Semantic Search Trend

Trend in Semantic Technology
and
Semantic Search
의미기술의 동향과 의미 검색

Sung-Kook Han

Semantic Technology Research Group, Won Kwang University

2010-01-28 skhan@wku.ac.kr page 1

Agenda

Information Technology and Semantics

Trends in Semantic Technology

Overview of Semantic Technology

Semantic Search

Summary

2010-01-28 skhan@wku.ac.kr 2

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 Information Knowledge
World-Wide Web
Computing Integration Management

Delivery and Share Semantics of Information.


Semantic Gap

Sender
Concept

“Jaguar” Symbol Thing

Communication Information

“Jaguar” Symbol Thing

Concept
Receiver


Missing Piece: Semantics

Business Process

Digital Content

Semantics
Device Convergence

Internet and Web


Related Technologies

Controlled
Vocabulary
+ Grouping Classification

Controlled Hierarchical
Vocabulary + Structure
Taxonomy

Controlled Term
Vocabulary + Relations
Thesaurus

Controlled Semantic Relation,
Vocabulary + Constraints, Axioms, Rules Ontology

Ontology + Instances Knowledge
Base


Ontology Spectrum: One View
Modal Logic
strong semantics
First Order Logic
Technologies
has_experience_in works
Programs Personnel
Company
Logical Theory Is Disjoint Subclass of
Knowledge
Representation
Project
Management S1 illusion Description Logic with transitivity
am
Agent Natural
Language
Task Technical Program AS AS
AS
Department DAML+OIL, OWL property
Telecommunication Leo
Semantic
Interoperability
Director EcDARPA
Navy
Paulnderleez has WISO UML
Assistant

Conceptual Model
Request Director Intelligence
Reza
Ann Brad
Howard Is Subclass of
RDF/S Semantic Interoperability
XTM
Extended ER
Thesaurus Has Narrower Meaning Than
ER
DB Schemas, XML Schema Animal
Structural Interoperability

Taxonomy Mammal Reptile
Is Sub-Classification of
Bird
Relational Snake
Dog Cat
Model, XML Syntactic Interoperability
Cocker
Spaniel
weak semantics
Lady


AI and Knowledge Engineering
Category Theory

Domain
Theory
Denotational
Semantics
Truth
Maintenance
Systems Category Theory : Theoretical CS apps-
Denotational Semantics, Type Theory Category Theory : Software Spec

EMYCIN KIDS
SPECware
Expert Dempster-shafer Probabilistic Bayesian
Evidence Theory Networks Hybrid KR Distributed
MYCIN Systems Inference Reasoning
Assumption- Decision Graph
Semantic based Systems Theory Knledge Partitioning
Networks Frame Problem Game Compilation Knowledge
Default Logic Abduction Theory Partitioning
GPS BUI
Circumscription Microtheories LogicKBs
SOAR Agents
NetL Non-monotonic
Logic Reactive JATlite
Frame-based KR
Agents KQML
Spreading
Activation
Today
Classic Formalization PowerLOOM NSF KDI
Distributed KJ-ONE Of Context
Actors AI LOOM Formal TOVE DARPA DARPA
Blackboard CYC ARPA Ontology HPKB RKF, DAML
Architectures WAM Description Logics KADS
KSI Ontological OIL
Planning 1983 Constraint PARKA 1990 Engineering 2001
Logic Prolog III KIF Ontolingua
PARLOG GFP OKBC
Prolog II Finite Linear BinProlog
Prolog Logic
Constraint LIFE Domain OZ
Theorem Satisfaction Constraint PARKA-DB
Proving Solvers
Feature Logics ECLiPSe
CHIP

의미 기술의 확산 배경

웹 기술과 웹 2.0의 확산

실용화 단계의 시맨틱 웹

서비스지향 시스템의 의미 기반화

디지털 컨버전스와 유비쿼터스 컴퓨팅


웹 기술과 웹 2.0의 확산


Web 2.0: People-Services-Data
Information
People Services
Data

1/28/2010 skhan@wku.ac.kr 13

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

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

Ontology

Ontology-Annotated
Ontology Annotated Agents
Web


Semantic Web

Ontology Articulation
Toolkit End User

Ontology Construction
Tool Agents

Ontologies
Community
Portal

Inference
Engine
Annotated
Web-Page Annotation Metadata
Web-Pages
Tool Repository

Semantic Web Layers


차세대 웹 기술 발전 방향

Web 3.0 Web 4.0
Web 3.0

Web 1.0 Web 2.0

Web 1.0 Web 2.0


서비스 지향: Service-oriented
Web Applications Web 2.0

Web Pages Service
Applet/Servlet RIA
Global
Script Enterprise 2.0

1995 2000

Networking

Client-Server
Stand-alone
Objects
Database
Components
Application
Windows GUI

Local 1980 1990

Text User-Friendly Rich UI

Service-oriented Architecture (SOA)
1. Point to point systems 2. Message-based middleware with integration broker
Partner
Partner App B App D Warehouse
App A
App A
(J2EE)
Warehouse
App B Sales
Sales
(.Net) App C Service Bus / MOM
App C
App D
(.Net)
(J2EE) Adapter
Adapter
Shared
Legacy System Legacy
Legacy System
Application
Application
Finance
Finance

Service Oriented Architecture & Enterprise Service Bus
Business

Consumer
Custom Package Business Rules
Process “Above the bus”
applications applications Engine
Orchestration

HTTP Enterprise Service Bus
Internet Service

Provider
Routing Transformation (Process)
Services Adapter Adapter
orchestration
Legacy Shared “Below the bus”
System System
Author: Peter Campbell, ANZ Banking Group Australia


Semantic SOA


Digital Convergence and Ubiquitous Computing
Network Effect / Integration Effect / People Effect / Interoperability Effect

Semantics / Ontology
u-Home
Services Convergence u-Government
Media Convergence
Device Convergence

Network
u-Library u-Health
Convergence

u-Commerce u-Learning

Semantic-based Context Awareness


Semantic Technology: Capability

From Project 10X

Semantic Technology: Value Innovation


Overview of Semantic Technology

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

Shared Knowledge

Common Vocabulary


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.
Concept
 Agreed, explicit semantics
 Understandable to outsiders Instance Relation
 (Often) derived in a community process

Function Axiom


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,… Concept

 Instances Instance Relation
 to represent specific elements
 Example : Student called Peter,…
Function Axiom


Example: Ontology
Define-Class Research-Topic (?Res-Topic) Ontolingua (based on KIF)
“Text Description here”
:DEF OWL
(and
(Superclass-of ?Res-Topic <owl:Class rdf:about="http://swrc.ontoware.org/ontology#University">
KA-Through-Machine-Learning <rdfs:subClassOf>
------- <owl:Class rdf:about="http://swrc.ontoware.org/ontology#Organization" />
</rdfs:subClassOf>
Knowledge-Management
<rdfs:subClassOf>
KA-Methodologies
<owl:Restriction>
Evaluation-of-KA <owl:onProper ty rdf:resource="http://swrc.ontoware.org/ontology#hasParts" />
Knowledge-Elicitation <owl:allValuesFrom>
(Has-At-Least Approaches ? Res-Topic 1) <owl:Class rdf:about="http://swrc.ontoware.org/ontology#Department" />
(Cardinality Date-of-last-modification Res-Topic 1) </owl:allValuesFrom>
(Has-At-Least Related-Topics ?Res-Topic 1))) </owl:Restriction>
</rdfs:subClassOf>
<rdfs:subClassOf>
F-Logic <owl:Restriction>
ResearchTopic :: Object <owl:onProperty rdf:resource="http://swrc.ontoware.org/ontology#student" />
ResearchTopic ( <owl:allValuesFrom>
[decsription -> “Text Description here”; <owl:Class rdf:about="http://swrc.ontoware.org/ontology#Student" />
Approaches =>> Topics; </owl:allValuesFrom>
DateOfLastModification => DATE; </owl:Restriction>
</rdfs:subClassOf>
RelatedTopics =>> ResearchTopic].
</owl:Class>

KA-Through-Machine-Learning:: ResearchTopic.
Reuse :: ResearchTopic.
Specification-Languages :: ResearchTopic.
--------
Evaluation-of-KA :: ResearchTopic.
Knowledge-Elicitation :: ResearchTopic.)


RDF Concept
Resource
(Document)

value
Property (Information))
(Metadata)
(Tag)

resource (subject) property (predicate) value (object)
Creator
http://www.w3.org/Home/Saron Saron Stone

property of the
web page web page value of
being described the predicate
creator


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"

resource (subject) property (predicate) value (object)
Creator
http://www.w3.org/Home/Saron Saron Stone

property of the
web page web page value of
being described the predicate
creator


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 #Person
author

Property-Centric approach


RDF Schema Core Classes and Properties

rdfs:Resource
rdfs:Literal
Core Class rdfs:XMLLiteral
rdfs:Class
rdfs:Property
rdfs:DataType

rdfs:type
rdfs:SubClassOf
rdfs:SubPropertyOf
Core Property rdfs:domain
rdfs:range
rdfs: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

RDFa

Microformat

GRDDL

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

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…


Rich Content and Vertical Search

Amazon Articles Wikipedia
Books

Blog
Blogs Photos Flickr

del.icio.us Events Upcoming.org
Book marks

Music Last.fm Places Dopplr

Movies Netflix Products Microsoft Aura

2010-01-28 skhan@wku.ac.kr

Rich Content and Vertical Search
Video http://kr.youtube.com/ Map http://maps.live.com/

Blog http://www.google.com/blogsearch
People http://www.pipl.com/


User-Friendly Interface
Tree http://www.tafiti.com/ Network http://www.kartoo.com/

Space
http://www.quintura.com/


Information Overload

42

Beyond the Limits of Keyword Search
Productivity of Search

The Intelligent Web

Web 4.0
2020 - 2030
Reasoning
The Semantic Web

Web 3.0 Semantic Search
The Social Web 2010 - 2020

The World Wide Web Web 2.0 Natural language search

Web 1.0 2000 - 2010
Tagging
1990 - 2000
The Desktop Keyword search
Directories
PC Era
1980 - 1990
Files & Folders

Databases

Amount of data
By Radar Networks


The Age of Semantic Search


Typical Semantic Search Engine
Freebase
General Search Yahoo! Microsearch,
…
Powerset
Hakia
Natural Language Search AskMeNow AskWiki
…
Kango …now UpTake
AdaptiveBlue
Vertical Search ReportLinker
…
SemantiNet
Delver
Social Networking Search Google Social Graph API
…
Twine
Personalized Search MavinIT PSS
…


Search
Roles Language Input Index Metadata Design
Goals Vocabulary Interaction Algorithms Controlled Vocabulary Interaction
Tasks Syntax Feedback Linguistics Knowledge Management Behavior

User
?Query
Search
Interface
Search
Engine

Ask, Browse, or Search Again
Content Results

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

Syntactic Search Semantic Search

Document View Bag-of-Words Vocabularies and Concepts
Search Approach Word matching Concept matching
Search Process One hot Reasoning / Inference

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


Semantic Search Problems

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

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

Natural Language : Requires query analysis
Example : “What year was Leonardo Da Vinci born?”
I

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

Alex Iskol – Read/Write Web


5 Core technologies for Semantic Search

Semantic Tagging

Statistics Concept organization

Linguistics
Natural language Processing

Semantic Web
Metadata / Ontology
Reasoning

Artificial Intelligence


Semantic Search

Ontology/Metadata
Semantic Annotation

Query Processing Semantic Semantic Processing
User Interaction Search
Reasoning
Engine

System Architecture
Service Architecture


Categorical Features of Semantic Search Engine

Stand-alone Maintain an concept index of document
Architecture
Meta Search Use subordinate search engines

Coupling Data of documents refer explicitly to
Tight coupling
concepts of a specific ontology.
between documents
and ontologies Loose coupling Not committed to any available ontology
Transparent Semantic capabilities invisible to the user.
User Interaction Interactive Ask for clarification or recommendation
Hybrid Both


Categorical Features of Semantic Search Engine

Learning Extract from user interaction dynamically
User context
Hard-coded Ask for query category
Manually The user modifies a query.
Query modification Query rewritten A query can be optimized by the system.
Graph-based Use graph traversal algorithm
anonymous Disregard the vocabulary and the semantics
Standard
Ontology Synonym, hyponym,…
property
Construction
Domain-specific
Domain ontology
property
Ontology technology Language RDF, OWL,…

A survey and classification of semantic search approaches by Christoph Mangold


Technology for Semantic Search

Augmenting traditional keyword search with semantic techniques

Semantic annotation

Complex constraint queries

Problem solving

Semantic connectivity discovery

54

Augmenting traditional keyword search with semantic techniques

WordNet
synonym and meronym

Keyword Concept

RDF
Repository

55

Semantic annotation

Ontology

Semantically annotated
Document

Document

56

Complex constraint queries

Ontology

Constraint
Query
Query

57

Problem solving

Ontology

Query Reasoning
Engine

58

Semantic connectivity discovery

Semantic Web

59

Evaluation of Semantic Search

Search phase Feature Functionality Interface Components
• keyword(s) • Single text entry
Free text input
• natural language • Property-specific fields
• Boolean operators
Operators • semantic constraints • Application-specific syntax
• regular expressions
Query construction
• disambiguate input • Value list
Controlled terms • restrict output • Faceted
• select predefined queries • Graph
• Suggestion list
User feedback • pre-query disambiguation
• Semantic auto completion
• exact, prefix, substring match
Syntactic matching • minimal edit distance
• stemming
Search algorithm
• thesauri expansion
Semantic matching • graph traversal
• RDFS/OWL reasoning

60

Evaluation of Semantic Search

Search phase Feature Functionality Interface Components
• Text
• Graph
• Selected property values
• Tag cloud
Data selection • Class specific template
• Map
• Display vocabulary
• Timeline
• Calendar
Presentation Ordering • Content and link structure based ranking • Ordered list
• Tree
• Clustering by property or path
Organization • Nested box structure
• Dynamic clustering
• Cluster map
• Post-query disambiguation • Facets
User feedback • Query refinement • Tag cloud
• Recommendation of related resources • Value list

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

Applications of Semantic Search

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

BPM Find PO web services for car repair parts.

Medicine What are side-effects of rifamycin?

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

Science Which parameters are seriously changed during CO2 combustion?

Search = Generic Task

62

Summary
 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.
• RDFa, Microformat, GRDDL,…
• RDF, RDF Schema, OWL,…
• Ontology Annotation and Population
• SPARQL and Query processing,

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

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

63

의미기술의 동향과 의미 검색

경청해 주시어,감사드립니다.


Semantic Search Trend

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