An intelligent expert system for location planning is proposed that uses semantic web technologies and a Bayesian network. The system integrates heterogeneous information through an ontology. It develops an integrated knowledge process to guide the engineering procedure. Based on a Bayesian network technique, the system recommends well-planned attractions to users.

1. Intelligent Expert Systems
for Location Planning
Daizhong Tang Jiangang Shi and Wei Wang
Nov 2014
Keywords:Expert Systerms,Location Planning, Bayesian Network

2. Abstract
2

3. Abstract
 Semantic Web technologies can support information
integration and create semantic mashups.
 Web 2.0 enabled contributions to the Web development
on an unprecedented scale
 Through the ontology, the expert system allows
integration of heterogeneous information.
 An intelligent expert system for location planning
 An integrated knowledge process is developed to
guarantee the whole engineering procedure.
 Based on Bayesian network technique, the system
recommends well planed attractions to a user.

4. Semantic Web
4

5. 5
The 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 co-operation.“
[Berners-Lee et al, 2001]

6. 6
Today’s Web
 Currently most of the Web content is suitable
for human use.
 Typical uses of the Web today are information
seeking, publishing, and using, searching for
people and products, shopping, reviewing
catalogues, etc.
 Dynamic pages generated based on information
from databases but without original information
structure found in databases.

7. 7
Limitations of the Web Search today
 The Web search results are high recall,
low precision.
 Results are highly sensitive to vocabulary.
 Results are single Web pages.
 Most of the publishing contents are not
structured to allow logical reasoning and
query answering.

8. 8
Today’s Web

9. 9
What is a Web of Data?
Thinking back a bit... 1994
HTML and URIs
Markup language and means
for connecting resources
Below the file level
Stopped at the text level
[Miller 04]

10. 10
What is a Web of Data?
(continued)
Now
XML, RDF, OWL and URIs
Markup language and means for
connecting resources
Below the file level
Below the text level
At the data level
[Miller 04]

11. 11
The Syntactic Web
[Hendler & Miller 02]

12. 12
i.e. the Syntactic Web is…
 A place where
 computers do the presentation (easy) and
 people do the linking and interpreting (hard).
 Why not get computers to do more of the
hard work?
[Goble, 03]

13. Web 2.0
13

14. 14
Web 2.0
 It is all about people, collaboration,
media, ...
[The mind-map pictured above constructed by Markus Angermeier, source Wikipedia]

15. 15
Web 2.0 and Folksonomies
[http://flickr.com/photos/tags/]

16. 16
Distinguishing the meaning
 It is simply difficult for machines to
distinguish the meaning of:
I am a philosopher.
from
I am a philosopher, you may think.
Well,…

17. 17
…Limitations of the Web today
The Web activities are mostly focus on Machine-to-Human,
and Machine-to-Machine activities are not particularly well
supported by software tools.
[Davies, 03]

18. 18
How Can the Current Situation be
Improved?
 An alternative approach is to represent
Web content in a form that is more easily
machine-accessible and to use intelligent
techniques to take advantage of these
presentations.

19. 19
Machine Accessible Meaning
CV
name
education
work
private
[Davies, 03]

20. 20
XML
<H1>Internet and World Wide Web</H1>
<UL>
<LI>Code: G52IWW
<LI>Students: Undergraduate
</UL>
<H1>Internet and World Wide Web</H1>
<UL>
<LI>Code: G52IWW
<LI>Students: Undergraduate
</UL>
HTML:
<module>
<title>Internet and World Wide Web</title>
<code>G52IWW</code>
<students>Undergraduate</students>
</module>
<module>
<title>Internet and World Wide Web</title>
<code>G52IWW</code>
<students>Undergraduate</students>
</module>
XML:
User definable and domain specific markup

21. 21
XML: Document = labeled tree
module
lecturertitle students
name weblink
<module date=“...”>
<title>...</title>
<lecturer>
<name>...</name>
<weblink>...</weblink>
</lecturer>
<students>...</students>
</module>
=
 DTD: describe the grammar and structure of
permissible XML trees
 node = label + contents

22. 22
But What about this?
CV
name
education
work
private
< >
< >
< >
< >
< >
< Χς >
< ναµε >
<εδυχατιον>
<ωορκ>
<πριϖατε>
[Davies, 03]

23. 23
XML
 Meaning of XML-Documents is intuitively clear
 due to "semantic" Mark-Up
 tags are domain-terms
 But, computers do not have intuition
 tag-names do not provide semantics for machines.
 DTDs or XML Schema specify the structure of
documents, not the meaning of the document contents
 XML lacks a semantic model
 has only a "surface model”, i.e. tree

24. 24
XML is a first step
 Semantic markup
 HTML  layout
 XML  content
 Metadata
 within documents, not across documents
 prescriptive, not descriptive
 No commitment on vocabulary and modelling
primitives
 RDF is the next step
[Davies, 03]

25. 25
RDF: Basic Ideas
 Statements
 A statement is an object-attribute-value
triple.
 It consists of a resources, a property, and a
value.
http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10140
publishedBy
#MIT Press

26. 26
RDF Schema: Basic Ideas
 RDF is a universal language that enables
users to describe their own vocabularies.
 But, RDF does not make assumption about
any particular domain.
 It is up to user to define this in RDF
schema.

27. 27
What does RDF Schema add?
• Defines vocabulary for RDF
• Organizes this vocabulary in a typed hierarchy
• Class, subClassOf, type
• Property, subPropertyOf
• domain, range
AlanTom
Staff
Lecturer Research Assistant
subClassOf
subClassOf
type
supervisedBy
domain range
type
supervisedBy
[adapted from: Studer et al, 04]
Schema(RDFS)
Data(RDF)

28. 28
Basic Queries
 The example provided in RQL.
 Using select-from-where
 select specifies the number and order of
retrieved data.
 from is used to navigate through the data
model.
 where imposes constraints on possible
solutions

29. 29
Basic Queries: Example
select X,Y
From {X} writtenBy {Y}
X, Y are variables, {X} writtenBy {Y}
represents a resource-property-value
triple

30. Ontology
30

31. 31
Ontologies
 The term ontology is originated from
philosophy. In that context it is used as
the name of a subfield of philosophy,
namely, the study of the nature of
existence.
 For the Semantic Web purpose:
 “An ontology is an explicit and formal
specification of a conceptualisation”.
(R. Studer)

32. 32
Ontologies and Semantic Web
 In general, an ontology describes formally a
domain of discourse.
 An ontology consists of a finite list of terms and
the relationships between the terms.
 The terms denote important concepts classes of
objects of the domain.
 For example, in a Tourism, Transportation,
Attraction, Culture, Shopping, General
information, Accommodation, Dinning, and
News & Events are some important concepts.

33. 33
OntologyF-Logic
similar
OntologyF-Logic
similar
PhD StudentDoktoral Student
Object
Person Topic Document
Tel
PhD StudentPhD Student
Semantics
knows described_in
writes
Affiliation
described_in is_about
knowsP writes D is_about T P T
DT T D
Rules
subTopicOf
• Major Paradigms: Logic Programming, Description Logic
• Standards: RDF(S); OWL
ResearcherStudent
instance_of
is_a
is_a
is_a
Affiliation
Affiliation
Siggi
AIFB+49 721 608 6554
A Sample Ontology
[Studer et al, 04]

34. 34
PhD StudentPhD Student AssProfAssProf
AcademicStaffAcademicStaff
rdfs:subClassOfrdfs:subClassOf
cooperate_withcooperate_with
rdfs:range
rdfs:domain
Ontology
<swrc:AssProf rdf:ID="sst">
<swrc:name>Steffen Staab
</swrc:name>
...
</swrc:AssProf>
http://www.aifb.uni-karlsruhe.de/WBS/sst
Anno-
tation
<swrc:PhD_Student rdf:ID="sha">
<swrc:name>Siegfried
Handschuh</swrc:name>
...
</swrc:PhD_Student>
Web
Page
http://www.aifb.uni-karlsruhe.de/WBS/shaURL
<swrc:cooperate_with rdf:resource =
"http://www.aifb.uni-
karlsruhe.de/WBS/sst#sst"/>
instance of
instance
of
Cooperate_with
Ontology & Annotation
Links have explicit meanings!
[Studer et al, 04]

35. 35
Ontologies (OWL)
 RDFS is useful, but does not solve all possible
requirements
 Complex applications may want more possibilities:
 similarity and/or differences of terms (properties or classes)
 construct classes, not just name them
 can a program reason about some terms? E.g.:
 “if «Person» resources «A» and «B» have the same «foaf:email»
property, then «A» and «B» are identical”
 etc.
 This lead to the development of OWL (Web Ontology
Language)
source: Introduction to the Semantic Web, Ivan Herman, W3C

36. 36
Ontology Languages for the Web
 RDF Schema is a vocabulary description
language for describing properties and
classes of RDF resources, with a
semantics for generalization hierarchies
of such properties and classes.
 OWL is a richer vocabulary description
language for describing properties and
classes.

37. 37
Classes in OWL
 In RDFS, you can subclass existing
classes… that’s all.
 In OWL, you can construct classes from
existing ones:
 enumerate its content
 through intersection, union, complement
 through property restrictions
source: Introduction to the Semantic Web, Ivan Herman, W3C

38. Semantic Web
Services
38

39. 39
Web Services
 Web Services provide data and services to other
applications.
 Thee applications access Web Services via
standard Web Formats (HTTP, HTML, XML, and
SOAP), with no need to know how the Web
Service itself is implemented.
 You can imagine a web service like a remote
procedure call (RPC) which it returns a
message in an XML format.

40. 40
The Promise of Web Services
[Stollberg et al., 05]

41. 41
Semantic Web Technology
+
Web Service Technology
Semantic Web Services
=> Semantic Web Services as integrated solution for
realizing the vision of the next generation of the Web
• allow machine supported data interpretation
• ontologies as data model
automated discovery, selection, composition,
and web-based execution of services
[Stollberg et al., 05]

42. Bayesian Network
42

43. 43
Why the Excitement?
 What are they?
 Bayesian nets are a network-based framework for representing and
analyzing models involving uncertainty
 What are they used for?
 Intelligent decision aids, data fusion, feature recognition, intelligent
diagnostic aids, automated free text understanding, data mining
 Where did they come from?
 Cross fertilization of ideas between the artificial intelligence, decision
analysis, and statistic communities
 Why the sudden interest?
 Development of propagation algorithms followed by availability of easy
to use commercial software
 Growing number of creative applications
 How are they different from other knowledge representation and
probabilistic analysis tools?
 uncertainty is handled in mathematically rigorous yet efficient and
simple way
 representation of problems, use of Bayesian statistics, and the synergy
between these

44. 44
Bayes Rule
 Based on definition of conditional probability
 p(Ai|E) is posterior probability given evidence E
 p(Ai) is the prior probability
 P(E|Ai) is the likelihood of the evidence given Ai
 p(E) is the preposterior probability of the evidence
A1
A2 A3 A4
A5A6
E
∑∑

45. 45
BN Software
 Protégé addin for BN’s Export
 Netica have a API for use BN in another
Applications (have demo)
 GENIE on SMILE is opensource
 100+ Program is developed with source and
without source can use in projects

46. Knowledge
Engineering
46

47. 47
Semantic Web & Knowledge
Management
 Organising knowledge in conceptual
spaces according to its meaning.
 Enabling automated tools to check for
inconsistencies and extracting new
knowledge.
 Replacing query-based search with query
answering.
 Defining who may view certain parts of
information

48. 48
Knowledge Engineering Process
 These stages are done iteratively
 Stops when further expert input is no
longer cost effective
 Process is difficult and time consuming
 As yet, not well integrated with methods
and tools developed by the Intelligent
Decision Support community
[S.O. Rezend et al.,2000 WIT Press]

49. 49
Knowledge discovery
 There is much interest in automated
methods for learning BNS from data
 parameters, structure (causal discovery)
 Computationally complex problem, so
current methods have practical
limitations
 e.g. limit number of states, require variable
ordering constraints, do not specify all arc
directions
 Evaluation methods
[S.O. Rezend et al.,2000 WIT Press]

50. 50
The knowledge engineering
process
 1. Building the BN
 variables, structure, parameters, preferences
 combination of expert elicitation and knowledge
discovery
 2. Validation/Evaluation
 case-based, sensitivity analysis, accuracy testing
 3. Field Testing
 alpha/beta testing, acceptance testing
 4. Industrial Use
 collection of statistics
 5. Refinement
 Updating procedures, regression testing
[S.O. Rezend et al.,2000 WIT Press]

51. Overview of the
system
51

52. 52
Overview of the system
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 divided into 3 parts
 Firstly ,is the
metadata consisting of
preference profile and
transaction profile.
 Secondly, the
information repository
of Ontology was build.
 Finally, the interface
is the part for user
query.
25872 pages(last)
105809 pages(today)

53. 53
Ontology Building
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 Ontology is the central mechanism of the
system.
 Tourist information and service resources are
classified according to a common ontology
 As currently there is no existing commonly adopted
ontology for tourism.
 needs the expertise of experienced tourist
consultants
 Once the ontology framework is established,
automated method could be used to replace human
effort

54. 54
Ontology engineering
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Locationonto
ʘZÀ˜Êf˜Å½Z¼fyZ˜
 more than 80 websites for location planning in
China
 Web developers often group related contents
into categories.
 collected a number of websites Yahoo!
Directory. After removing duplicates, we were
left with 232 websites.
 filtering and grouping similar terms to create
upper level ontology.

55. 55
Ontology engineering
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 After collecting and analyzing :
 Web Ontology Language (OWL)
 recommended by the World Wide
Web Consortium (W3C)
 used to represent the ontology due to
its capability of explicitly representing
the concepts and their relationships.
 The travel ontology is
modeled using Protege
 Protégé is a free, open-source
platform with
 a friendly user interface that provides
a set of tools

56. 56
Tourism.OWL
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57. 57
Estimating user preferences
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Locationonto
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 After ontology building is performed,we can
construct a Bayesian network for modelling users
preferences.
 nodes selection
 topology building
 parameters setting
predefined regarding the ontologies
Survey or Learn

58. 58
Qualitative, Quantitative
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 the relevant variables are defined
 relationships between the variables have to be
established
 Bayesian Network is Published
 probability distributions assign
 conditional probability tables set
 using Bayes theorem
 Update data

59. 59
Integrated Bayesian
Knowledge
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Locationonto
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 a spiral engineering process is necessary
 developed an integrated Bayesian knowledge
engineering process (I-BKEP)

60. 60
Integrated Bayesian
Knowledge
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61. 61
Sensitive Analysis Methods
for I-BKEP
 Sensitivity analysis can be quantified
using two types of measures:
 entropy : used to evaluate the uncertainty or
randomness of a variable X characterized by a
probability distribution
 mutual information : measures the amount of
information one random variable contains about
another

62. System
implementation
62

63. 63
System implementation
 Ruby Programming Language
 Rails addin for web developing
 ROR framework
 Ruby Meta-Programming support
 Simple use of a Web services(REST,RSS,Atom)
 Three-tiered way implementation
 Standard Web site
 Two Type of server : application server and
the Web server
 spatial Web services

64. 64
prototype of this system
 Apache Server
 Ruby on Rails and Google Map API
 OpenStreetMap Code
 The Netica API Programmers Library, is
embedded in the Web server side to estimate a
travelers preferences in a Bayesian network.
 Integrated information about tourist
attractions is represented in OWL

65. 65
Scenario
 Eric is living in New York and he wants to go to
Tongji university by airplane today to attend a
academic conference. We know that his
preference are horse riding, golf, swimming in
order from profile. However, it might be rainy
in Shanghai. Please make a location planning
for him.

66. 66
Scenario in OWL
 Result of OWL Select Query:

67. 67
Sensitivity analysis For Each BN’s Node
 personality and
motivation are the
variables having the
greatest influence
on the whole
network.
 analysis results can
be checked by
domain expert
(agreed)and utilized
in the next iteration.

68. Idea
68

69. 69
Intelligent Expert Systems for Location
Planning Based on Smart Phone Data
 increase of mobile network users, the
development of mobile networks and the
occurrence of new information service
 As for as customers and users, they have
different demands to information due to
different interests, different purpose and
different environment.
 A good number of works have been conducted
in location based services domain for addressing
various problems

70. 70
Intelligent Expert Systems for Location
Planning Based on Smart Phone Data
 mining user's interest can find users
behavior on smart phone data such as:
 Location roaming
 Searching in search engines
 Installing applications
 Browsing in Browsers
 Using social networks

71. 71
Intelligent Expert Systems for Location
Planning Based on Smart Phone Data
 Add accuracy to location planning with
adding Probability of User interesting in
Bayesian network and set this value with
users daily behavior.
 Find user interest with k-center
algorithm.

72. 72
Intelligent Expert Systems for Location
Planning Based on Smart Phone Data
 Building mobile user's interests model
 1) Analyze user's logs, include detailed information
about downloading news, the mobile user's location
and the URLs that the user downloads.
 2) Compute the user's interests degree to every
category in a day
 3) Produce the matrix of user's interests: is defined
as a matrix which is made of k rows and d lists
 4) Compute the user's base interest degree on the
kth category
 5) Arraign the categories according to the results
calculated above

73. 73
Intelligent Expert Systems for Location
Planning Based on Smart Phone Data
 User interest(Based ontology) categories:
 Social
 Sports
 Estate
 Movie
 International(culture)
 Technology and Science
 Games
 Politic

74. 74
Intelligent Expert Systems for Location
Planning Based on Smart Phone Data
 Implementation:
 Android OS Platform(85% global market)
 OpenStreetMap Foundation(opensource,Bing)
 Netica API(Bayesian Network using in server)
 PHP(Web 2.0 server)
 REST,JSON,API,OWL,XML Compatible
 Apache Web server
 Protégé(Ontology Development)

75. ˜˜˜˜˜˜
75