ Iso : ρ-isomorphic paths implies a similarity of nodes and edges along the paths, and returns such similar paths between entities.
    42. 42. Formalism ∀ρ-pathConnected(x, y): is true if there is a path – <x, p1, a, p2, b, p3, …. y> in the knowledge base ∀ρ-joinConnected(x, y): is true if there two paths P1, P2 such that: – P1 = <x, pa, a, pb, b, pc, c, pd…k, pl l, pm, m> and – P2 = <y, pu, b, pv,…k, pw, l, py, n> Or – P1 = < a, pa, b, pb,…k, pk, l, pl, x > and – P2 = < y, pu, b, pv, m, pw, l,…k, p5, l, p6, y >
    43. 43. Complex Relationship Validation • Arise in several contexts, especially involving multiple ontologies (hence mappings) – information interoperability where related resources subscribe to different but related ontologies – information requestor and resource modelers choose to use different ontologies – information requests to support analysis, knowledge discovery, decision making, learning that requires linking multiple domains with different ontologies Developing all encompassing, unified ontology is not shown to be practical. Preexisting classifications/metadata standards/taxonomies are hard to ignore.
    44. 44. Complex Relationships - Cause-Effects & Knowledge discovery VOLCANO LOCATION ASH RAIN PYROCLASTIC FLOW ENVIRON. LOCATION PEOPLE WEATHER PLANT BUILDING DESTROYS COOLS TEMP DESTROYS KILLS
    45. 45. Knowledge Discovery - Example Earthquake Sources Nuclear Test Sources Nuclear Test May Cause Earthquakes Is it really true? Complex Relationship: How do you model this?
    46. 46. Inter-Ontological Relationships A nuclear test could have caused an earthquake if the earthquake occurred some time after the nuclear test was conducted and in a nearby region. NuclearTest Causes Earthquake <= dateDifference( NuclearTest.eventDate, Earthquake.eventDate ) < 30 AND distance( NuclearTest.latitude, NuclearTest.longitude, Earthquake,latitude, Earthquake.longitude ) < 10000
    47. 47. Knowledge Discovery - Example When was the first recorded nuclear test conducted? Find the total number of earthquakes with a magnitude 5.8 or higher on the Richter scale per year starting from 1900 195 0 Increase in number of earthquakes since 1945
    48. 48. Knowledge Discovery – exploring relationship… For each group of earthquakes with magnitudes in the ranges 5.8-6, 6-7, 7-8, 8-9, and >9 on the Richter scale per year starting from 1900, find number of earthquakes Number of earthquakes with magnitude > 7 almost constant. So nuclear tests probably only cause earthquakes with magnitude < 7
    49. 49. Knowledge Discovery - Example… Find nuclear tests and earthquakes that may have occurred as a result of the test KB
    50. 50. InfoQuilt System Core capabilities • Ability to handle heterogeneous, static or dynamic content – wrappers & extractors, with resource modeling (completeness, data characteristics, binding patterns) • Information Extraction: Semi-Automatically or Automatically create domain-specific or contextually relevant metadata • Domain modeling with complex (user defined, inter-ontology) relationships, domain rules and FD • User defined Functions (esp. for fuzzy/approximate matching) and Simulation • Post processing result analysis (e.g., chart creator)
    51. 51. IScape (Information Scape) A computing paradigm that allows users to query and analyze the data available from a diverse autonomous sources, gain better understanding of the domains and their interactions as well as discover and study relationships.
    52. 52. IScape …a simple example • user’s request – for semantically related information (regardless of all forms of heterogeneity) – specified in terms of components of knowledge base (domain model, relationships, functions, simulations) “Find all earthquakes with epicenter less than 5000 mile from the location at latitude 60.790 North and longitude 97.570 East and find all tsunamis that they might have caused” Next - KD using ISacpes
    53. 53. Ontologies Disaster eventDate description site => latitude, longitude site latitude longitude Natural Disaster Man-made Disaster damage numberOfDeaths damagePhoto Volcano Earthquake NuclearTest magnitude bodyWaveMagnitude conductedBy explosiveYield bodyWaveMagnitude < 10 bodyWaveMagnitude > 0 magnitude < 10 magnitude > 0 Terms/Concepts (Attributes) Functional Dependencies (FDs) Domain Rules Hierarchies
    54. 54. Knowledge Builder
    55. 55. IScape Builder
    56. 56. IScape Execution IScape Plan Plan Knowledge IScape Query Query Query Data retrieved Final Results Final Results
    57. 57. IScape 1 NuclearTestsDB( testSite, explosiveYield, waveMagnitude, testType, eventDate, conductedBy, [dc] waveMagnitude > 3 ); NuclearTestSites( testSite, latitude, longitude ); SignificantEarthquakesDB( eventDate, description, region, magnitude, latitude, longitude, numberOfDeaths, damagePhoto, [dc] eventDate > “January 1, 1970” ); NuclearTest( testSite, explosiveYield, waveMagnitude, testType, eventDate, conductedBy, latitude, longitude, waveMagnitude > 0, waveMagnitude < 10, testSite -> latitude longitude ); Earthquake( eventDate, description, region, magnitude, latitude, longitude, numberOfDeaths, damagePhoto, magnitude > 0 ); “Find all nuclear tests conducted by India or Pakistan after January 1, 1995 with seismic body wave magnitude > 4.5 and find all earthquakes that could have been caused due to these tests.” NuclearTest Causes Earthquake <= dateDifference( NuclearTest.eventDate, Earthquake.eventDate ) < 30 AND distance( NuclearTest.latitude, NuclearTest.longitude, Earthquake,latitude, Earthquake.longitude ) < 10000 Ontology Ontology ResourceResource Resource Relationship IScape USGS site http://sun00781.dn.net/nuke/hew/Library/Catalog
    58. 58. IScape Processing Monitor
    59. 59. Future Future work in Semantic Web will increasingly focus on all dimensions of relationships, especially complex relationships. New Semantic Applications (business/govt. intelligence) are being enabled.
    60. 60. Further Information • Related Paper: Sheth, Arpinar, Kashyap: Relationships at the Heart of Semantic Web: Modeling, Discovering, and Exploiting Complex Semantic Relationships http://lsdis.cs.uga.edu/lib/2002.html • InfoQuilt and Semantic Association Projects at the LSDIS Lab: http://lsdis.cs.uga.edu • Green, Bean and Myaeng: The Semantics of Relationships: An Interdisciplinary Perspective, Kluwer Academic Publishers 2002.

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