Relationships at the Heart of Semantic Web: Modeling, Discovering, Validating and Exploiting Complex Semantic Relationships

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

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

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.

Move from Syntax to Semantics in Information System ( a personal perspective) Semantic Web, some DL-II projects, Semagix SCORE, Applied Semantics VideoAnywhere InfoQuilt OBSERVER Generation III (information brokering) 1997... Semantics (Ontology, Context, Relationships, KB) InfoSleuth, KMed, DL-I projects Infoscopes, HERMES, SIMS, Garlic,TSIMMIS,Harvest, RUFUS,... Generation II (mediators) 1990s VisualHarness InfoHarness Metadata (Domain model) Mermaid DDTS Multibase, MRDSM, ADDS, IISS, Omnibase, ... Generation I (federated DB/ multidatabases) 1980s Data (Schema, “semantic data modeling)

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.

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)

Semantics and Relationships
Increasing depth and sophistication in dealing with semantics by dealing with (identifying/searching to analyzing) documents, entities, and relationships.
Documents Entities Relationships Future Current Past

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…)

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
…

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) Simple Taxonomies Expressive Ontologies Wordnet CYC RDF DAML OO DB Schema RDFS IEEE SUO OWL UMLS

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

Metadata and Ontology: Primary Semantic Web enablers Data (Heterogeneous Types/Media) Content Independent Metadata (creation-date, location, type-of-sensor...) Content Dependent Metadata (size, max colors, rows, columns...) Direct Content Based Metadata (inverted lists, document vectors, LSI) Domain Independent (structural) Metadata (C++ class-subclass relationships, HTML/SGML Document Type Definitions, C program structure...) Domain Specific Metadata area, population (Census), land-cover, relief (GIS),metadata concept descriptions from ontologies Ontologies Classifications Domain Models User More Semantics for Relevance to tackle Information Overload!!

SCORE technology Ontology Metadata adapter Metadata adapter Enterprise Content Applications Knowledge Agent Monitor KS KS KS KS KA KA KA Knowledge Sources Knowledge Agents KA Toolkit Metabase Semi- Structured Content Sources Content Sources CA CA CA Content Agent Monitor Content Agents CA Toolkit Databases XML/Feeds Websites Email Reports Documents Structured Unstructured 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

Information Extraction for Metadata Creation METADATA EXTRACTORS Key challenge: Create/extract as much (semantics) metadata automatically as possible WWW, Enterprise Repositories Digital Maps Nexis UPI AP Feeds/ Documents Digital Audios Data Stores Digital Videos Digital Images . . . . . . . . .

Video with Editorialized Text on the Web Automatic Classification & Metadata Extraction (Web page) Auto Categorization Semantic Metadata

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

Automatic Semantic Annotation of Text: Entity and Relationship Extraction

Extraction Agent Enhanced Metadata Asset Ontology-directed Metadata Extraction (Semi-structured data) Web Page

Entity and Semantic Metadata Extraction Semantic Metadata Syntax Metadata

Semantic Metadata Enhancement

Semantic Application Example – Research Dashboard 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 3 rd party content integration

Related Stock News Semantic Web – Intelligent Content Industry News Technology Products COMPANY 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 SEC Intelligent Content = What You Asked for + What you need to know!

led by Same entity Human-assisted inference Knowledge-based and Manual Associations Syntax Metadata Semantic Metadata

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 <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

MREF Metadata Reference Link -- complementing HREF
Creating “logical web” through
Media Independent Metadata based Correlation

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>

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 implementation choice)

Correlation based on Content-based Metadata height, width and size 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). water.gif (Data) Metadata Storage water.gif …… mpeg …… ppm Major component(RGB) Blue Content based Metadata Content Dependent Metadata

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>

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

TIGER/Line DB Repositories and the Media Types Population: Area: Boundaries: Land cover: Relief: Census DB Map DB Regions (SQL) Boundaries Image Features (IP routines)

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.

Example passengerOf AlQaida Terrorist Organization leaderOf friendOf Mohammad Atta Osama, bin laden Ramzi Binalshibh name name memberOf name

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.

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.

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 fname lname creates exhibited sculpts String Painting Painter paints technique material typeOf(instance) subClassOf(isA) subPropertyOf mime-type exhibited technique exhibited title last_modified last_modified title technique exhibited &r6 &r1 fname lname paints paints creates &r4 “ Rembrandt” fname
 - pathConnected (x, y): is true if there is a path
< x , p1, a, p2, b, p3, …. y > in the knowledge base
X Y a p1 p2 String String “ Rodin” “ August” fname lname “ Pablo” “ Picasso”

&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 fname lname creates exhibited sculpts String Painting Painter paints technique material typeOf(instance) subClassOf(isA) subPropertyOf mime-type exhibited technique exhibited title last_modified last_modified title technique exhibited &r6 &r1 fname lname paints paints creates &r4 “ Rembrandt” fname X k a
 - 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 >
m y b n String String “ Rodin” “ August” fname lname “ Pablo” “ Picasso”

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 fname lname creates exhibited sculpts String Painter paints technique material typeOf(instance) subClassOf(isA) subPropertyOf mime-type exhibited technique exhibited title last_modified last_modified title technique exhibited &r6 &r1 fname lname paints paints creates &r4 “ Rembrandt” fname X Y pa pa a u pc p1 c 1
 - 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 ….
String String “ Rodin” “ August” fname lname “ Pablo” “ Picasso”

 Operators
The  Operator computes Semantic Associations between two entities.
Three kinds of Operators are defined.
 Path : This operator returns all paths between two entities in the data model.
 Connect : This operator returns intersecting paths, on which the two entities lie.
 Iso :  - isomorphic paths implies a similarity of nodes and edges along the paths, and returns such similar paths between entities.

Formalism
 - pathConnected (x, y): is true if there is a path
< x , p 1 , a, p 2 , b, p 3 , …. y > in the knowledge base
 - joinConnected (x, y): is true if there two paths P 1 , P 2 such that:
P 1 = < x , p a , a, p b , b, p c , c, p d … k , p l l, p m , m> and
P 2 = < y , p u , b, p v ,… k , p w , l, p y , n>
Or
P 1 = < a, p a , b, p b ,… k , p k , l, p l , x > and
P 2 = < y, p u , b, p v , m, p w , l,… k , p 5 , l, p 6 , y >

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.

Complex Relationships - Cause-Effects & Knowledge discovery AFFECTS VOLCANO LOCATION ASH RAIN PYROCLASTIC FLOW ENVIRON. LOCATION PEOPLE WEATHER PLANT BUILDING DESTROYS COOLS TEMP DESTROYS KILLS

Knowledge Discovery - Example Earthquake Sources Nuclear Test Sources Nuclear Test May Cause Earthquakes Is it really true? Complex Relationship: How do you model this?

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

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 1950 Increase in number of earthquakes since 1945

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

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, 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)

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 .

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

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

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 ); 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 Resource Resource Resource Relationship IScape USGS site http://sun00781.dn.net/nuke/hew/Library/Catalog

IScape Processing Monitor

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.

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.

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 — Presentation Transcript