From%20semantic%20networks,%20to%20ontologies,%20and%20concept%20maps:%20knowledge%20tools%20in%20digital%20libraries - PowerPoint PPT Presentation

About This Presentation
Title:

From%20semantic%20networks,%20to%20ontologies,%20and%20concept%20maps:%20knowledge%20tools%20in%20digital%20libraries

Description:

From semantic networks, to ontologies, and concept maps: ... Marcos Andr Gon alves. Digital Library Research Laboratory. Virginia Tech. Outline. Introduction ... – PowerPoint PPT presentation

Number of Views:65
Avg rating:3.0/5.0
Slides: 45
Provided by: marcos167
Category:

less

Transcript and Presenter's Notes

Title: From%20semantic%20networks,%20to%20ontologies,%20and%20concept%20maps:%20knowledge%20tools%20in%20digital%20libraries


1
From semantic networks, to ontologies, and
concept maps knowledge tools in digital libraries
  • Marcos André Gonçalves
  • Digital Library Research Laboratory
  • Virginia Tech

2
Outline
  • Introduction
  • Semantic Networks in Information Retrieval
  • The MARIAN system
  • Digital Library Ontologies
  • Concepts maps knowledge representation and
    visualization in DLs

3
Introduction
  • Experiment how new knowledge representation tools
    can be used in Digital Libraries
  • Semantic networks
  • Representation, retrieval and inference of DL
    constructs and relationships
  • Ontologies
  • Formalize, model and generate DLs
  • Concept Maps
  • Visualization tool
  • Supporting collaborative work
  • Transforming information to knowledge creation

4
Outline
  • Introduction
  • Semantic Networks in Information Retrieval
  • The MARIAN system
  • Digital Library Ontologies
  • Concepts maps knowledge representation and
    visualization in DLs

5
Semantic Networks in DLs MARIAN
  • Motivation
  • Support rich DL information services which are
  • Extensible
  • Tailorable
  • Support large, diverse collections of digital
    objectives which
  • have complex internal structures
  • are in complex relationships with each other and
    with other non-library objects such as persons,
    institutions, and events

6
Design choices
Design choices Objective Examples of use
Semantic networks Basic, unified representation of digital library structures Document and metadata structure hierarchical relationships of classification systems concept maps
Weighting schemes Support IR operations and services quantitative representation of qualitative properties (similarity, uncertainty, quality) Weighted links representing indexes multi-field, multi-word, fusion of weighted IR sets degree of similarity among concepts in different ontologies
Object oriented class system Provide common behavior, extensibility, and opportunity for improved performance Shared methods for matching different types of nodes (terms, controlled, free texts) and link topologies multilingual support and common presentation methods
Lazy evaluation Performance management of large collections Reduced number of search results enhanced merging algorithms for weighted sets of searching results
7
Design choices semantic networks
  • Represent knowledge in patterns of interconnected
    nodes
  • Graph representation to express knowledge or to
    support automated systems for reasoning
  • Sowas classification
  • Definitional networks
  • Inheritance hierarchies
  • Assertional networks
  • Assert propositions
  • Implicational networks
  • Implication as the primary relation
  • Executable networks
  • Mechanism to pass messages (tokens, weights)
  • Learning networks
  • Modify internal representations (weights,
    structure)
  • Ability to measure similarity
  • Hybrid networks

8
Design choices MARIAN semantic network
occursInAuthor
Person
hasAuthor
term
ETD Metadata
occursInAbstract
hasAbstract
term
id
Abstract
hasSubject
term
Subject
occursInAbstract
describes
hasParagraph
ETD Doc
hasSection
term
hasChapter
Paragraph
Section
occursInSubject
id
Chapter
cites
term
Section
Paragraph
Paper
occursInParagraph


id
9
MARIAN API (Main)
ClassMgr


termClassMgr

linkClassMgr

nodeClassMgr



unwtdLink

wtdLink


ClassMgr

ClassMgr



SpanishRoot
EnglishRoot

occursIn

has

TextClassMgr

ClassMgr

ClassMgr

ClassMgr

ClassMgr




EnglishText

SpanishText

ChineseText

controlledText
ClassMgr

ClassMgr

ClassMgr

ClassMgr

10
Architecture and Implementation (cont.)
  • The Search layer
  • Mapping from abstract object description to
    weighted set of objects
  • Types of search
  • Link activation
  • Search in context
  • Searchers
  • OO search engines
  • Based on fusion
  • Examples maximizing union searcher, summative
    union searcher
  • Supported by
  • Tables short-term memory of elements seen to
    date, checking each new element to keep or
    discard
  • Sequencers take a set of incoming streams of
    weighted sets and produce single output. Exs
    PriQueueSequencer, MergeSequencer.

11
Architecture and Implementation (cont.)
  • The Search layer

OccursIn Abstract Searcher
1
Parser (Morphological matcher)
Digital
occursInAbstract
200660812
Library
Abstract
200642369
hasTitle
E. A . Fox
query
6029656551.00, 60299890.74,

602930000.85, 6029656550.8

2
200774667
1
hasAdvisor
OccursIn Advisor Searcher
Advisor
Summative Union Searcher
6015656550.90, 601530000.425 60159890.37
,
occursInAdvisor
3
6031456341.0, 603156780.9,

2
4
4
Summative Union Searcher
hasAbstract Searcher
hasAdvisor Searcher
60008560.90, 600078900425,

6000545441.0, 600029870.9 60000030.74,

5
5
Final result set
6
12
Future Work
  • Testing of
  • Efficiency
  • OO class-model vs. instance level semantic
    network
  • Lazy evaluation
  • Tables and sequencers
  • Effectiveness with
  • Structured documents and metadata
  • Fulltext
  • Supporting richer networks of relationships
  • Citation linking
  • Multi-language term relationships

13
Future Work
  • Support for other types of networks and
    graph-based digital objects and structures
  • Belief networks
  • Topic/Concept maps
  • Ontologies, classification schemes
  • Supporting multimedia retrieval
  • Supporting for CLIR

14
Outline
  • Introduction
  • Semantic Networks in Information Retrieval
  • The MARIAN system
  • Digital Library Ontologies
  • Concepts maps knowledge representation and
    visualization in DLs

15
Ontologies for DLs
  • Motivation
  • DLs are an ill-understood phenomena
  • Lack of formal models for DLs
  • Ad-hoc development, interoperability
  • Formal Ontologies for DLs
  • specify relevant concepts the types of things
    and their properties and the semantics
    relationships that exist between those concepts
    in a particular domain.
  • use a language with a mathematically well-defined
    syntax and semantics to describe such concepts,
    properties, and relationships precisely

16
5S Model (informally)
  • Digital libraries are complex information systems
    that
  • help satisfy info needs of users (societies)
  • provide info services (scenarios)
  • organize info in usable ways (structures)
  • present info in usable ways (spaces)
  • communicate info with users (streams)

17
5S Model
Models Examples Objectives
Stream Text video audio image Describes properties of the DL content such as encoding and language for textual material or particular forms of multimedia data
Structures Collection catalog hypertext document metadata organization tools Specifies organizational aspects of the DL content
Spatial Measure measurable, topological, vector, probabilistic Defines logical and presentational views of several DL components
Scenarios Searching, browsing, recommending, Details the behavior of DL services
Societies Service managers, learners, Teachers, etc. Defines managers, responsible for running DL services actors, that use those services and relationships among them
18
5S Model Mathematical formal theory for DLs
5S Definition
Streams Sequences of elements of an arbitrary type
Structures Labeled directed graphs
Spatial Sets and operations on those sets
Scenarios sequences of events that modify states of a computation in order to accomplish some functional requirement.
Societies Sets of communities and relationships among them
19
measurable, measure, probability, vector,
topological spaces
relation
tuple
sequence
graph
sequence
state
event
function
5S
grammar
structures
streams
spaces
scenarios
societies
services
structured stream
structural metadata specification
descriptive metadata specification
indexing service
browsing service
searching service
digital object
hypertext
metadata catalog
transmission
digital library (minimal)
collection
repository
20
Ontologies for DLs
21
Ontologies for DLs
  • Realizations of the theory/ontology
  • Meta-Model for a DL descriptive modeling
    language 5SL (JCDL2002)
  • Meta-Model for a DL Visual modeling Tool 5SGraph
    (ECDL2003)
  • Meta-Model for an XML Log Standard (ECDL2002,
    JCDL2003)

22
Realizations of the theory/ontology
  • 5S Meta-Schema

23
Realizations of the theory/ontology
  • 5SGraph Interface

24
Future Work
  • Semantic relationships
  • Only syntactic ones were defined
  • Constraints and dependencies (in form of axioms)
  • Taxonomy of services
  • Composability, Extensibility
  • Formal definitions of properties of DL
    models/architectures and proofs
  • Completeness
  • Soundness
  • Equivalence

25
Outline
  • Introduction
  • Semantic Networks in Information Retrieval
  • The MARIAN system
  • Digital Library Ontologies
  • Concepts maps knowledge representation and
    visualization in DLs

26
Concepts maps knowledge representation and
visualization in DLs
  • Challenges in Visual Interfaces for DLs (Chen
    Borner)
  • Supporting collaborative work
  • Transforming information to knowledge creation
  • Hypothesis Concepts maps can serve as a uniform
    visual abstraction to provide solutions for these
    problems.

27
What are concept maps
28
Applications
  • Knowledge organization and creation
  • Collaborative learning
  • GetSmart Experience (JCDL2003)
  • Domain summarization
  • Browsing tool

29
Knowledge Repository
30
GetSmart Experience (Cont.)
  • Collaborative learning Group maps

31
GetSmart Experience (Cont.)
  • Summarization tool

32
Summarization tool
  • Supplement to document abstracts both for one
    language and across language
  • ----pilot experiment

Group 1(14) Group 2 (14)
English papers Original abstract Original abstract concept map
Spanish papers Original abstract plus translated version Original abstract plus machine translated version plus translated concept map
33
Summarization tool (Cont.)
  • Pilot experiment results

Group 1(14) average Group 2 (14) average P-value
Q1 (English) 1.6631 1.3839 0.527
Q2 (English) 1.6599 1.1310 0.185
Q3 (Spanish) 1.7085 1.1039 0.209
Q4 (Spanish) 1.6815 0.9831 0.030
Likert (English) N/A 3.6, 4.4 0.022
Likert (English) N/A 2.7, 4.3 0.001
34
Automatic generation
  • Motivation
  • Automatic concept map is tedious and
    time-consuming
  • Novices will draw flawed or overly simplistic map
  • Maintain uniformity
  • Technique
  • Term co-occurrence (Gaines Shaw)

35
(No Transcript)
36
Automatic generation (Cont.)
  • Spanish documents
  • Procedure
  • Determine part-of-speech for each word
  • Collapse all inflected forms to root form
  • Concatenate noun phrases into one concept
  • Remove some stopwords, keep others for use in
    crosslinks

37
(No Transcript)
38
(No Transcript)
39
Browsing tools
  • Visual aid to navigate through complex
    collections of
  • inter-related digital objects
  • Support Multi-hierarchy browsing

40
(No Transcript)
41
(No Transcript)
42
Concept Maps supports for DL (cont.)
  • Browsing and searching assistant

43
Future Work
  • Improve the quality of automatic created concept
    maps
  • Create repository of maps
  • Provide services over the repository

44
Thank you
Write a Comment
User Comments (0)
About PowerShow.com