semantic search trend

01-28 [email protected]

Trend in Semantic TechnologyandSemantic Search

Sung-Kook Han

Semantic Technology Research Group, Won Kwang University

의미기술의 동향과 의미 검색

2010-01-28 [email protected] 2

Agenda

Information Technology and Semantics

Trends in Semantic Technology

Overview of Semantic Technology

Semantic Search

Summary

Information Technology and Semantics


Information and Communication

Digitally stored information resources are growing.

Communication between Human and Computer is more common.

Communication devices are diverse.

Ubiquitous

Computing

Information

Integration

Knowledge

ManagementWorld-Wide Web

Delivery and Share Semantics of Information.Delivery and Share Semantics of Information.


Semantic Gap

Concept

Symbol Thing“Jaguar”

Concept

Symbol Thing“Jaguar”

Sender

Receiver

CommunicationCommunication InformationInformation


Missing Piece: Semantics

Semantics

Internet and WebInternet and Web

Device ConvergenceDevice Convergence

Digital ContentDigital Content

Business ProcessBusiness Process


ControlledControlled

VocabularyVocabulary







OntologyOntology

GroupingGrouping

HierarchicalHierarchicalHierarchicalHierarchical

StructureStructure

TermTerm

RelationsRelations

Constraints, Axioms, RulesConstraints, Axioms, Rules

Semantic Relation,Semantic Relation,

Constraints, Axioms, RulesConstraints, Axioms, Rules

InstancesInstances

ClassificationClassification

TaxonomyTaxonomy

ThesaurusThesaurus

OntologyOntology

KnowledgeKnowledgeKnowledgeKnowledge

BaseBase

+

+

+

+

+

Related Technologies


Animal

Mammal ReptileBird

SnakeDog Cat

Cocker

Spaniel

Lady

Technologieshas_experience_in

Programsworks

Personnel

S1

Agent

Company

illusion

has WISO

Department

am

AS ASAS

LeoPaulnderleez

IntelligenceNavy

BradAnn

Howard

AssistantDirectorReza

Director

Technical

ManagementProject

TelecommunicationTask

Program

EcDARPA

Request

SemanticInteroperability

KnowledgeRepresentation

NaturalLanguage

Ontology Spectrum: One View

Is Disjoint Subclass of

with transitivity

property

Modal Logic

Logical Theory

ThesaurusHas Narrower Meaning Than

TaxonomyIs Sub-Classification of

Conceptual ModelIs Subclass of

DB Schemas, XML Schema

UML

First Order Logic

Relational

Model, XML

ER

Extended ER

Description Logic

DAML+OIL, OWL

RDF/SXTM

Syntactic Interoperability

Structural Interoperability

Semantic Interoperability

weak semanticsweak semantics

strong semanticsstrong semantics


AI and Knowledge Engineering

TodayTodayTodayToday

1983 1990 2001

Category Theory

Denotational

Semantics

Domain

Theory

Truth

Maintenance

Systems Category Theory : Theoretical CS apps-

Denotational Semantics, Type Theory Category Theory : Software Spec

EMYCIN

MYCIN

Semantic

Networks

Expert

Systems

Dempster-shafer

Evidence TheoryProbabilistic

Inference

Bayesian

Networks

KIDSSPECware

Hybrid KR Distributed

Reasoning

GPS

NetLSOAR

Spreading

Activation

Actors Blackboard

Architectures

Planning

Distributed

AI

Circumscription

Assumption-

based Systems

Default Logic

Game

TheoryAbduction

Non-monotonic

Logic

CYC

KQML

Classic

LOOM

Description LogicsWAMARPA

KSI

Formal

Ontology

PowerLOOM

TOVE

NSF KDI

OKBC

Decision

Theory

Agents

OntolinguaGFP

KIF

Prolog

Theorem

Proving

Constraint

Satisfaction

Feature Logics

LIFE

Prolog II

PARKA

PARLOG

CHIP

ECLiPSe

BinProlog

PARKA-DBOZ

JATliteFrame-based KR

Frame Problem

KJ-ONE

Microtheories

Knledge

Compilation

Graph

Partitioning

Knowledge

Partitioning

DARPA

HPKB

Ontological

Engineering

Reactive

Agents

BUI

Formalization

Of Context

KADS

LogicKBs

DARPA

RKF, DAML

OIL

Finite

Domain

Constraint

Solvers

Prolog III

Linear

Logic

Constraint

Logic

Trends in Semantic Technology


의미 기술의 확산 배경

웹 기술과 웹 2.0의 확산웹 기술과 웹 2.0의 확산

실용화 단계의 시맨틱 웹실용화 단계의 시맨틱 웹

서비스지향 시스템의 의미 기반화서비스지향 시스템의 의미 기반화

디지털 컨버전스와 유비쿼터스 컴퓨팅디지털 컨버전스와 유비쿼터스 컴퓨팅


웹 기술과 웹 2.0의 확산

Web 2.0: People-Services-Data

ServicesServicesPeoplePeopleInformationInformation

Data

1/28/2010 [email protected]

Semantic Web

“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”

T. Berners-Lee, J. Hendler, O. Lassila, The Semantic Web”, Scientific American, May 2001

Ontology AnnotatedOntology-Annotated

Web

OntologyOntology


기존 웹을 컴퓨터가 처리할 수 있는 잘 정의된 의미 어휘로 확장하여컴퓨터-컴퓨터, 컴퓨터-인간의 원활한 상호 작용을 실현하는 웹.

AgentsAgents

Semantic Web

Ontology Construction

Tool

Web-Page Annotation

Tool

Ontologies

Ontology Articulation

Toolkit

Annotated

Web-PagesMetadata

Repository

Inference

Engine

Community

Portal

End User

Agents


Semantic Web Layers


차세대 웹 기술 발전 방향

Web 2.0

Web 3.0

Web 1.0

Web 2.0

Web 3.0

Web 1.0

Web 4.0


1/28/2010 [email protected] 18

서비스 지향: Service-oriented

Web Applications

Web PagesApplet/ServletScript

Web 2.0

ServiceRIAEnterprise 2.0

Stand-aloneClient-Server

ObjectsComponentsWindows GUI

DatabaseApplication

1995 2000

1980 1990

Global

Networking

Local

Text Rich UIUser-Friendly

Service-oriented Architecture (SOA)

1. Point to point systems

App A(J2EE)

Legacy

ApplicationLegacy

Application

App B(.Net)

App D

(J2EE)

Warehouse

Finance

SalesApp C(.Net)

Partner

2. Message-based middleware with integration broker

Service Bus / MOM

App AApp B

Legacy

System

Shared

System

Adapter

Adapter

App C

App D

Sales

PartnerWarehouse

Finance

Service Oriented Architecture & Enterprise Service Bus

Enterprise Service Bus

RoutingServices

Consum

er

Pro

vid

er

TransformationAdapter

Shared

System

Adapter

Legacy

System

Service(Process)

orchestration

“Above the bus”

“Below the bus”

HTTPInternet

Custom applications

Packageapplications

BusinessProcess

Orchestration

Business RulesEngine

Author: Peter Campbell, ANZ Banking Group Australia



Semantic SOA


Digital Convergence and Ubiquitous Computing

u-Home

u-Library

u-Commerce

u-Government

u-Health

u-Learning

Services ConvergenceServices Convergence

Media ConvergenceMedia Convergence

Device ConvergenceDevice Convergence

Network Network

ConvergenceConvergence

Network Effect / Integration Effect / People Effect / Interoperability Effect

Semantics / Ontology


Semantic-based Context Awareness


Semantic Technology: Capability

From Project 10X


Semantic Technology: Value Innovation

Overview of Semantic Technology


Ontology

Common VocabularyCommon Vocabulary

Shared KnowledgeShared Knowledge

An ontology is a formal, explicit specification of a shared conceptualizationAn ontology is a formal, explicit specification of a shared conceptualization. [Borst 1997]


Ontology in a nutshell

Domain Knowledge Model

A vocabulary for representing knowledge about a domain and for describing

specific situations in a domain

classes, properties, predicates, and functions, and a set of relationships that

necessarily hold among those vocabulary terms.

Shared formal conceptualizations of particular domains that provide a common

interpretation of topics that can be communicated between people and

applications.

Also allow definition of axioms and constraints on particular concepts and

properties.

Ontological Commitment: General agreement to use a vocabulary

Ontology is social contracts.

Agreed, explicit semantics

Understandable to outsiders

(Often) derived in a community process

RelationRelation

ConceptConcept

InstanceInstance

AxiomAxiomFunctionFunction


Ontology

Concepts

concepts of the domain or tasks, which are usually organized in taxonomies

Example: Person, Car, University,…

Relations

a type of interaction between concepts of the domain

Example: subclass-of, is-a, part-of, hasJob, workWith,…,

Functions

a mapping of relations that return some value

Example : John = Father_of (Mary), 2006 = PublingYear(John, Book),…

Axioms

model sentences that are always true

Example: Cow is larger than a dog., a = a + 0,…

Instances

to represent specific elements

Example : Student called Peter,…

RelationRelation

ConceptConcept

InstanceInstance

AxiomAxiomFunctionFunction


Example: Ontology

Define-Class Research-Topic (?Res-Topic)

“Text Description here”

:DEF

(and

(Superclass-of ?Res-Topic

KA-Through-Machine-Learning

-------

Knowledge-Management

KA-Methodologies

Evaluation-of-KA

Knowledge-Elicitation

(Has-At-Least Approaches ? Res-Topic 1)

(Cardinality Date-of-last-modification Res-Topic 1)

(Has-At-Least Related-Topics ?Res-Topic 1)))

ResearchTopic :: Object

ResearchTopic (

[decsription -> “Text Description here”;

Approaches =>> Topics;

DateOfLastModification => DATE;

RelatedTopics =>> ResearchTopic].

KA-Through-Machine-Learning:: ResearchTopic.

Reuse :: ResearchTopic.

Specification-Languages :: ResearchTopic.

--------

Evaluation-of-KA :: ResearchTopic.

Knowledge-Elicitation :: ResearchTopic.)

Ontolingua (based on KIF)Ontolingua (based on KIF)

F-LogicF-Logic

<owl:Class rdf:about="http://swrc.ontoware.org/ontology#University">

<rdfs:subClassOf>

<owl:Class rdf:about="http://swrc.ontoware.org/ontology#Organization" />

</rdfs:subClassOf>

<rdfs:subClassOf>

<owl:Restriction>

<owl:onProper ty rdf:resource="http://swrc.ontoware.org/ontology#hasParts" />

<owl:allValuesFrom>

<owl:Class rdf:about="http://swrc.ontoware.org/ontology#Department" />

</owl:allValuesFrom>

</owl:Restriction>

</rdfs:subClassOf>

<rdfs:subClassOf>

<owl:Restriction>

<owl:onProperty rdf:resource="http://swrc.ontoware.org/ontology#student" />

<owl:allValuesFrom>

<owl:Class rdf:about="http://swrc.ontoware.org/ontology#Student" />

</owl:allValuesFrom>

</owl:Restriction>

</rdfs:subClassOf>

</owl:Class>

OWLOWL


RDF ConceptResource

(Document)

Property(Metadata)Property

(Metadata)(Tag)

value(Information))

value(Information))

creator

http://www.w3.org/Home/Saron

resource (subject)

Saron StoneCreator

property (predicate) value (object)

being described

web page

being described

property of theproperty of theweb page

the predicatevalue of

the predicate


RDF: Data Model

Saron Stone is the creator of the resource http://www.w3.org/Home/Saron.

Subject (Resource) http://www.w3.org/Home/Saron

Predicate (Property) Creator

Object (literal) “Saron Stone"

creator

http://www.w3.org/Home/Saron

resource (subject)

Saron StoneCreator

property (predicate) value (object)

being described

web page

being described

property of theproperty of theweb page

the predicatevalue of

the predicate


RDF Schema

RDF Schema

RDF Vocabulary Description Language.

For defining an appropriate RDF vocabulary (classes, properties and

constraints) for each specific domain.

Comprises very limited predefined primitives: subClassOf,

subPropertyOf, domain and range.

Cannot assert that particular properties are equivalent, transitive,

reverse of one another, etc.

RDF Schema

#Book #Personauthor

Property-Centric approach


RDF Schema Core Classes and Properties

Core Class

Core Property

rdfs:Resourcerdfs:Literal

rdfs:XMLLiteralrdfs:Class

rdfs:Propertyrdfs:DataType

rdfs:typerdfs:SubClassOf

rdfs:SubPropertyOfrdfs:domain

rdfs:rangerdfs:Label

rdfs:Comment


OWL

Web Ontology Language (OWL) :

RDF/ RDF Schema에 기반을 둔 웹 정보 자원의 의미 기술 표준 언어

Description Logic (DL) 기반의 논리 언어

다양한 개념 구조 표현 가능

3종류의 OWL

OWL-Lite, OWL-DL, OWL-Full

필요에 따라 선택

Semantic Web Standards

1/28/2010 [email protected] 35

전종홍 외, 시맨틱웹, TTA Jouranl, No 107, 2006년, 10월

RDFa

Microformat

GRDDL

Semantic Search


Search!! Search!!


Search Engine Market Share

Google by far comprises the largest share of searches. Microsoft has been trying to buy Yahoo to increase Microsoft’s search share. As of June 12th, both com

panies have ended merger talks.

Now, Microsoft merges Powerset…

2010-01-28 [email protected]

Rich Content and Vertical Search

AmazonBooks

Articles Wikipedia

BlogBlogs Photos Flickr

Book marksdel.icio.us Events Upcoming.org

Music Last.fm Places Dopplr

Movies Netflix Products Microsoft Aura


Rich Content and Vertical Search

http://maps.live.com/

http://www.google.com/blogsearch

http://kr.youtube.com/

http://www.pipl.com/

VideoVideo MapMap

BlogBlog PeoplePeople


User-Friendly Interface

http://www.quintura.com/

http://www.tafiti.com/ http://www.kartoo.com/TreeTree NetworkNetwork

SpaceSpace

42

Information Overload


Beyond the Limits of Keyword Search

Amount of data

Pro

du

ctivity o

f S

ea

rch

Databases

2010 - 2020

Web 1.0 2000 - 2010

1990 - 2000

PC Era1980 - 1990

2020 - 2030

Web 3.0

Web 4.0

Web 2.0 The World Wide Web

The Desktop Keyword search

Natural language search

Reasoning

Tagging

Semantic Search

The Semantic Web

The Intelligent Web

Directories

The Social Web

Files & Folders

By Radar Networks


The Age of Semantic Search


Typical Semantic Search Engine

General SearchFreebase Yahoo! Microsearch, …

Natural Language Search

PowersetHakiaAskMeNow AskWiki…

Vertical Search

Kango …now UpTakeAdaptiveBlueReportLinker…

Social Networking Search

SemantiNetDelverGoogle Social Graph API…

Personalized SearchTwineMavinIT PSS …


Search

Search

Interface

Roles

Goals

Tasks

Search

EngineContent

Language

Vocabulary

Syntax

Input

Interaction

Feedback

Index

Algorithms

Linguistics

Metadata

Controlled Vocabulary

Knowledge Management

?QueryUser

Ask, Browse, or Search Again

Results

Design

Interaction

Behavior

No definitive formulation.

Considerable uncertainty.

Complex interdependencies.

Incomplete, contradictory, and changing requirements.

Stakeholders have radically different world views and different frames for understanding information.


Semantic Search

Syntactic Search Semantic Search

Document View Bag-of-Words Vocabularies and Concepts

Search Approach Word matching Concept matching

Search Process One hot Reasoning / Inference

Help user formulate semantic queries

Help user formulate semantic queries Re-formulate or re-interpret queries Browse domain Formulate related queries Interoperability between search application Semantic indexing of documents

Ontology and Semantic Search

Semantic Search attempts to augment and improve traditional search results by using data from the SW.Semantic Search attempts to augment and improve traditional search results by using data from the SW.


Semantic Search Problems

Optimization : Requires massive parallel computerExample : “What is the best vocation for me how?”

Inference : Requires NLP + Interface Engine + DatabaseExample : “What US Senator took money from foreign entity?”

Natural Language : Requires query analysisExample : “What year was Leonardo Da Vinci born?”

Simple : Solvable with Google Statistical AlgorithmExample : “read write web blog”

Alex Iskol – Read/Write Web

I

II

III


5 Core technologies for Semantic Search

Semantic Tagging

Statistics

LinguisticsNatural language Processing

LinguisticsNatural language Processing

Semantic WebSemantic WebMetadata / Ontology

Artificial Intelligence

Concept organization

Reasoning


Semantic Search

Ontology/MetadataSemantic AnnotationOntology/Metadata

Semantic Annotation

Query ProcessingQuery ProcessingUser Interaction

System ArchitectureService ArchitectureSystem ArchitectureService Architecture

Semantic ProcessingSemantic ProcessingReasoning

SemanticSearchEngine


Categorical Features of Semantic Search Engine

ArchitectureStand-alone Maintain an concept index of document

Meta Search Use subordinate search engines

Coupling between documents and ontologies

Tight couplingData of documents refer explicitly to concepts of a specific ontology.

Loose coupling Not committed to any available ontology

User Interaction

Transparent Semantic capabilities invisible to the user.

Interactive Ask for clarification or recommendation

Hybrid Both


Categorical Features of Semantic Search Engine

User contextLearning Extract from user interaction dynamically

Hard-coded Ask for query category

Query modification

Manually The user modifies a query.

Query rewritten A query can be optimized by the system.

Graph-based Use graph traversal algorithm

Ontology Construction

anonymous Disregard the vocabulary and the semantics

Standardproperty

Synonym, hyponym,…

Domain-specific property

Domain ontology

Ontology technology Language RDF, OWL,…

A survey and classification of semantic search approaches by Christoph Mangold

54

Technology for Semantic Search

Augmenting traditional keyword search with semantic techniquesAugmenting traditional keyword search with semantic techniques

Semantic annotationSemantic annotation

Complex constraint queriesComplex constraint queries

Problem solvingProblem solving

Semantic connectivity discoverySemantic connectivity discovery

55


KeywordKeyword Concept

RDF

Repository

WordNetsynonym and meronym

WordNetsynonym and meronym

Augmenting traditional keyword search with semantic techniquesAugmenting traditional keyword search with semantic techniques

56


OntologyOntology

DocumentDocument

Semantically annotatedDocument

Semantically annotatedDocument

Semantic annotationSemantic annotation

57


QueryConstraint

Query

OntologyOntology

Complex constraint queriesComplex constraint queries

58


QueryQuery ReasoningEngine

OntologyOntology

Problem solvingProblem solving

59


Semantic Web

Semantic connectivity discoverySemantic connectivity discovery

60

Evaluation of Semantic Search

Search phase Feature Functionality Interface Components

Query construction

Free text input • keyword(s) • natural language

• Single text entry • Property-specific fields

Operators • Boolean operators • semantic constraints• regular expressions

• Application-specific syntax

Controlled terms • disambiguate input • restrict output • select predefined queries

• Value list • Faceted • Graph

User feedback • pre-query disambiguation • Suggestion list • Semantic auto completion

Search algorithm

Syntactic matching • exact, prefix, substring match • minimal edit distance • stemming

Semantic matching • thesauri expansion • graph traversal • RDFS/OWL reasoning

Evaluation of Semantic Search

Search phase Feature Functionality Interface Components

Presentation

Data selection • Selected property values • Class specific template • Display vocabulary

• Text • Graph • Tag cloud • Map • Timeline • Calendar

Ordering • Content and link structure based ranking • Ordered list

Organization • Clustering by property or path • Dynamic clustering

• Tree • Nested box structure • Cluster map

User feedback • Post-query disambiguation • Query refinement • Recommendation of related resources

• Facets • Tag cloud • Value list

refer to: http://swuiwiki.webscience.org/index.php/Semantic_Search_Survey

62

Applications of Semantic Search

Library 2.0Library 2.0 Find books related to “Semantic Search” written by TBL.Find books related to “Semantic Search” written by TBL.

BPMBPM Find PO web services for car repair parts.Find PO web services for car repair parts.

MedicineMedicine What are side-effects of rifamycin? What are side-effects of rifamycin?

e-Commercee-Commerce Search the specifications of RFID chips produced by SamTech.Search the specifications of RFID chips produced by SamTech.

ScienceScience Which parameters are seriously changed during CO2 combustion?Which parameters are seriously changed during CO2 combustion?

Search = Generic TaskSearch = Generic Task

63

Summary

Semantic Search is a kind of Generic tasks. Semantic Search is a kind of Generic tasks.• More than simple document search• Diverse applications in BioInfomatics, EcoScience, Medical Science….

Ontology is a key player of Semantic Search. Ontology is a key player of Semantic Search.• RDFa, Microformat, GRDDL,…• RDF, RDF Schema, OWL,…• Ontology Annotation and Population• SPARQL and Query processing,

Multi-disciplinary research and development. Multi-disciplinary research and development.• Natural Language Processing and Text Mining• Web Science

User-friendly User-friendly• Diverse vertical semantic search with domain ontologies• Visualization• Mobile Search


의미기술의 동향과 의미 검색

경청해 주시어,감사드립니다.