Building Sharable Ontology for Intelligent Agents based on Semantic Web

1
Building Sharable Ontology for Intelligent Agents
based on Semantic Web

• Von-Wun Soo
• Department of Computer Science
• National Tsing Hua University

2
Outline of the talk

• Basic concepts in Agents, ontology and Semantic
  Web
• Projects related to Semantic Web
• Using Sharable Ontology to Retrieval Historical
  Images
• Answer Simple Historical Questions based on
  Thesaurus and Ontology
• Conclusions

3
What is Web?

• The Web was designed as an information space,
• useful not only for human-human communication,
• machines would be also able to participate and
  help.
• Successful factors Simple, evolution, scalability

4
What is Semantic Web? (According to Tim
Berners-Lee)

• Knowledge Representation goes global
• Machine-understandable information
• Possible formulation of a universal Web of
  semantic assertions,
• based on a common model of great generality.
• The general model is the Resource Description
  Framework (RDF)

5
What is semantic Web? (2)

• The Semantic Web is a Web that includes
  documents, or portions of documents, describing
  explicit relationships between things and
  containing semantic information intended for
  automated processing by our machines.
• According to http//swag.semanticweb.org/whatIs
  SW

6
What Semantic Web is not?

• is not Artificial Intelligencebut will provide a
  foundation to make the technology more feasible
• will not require every application to use
  expressions of arbitrary complexity
• will not require proof generation to be useful
  proof validation will be enough.
• is not an exact rerun of a previous failed
  experiment

7
Why Semantic Web?

• Standardizing knowledge sharing and reusable on
  Web
• Interoperable (independent of devices and
  platforms)
• Machine readablefor possibility of intelligent
  processing of information

8
What is a software agent?

• A paradigm shift of information utilization from
  direct manipulation to indirect access and
  delegation
• A kind of middleware between information demand
  (client) and information supply (server)
• A software that has autonomous, personalized,
  adaptive, mobile, communicative, social, decision
  making abilities

9
Agents and Ontology

• Agents must have domain knowledge to solve
  domain-specific problems.
• Agents must have common sharable ontology to
  communicate and share knowledge with each other.
• The common sharable ontology must be represented
  in a standard format so that all software agents
  can understand and thus communicate with.

10
Agents and Semantic Web

• Semantic Web provides the structure for
  meaningful content of Web pages, so that software
  agents roaming from page to page will carry out
  sophisticated tasks.
• An agent coming to a clinics web page will know
  Dr. Henry works at the clinic on Monday,
  Wednesday and Friday without having the full
  intelligence to understand the text
• of course the assumption is Dr. Henry make the
  page using a off-the-shelf tool, as well as the
  resources listed on the Physical Therapy
  Associations site.

11
Knowledge representation on Web

• The challenge of web is to provide a language to
  express both data and rules for reasoning about
  the data meta-data that allows rules from any
  existing knowledge representation system to be
  exported onto web.
• Adding logic to web means to use rules to make
  inference, choose actions and answer question.
  The logic must be powerful enough but not too
  complicated for agents to consider a paradox.

12
What is ontology?

• An ontology is a formal and explicit
  specification of shared conceptualization of a
  domain of interest. (T. Gruber)
• Formal semantics
• Consensus of terms
• Machine readable and processible
• Model of real world
• Domain specific

13
What is Ontology?(2)

• Generalization of
• Entity relationship diagrams
• Object database schemas
• Taxonomies
• Thesauri
• Conceptualization contains phenomena like
• Concepts/classes/frames/entity types
• Constraints
• Axioms, rules

14
Language Layers on the Web
Trust
DAML-L (logic)
Declarative Languages OIL, DAMLOnt
PICS
DC
XHTML SMIL
RDF
XML
HTML
Semantic web infrastructure is built on RDF data
model
15
Ontological languages

• Ontology modeling languages
• Concept Map, UML, Entity-relation Model
• Ontological languages
• KIF, RDF, RDF schema, DAMLOIL

16
Tagging documents

• Everything on semantic web is a standard
  hypertext tagged with semantic tags
• Which can be regarded as a resource

17
Identifiers Uniform Resource Identifier (URI)

• All subjects and objects in web are represented
  by a URI just as a link in a page
• An URL is a most common type of URI

18
Documents Extensible Markup Language (XML)

• I just got a new pet dog. An English Sentence
• In XML
• ltsentencegtltperson href"http//aaronsw.com/"gtIlt/p
  ersongt just got a new pet ltanimalgtdoglt/animalgt.lt/s
  entencegt
• Tags
• A full set of tags (opening and closing) and
  their content is called an element
• Descriptions such as hrefhttp//aaaronsw.com/
  are called attributes

19
DTD (Data Type Definition)

• XMLs document consists of elements with
  attributes
• Define element
• lt!element code (PCDATA)gt
• lt!element message (ANY)gt
• Define Attribute
• lt!ATTLIST authorlist type CDATA IMPLIEDgt
• lt!ATTLIST authorlist type CDATA REQUIREDgt
• lt!ATTLIST book company CDATA FIXED Microsoftgt

20
XML Schema

• A well defined XML document
• Support more data types
• Support name space (more extensible than XML DTD)
• Disadvantage of DTD
• allow user to define ill-defined elements

21
XML namespaces

• A namespace is a collections of names that are
  defined in some way.
• With XML Name Spaces(give each element and
  attribute a URI).
• ltsentence
• xmlnshttp//example.org/xml/documents/
• xmlnschttp//animals.example.net/xmlns/gt
• ltcperson chref "http//aaronsw.com/"gtIlt/cpe
  rsongt
• just got a new pet ltcanimalgtdoglt/canimalgt.
• lt/sentencegt

22
XML is not the solution

• Meaning of XML-documents is intuitively clear
• But computers do not have intuition
• Tag-names per se do not provide semantics
• DTD or XML Schema does not distinguish between
  objects and relations
• XML lacks a semantic model
• Has only a surface model, i. e. tree.

23
XML is not the solution(2)

• ltpersongt
• ltidngt5634lt/idngt
• ltnamegtW. Chenlt/namegt
• ltmarriedWithgt
• S. Chenlt/marriedWithgt
• ltgendergtmalelt/gendergt
• ltsalarygt50000NTlt/salarygt
• lt/persongt

• ltman idn5634gt
• ltnamegtW. Chenlt/namegt
• ltmarriedWith ref4365/gt
• ltsalarygt1650 USDlt/salarygt
• lt/mangt

Challenges Name conflict
Value Conflict Structure
Conflicts
24
Statements Resource Description Framework (RDF)

• I really likes weaving the web.
• http//aaron.com/
• http//love.example.org/terms/reallylikes
• http//www.w3.org/People/Berner-Lee/Weaving/

25
Statements RDF(2)

• ltrdfRDF
• xmlnsrdfhttp//www.w3.org/1999/02/22-rdf-synta
  x-nsgt
• xmlnslovehttp//love.example.org/terms/gt
• ltrdfDescription rdfabouthttp//arron.com/gt
• ltlovereallyLikes rdfrecourcehttp//www.w3.
  org/People/Berners-Lee/Weavinggt
• lt/rdfDescriptiongt
• lt/rdfRDFgt

26
Statements RDF(3)

• The basic structure of RDF is object-attribute-va
  lue
• In terms of labeled graph O-A-gtV

A
O
V
27
Schemas and Ontologies RDF Schemas

• Ontologies and schemas are ways to describe
  meaning and relationships of terms
• Define ontology in terms of RDF means RDF schema
• A schema
• _at_prefix dclthttp??purl.org/dc/elements/1.1/gt
• _at_prefix rdfs http//www.w3.org/2000/01/rdf-schema
  
• An author is a type of contributor
• dcauthor rdfssubClassOf dccontributor

28
RDF Schema

• Is a set of pre-defined resources and
  relationships between them that define a simple
  meta-model including concepts of
• class,
• property,
• subclass and subproperty relationships,
• domain and range of property constraints
• and so on.

29
Family Ontology in terms of RDF schema
fPerson.name
r
t
d
rdfsLiteral
rdfBag
fPerson.father
t
d
r
et
fPerson.son
t
fMan
d
t
rdfProperty
r
s
fPerson.parent
rdfsClass
d
t
et
et
fPerson
t
t
d
t
fPerson.child
t
s
d
r
fPerson.mother
r
fWoman
d
et
rdfSeq
fPerson.daughter
30
Property Labels and Namespace Abbreviations

• t rdftype
• s rdfssubClassOf
• d rdfsdomain
• r rdfsrange
• et rdfsxcollectionElementType

• rdf http//www.w3.org/1999/02/22-rdf-syntax-nsn
  s
• rdfs http//www.w3.org/2000/01/rdf-schema
• rdfsx http//nzdis.otago.ac.nz/0_1/rdf-schema-x
  
• f any new namespace chosen for this schema

31
Family knowledge in terms of RDF
t
rdfBag
fWoman
1
fMan
2
t
n
Mary Smith
n
John Smith
p
t
c
m
fr
c
d
d
1
1
n
1
1
t
Susan Smith
t
t
t
rdfSeq
32
Property Labels and Namespace Abbreviations

• t rdftype
• 1 rdf_1
• 2 rdf_2
• n fPerson.name
• fr fPerson.father
• s fPerson.son
• p fPerson.parent
• e fPerson.child
• m fPerson.mother
• d fPerson.daughter

• rdf http//www.w3.org/1999/02/22-rdf-syntax-nsn
  s
• f namespace chosen in previous rdf schema

33
Using Sharable Ontology to Retrieve Historical
Images
34
Motivation

• Users might not have the complete historical
  knowledge for a query. Need the historical
  ontology.
• For example
• I want the picture of Qin dynastys emperor.
• Our Goal
• Establish an image retrieval model with the high
  precision and easy usage by applying the sharable
  domain ontology, knowledge and thesaurus.
• The endeavor of semantic web allows domain
  knowledge to be represented in an interoperable
  and sharable manner.

35
Processes of ontology-based image retrieval
36
Sharable Ontology Thesaurus

• Ontology
• Based on RDF Schema
• Describe the Relations between classes
• Currently implemented 6 classes and about 100
  properties.
• Thesaurus
• General term about 70000 terms in 13
  categories.
• Domain term add about 300 terms in historical
  domain of Qin terracotta soldiers.

37
Sharable domain ontology for terracotta warriors,
horses and related articles (in Graphic
representation)
38
An instance of the sharable domain ontology (in
RDFS)
39
An annotated image of a side view of a Qin
terracotta warrior's head

40
NL Query paring

• Users give the query in terms of a natural
  language phrase.
• The system parses the query into the RDF format
  with the aid of ontology and thesaurus.

The general in armor in Qin-dynasty
Parsing
Wear
General
Armor
Period
Qin-dynasty
41
NL Query paring (Naïve parsing Algorithm)
????????? (The general in armor in Qin-dynasty)
Word segmentation
?? ?? ?? ?? (Qin-dynasty,Wear,Armor,General)
Property assignment
?? ?? ?? ?? (Qin-dynasty,Wear,Armor,General)
42
NL Query paring (Naïve parsing Algorithm)
?? ?? ?? ??
Backward matching
??
??
??
??
????

• Disadvantage
• Too simple and easy to mismatch.

43
The Similarity Matching Algorithm

• Matching a query schema with annotated images.

44
The Similarity Matching Algorithm

• Method
• Treat the RDF query schema and the RDF query
  instance as a Tree
• Match all possible interpreting paths of a query
  instance with annotated pictures.
• Rank the similarity match and find the best
  answer.

45
Answer Simple Historical Questions Using
Thesaurus and Ontology
Case Study 2
46
An Ontology-Based Answer Extraction System
47
Word segmentation

• It divides the whole document into pieces of
  lexicons based on Chinese synonym thesaurus.
• It might result in wrong words.
• For example,
• ?????????
• Incorrect ?? ? ? ? ? ? ??
• Correct ? ???? ? ? ??

48
Pattern matching

• It makes complex and continuous fragments into to
  a unit.
• For example,
• 13?
• Original 1 3 ?
• Result 13?

49
Generalization lexicons thesaurus codes

• User may enhance the completeness of the
  meta-document by domain ontology or linguistic
  principle.
• Users may also refine the meta-sentence by
  interacting with an ontology.
• The instance from a meta-document can be
  expressed in XML/RDF format as knowledge base.

50
The Chinese Synonym Thesaurus
Soldier AE10
Thesaurus
51
Word Segmentation Post Editing Tool
52
Event Ontology
rdfsdomain
rdfsProperty
rdfsrange
IsPartOf
rdfsClass
Event
EventType
Literal
Agent
location
Action
Time
Theme
Event Structure
Time Structure
Location Structure
53
Event Ontology
lt?xml version"1.0" ?gt ltrdfRDF
xmlnsrdf"http//www.w3.org/1999/02/22-rdf-syntax
-ns" xmlnsrdfs"http//www.w3.org/2000/01/rdf-sc
hemagt ltrdfsClass rdfID"Event"gt
lt/rdfsClassgt ltrdfsClass rdfID"Agent"gt
lt/rdfsClassgt .. ltrdfProperty
rdfID"EventType"gt ltrdfsdomain
rdfresource"Event"gtlt/rdfsdomaingt
lt/rdfPropertygt ltrdfProperty rdfID
"IsPartOf"gt ltrdfsdomain rdfresource"Agent"
gtlt/rdfsdomaingt ltrdfsdomain
rdfresource"Action" gtlt/rdfsdomaingt ..
ltrdfsrange rdfresource"Event"gtlt/rdfsrangegt
lt/rdfPropertygt .. lt/rdfRDFgt
54
Event Structure

• ?? ?? ??
• Agent Verb Theme
• ? ? ?? ? ??
• Agent Be-Verb Theme TSubject
• ????????
• ?????
• ?????
• ??????

55
Time ontology (Schema)
56
Location ontology (Schema)
57
Time and Location schema

• ??? 227 ?
• Wtype WNum
• ? ???? ??
• TName
• ? ?? ??
• Country/InCountry CapitalCity

58
A Simple Sentence

• a sentence with only one verb.
• only deal with transitive verb and be-verb
• A grammar of a tuple (Agent, Verb, Theme) is
  similar to (Subject, VP, NP)

(Chinese),????????????226? (English),The general
of Chin Dyansty,Li-Ching,
attacked Yen Country in 226 B.C.
59
A Simple Sentence in RDF
xmlnss"http//aidl.cs.nthu.edu.tw/idlp
/event_ontology" gt .. ltsAgent
rdfID"??"gt ltsa_IsPersongt?lt/sa_IsPersongt
ltsa_Nationalitygt?lt/sa_Nationalitygt
ltsa_Identitygt??lt/sa_Identitygt lt/sAgentgt
ltsAction rdfIDAction01"gt
ltsVerbgt??lt/sVerbgt lt/sActiongt
ltsTime rdfID"???226?"gt
ltsWtypegt???lt/sWtypegt ..
ltsWNumgt226lt/sWnumgt ..
lt/sTimegt lt/rdfRDFgt
60
Linguistic Analysis of Sentences
Original ?????????,?????????????, ?????????????
Result ?????????, ?????????, ????? ??? is
the subject of ?, ?, and ??.
61
Query representation

• We use some selection functions for users to
  fulfill what might related to their queries by
  choosing the suitable items.
• Understanding the requirements of users becomes
  more consistent and less effort.

62
Query Template on Interface
63
Query Over Ontology
instance of concept
SubClassof
Action
Person
Object
Location
Time
Verb
Theme
Agent
instances
??
??
??
64
Query Over Ontology

• For example
• ??????
• Instances are ?? ? ??
• Even ?? and ? are not syntactically the same
  but is semantic meaning
• We use query schema to recognize the meaning of
  users query.

65
Examples
66
Query Interface
67
Who-queries
68
What-queries
69
Where-queries
70
When-queries
71
Current Results

• Query types include Who, What, Where and When
  questions
• 55 simple historical questions
• The returned answers are 40 for correct
• 15 for incorrect.

72
Advantages

• Query Schema-Like Interface
• split a simple question into several components
  by query schemas
• Using Thesaurus and Ontology
• Deal with synonyms and different syntactical
  structures
• The Inference by the Relations of Concepts
• ?????, ????????

73
Weakness

• Erroneous Linguistic Analysis
• ??????????,?????????,?????????
• An inverted sentence
• ?????????
• Ontology Incompleteness
• ?????,???????
• ???????

74
Conclusions

• Agents require domain knowledge to retrieve and
  extract information
• Building sharable ontology will ensure
  information agents to interpret domain
  information in the right context and semantics
• Semantic web concepts provide a
• feasible environment for various agents to
  behave and share and exchange knowledge with each
  other

75
Conclusions

• We design a framework that can retrieve annotated
  information using sharable domain ontology and
  thesaurus.
• The sharable domain ontology in RDF schemas.
• A query parser that parses NL queries into query
  schemas in terms of XML format.
• Tools for annotating the information into RDF
  instances.
• Tools for augmenting a Chinese thesaurus of
  general domain with lexical items.
• Heuristic algorithms to match the RDF queries
  with annotated images and documents.

76
ACKNOWLEDGMENT

• Colleagues
• National Tsing Hua University, Taiwan
• Von-Wun Soo,
• Chen-Yu Lee,
• Chao-Ming Lin
• Chao-Chun Yeh
• National Cheng-Chih University, Taiwan
• Jih-Shane Liu
• Simmons College, USA
• Ching-Chih Chen

• GRNATS
• MOE Programs of promoting academic excellence of
  universities project number 89-E-FA04-1-4
• NSC International Digital Library project (IDLP)
  NSC 90-2750-H-002-734
• (in collaboration with US NSF Chinese
  Memory Net project)