from semantic networks, to ontologies, and concept maps: knowledge tools in digital libraries marcos...
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From semantic networks, to ontologies, and concept maps: knowledge tools in digital libraries
Marcos André GonçalvesDigital Library Research Laboratory
Virginia Tech
Outline Introduction Semantic Networks in Information
Retrieval The MARIAN system
Digital Library Ontologies Concepts maps: knowledge representation
and visualization in DLs
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
Outline Introduction Semantic Networks in Information
Retrieval The MARIAN system
Digital Library Ontologies Concepts maps: knowledge representation
and visualization in DLs
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
Design choicesDesign
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
Design choices: semantic networks Represent knowledge in patterns of interconnected nodes Graph representation to express knowledge or to support automated
systems for reasoning Sowa’s 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
Design choices: MARIAN semantic network
ETD Metadata
Person
Subject
Abstract
ETD Doc
Chapter
id
id
hasAuthor
hasChapter
hasSubject
occursInAuthor
occursInAbstract
occursInAbstract
occursInSubject
term
term
term
term
term
term
Section
Section
…
Paragraph
Paragraph
…Paper
id
cites
occursInParagraph
hasSection hasParagraph
describes
hasAbstract
MARIAN API (Main)
ClassMgr
occursIn* ClassMgr
has* ClassMgr
TextClassMgr
EnglishRoot ClassMgr
SpanishRoot
ClassMgr
unwtdLink ClassMgr
wtdLink ClassMgr
linkClassMgr
nodeClassMgr
termClassMgr
controlledText ClassMgr
EnglishText ClassMgr
SpanishText ClassMgr
ChineseText ClassMgr
nGramClassMgr
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.
Architecture and Implementation (cont.) The Search layer
hasTitle
query
Abstract
Advisor
occursInAbstract
hasAdvisor
occursInAdvisor
#2006:42369
#2006:60812Digital
LibraryParser
(Morphological matcher)
E. A . Fox
#2007:74667
OccursInAbstractSearcher
{#6031:45634:1.0,#6031:5678:0.9,
… }
OccursInAdvisorSearcher
{#6029:65655:1.00,#6029:989:0.74,
… }
{#6029:3000:0.85,#6029:65655:0.8
… }
SummativeUnion
Searcher
{#6015:65655:0.90,#6015:3000:0.425#6015:989:0.37,
… }
hasAdvisor Searcher hasAbstract
Searcher
{#6000:54544:1.0,#6000:2987:0.9#6000:003:0.74,… }
{#6000:856:0.90,#6000:7890:0425,… }
SummativeUnion
Searcher
Final result set
1
1
2
2
4 4
3
55
6
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
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
Outline Introduction Semantic Networks in Information
Retrieval The MARIAN system
Digital Library Ontologies Concepts maps: knowledge representation
and visualization in DLs
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
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)
5S ModelModels 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
5S Model: Mathematical formal theory for DLs5S 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
5S
structures streams spaces scenarios societies
structural metadataspecification
descriptive metadataspecification
repository
collection
indexingservice
structured stream
digitalobject
metadata catalog
browsingservice
searchingservice
digital library(minimal)
services
sequence
graph
function
measurable, measure, probability, vector, topological spaces
event state
hypertext
sequence
transmission
relation
grammar
tuple
Ontologies for DLs
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)
Realizations of the theory/ontology 5S Meta-Schema
5S
Structural Model
Space Model
Scenario Model
Society Model
Stream Model
* Text
* Image
* Video
* Audio
* Application
* Collection
* Catalog
* Organizational Tool
* Document
* Metadata * Authority File
* Classification Schema
* Thesaurus
* OntologyUser Interface
Retrieval Model
* Rendering
* Index
* Services * Scenarios
* Actor
* Manager
Realizations of the theory/ontology 5SGraph Interface
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
Outline Introduction Semantic Networks in Information
Retrieval The MARIAN system
Digital Library Ontologies Concepts maps: knowledge representation
and visualization in DLs
Concepts maps: knowledge representation and visualization in DLs
Challenges in Visual Interfaces for DLs (Chen & Borner)
1. Supporting collaborative work
2. Transforming information to knowledge creation
Hypothesis: Concepts maps can serve as a uniform visual abstraction to provide solutions for these problems.
What are concept maps
Applications:
1. Knowledge organization and creation
2. Collaborative learning GetSmart Experience (JCDL2003)
3. Domain summarization
4. Browsing tool
Knowledge Repository
Data
information
knowledge
DL
Knowledge repository
Information provider
GetSmart Experience (Cont.) Collaborative learning: Group maps
GetSmart Experience (Cont.) Summarization tool
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
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 *
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)
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
Browsing tools
• Visual aid to navigate through complex collections of inter-related digital objects
• Support Multi-hierarchy browsing
Concept Maps’ supports for DL (cont.) Browsing and searching assistant
CCS:: Information Storage and Retrieval
CCS:: Online Information Services
CCS:: Library Automation
CCS:: Digital Libraries
super class of
CoRR:: Digital Libraries
cross mapping toCCS:: Information Storage and Retrieval
CCS:: Online Information Services
CCS:: Library Automation
CCS:: Digital Libraries
CCS:: Information Storage and Retrieval
CCS:: Online Information Services
CCS:: Library Automation
CCS:: Digital Libraries
CCS:: Information Storage and RetrievalCCS:: Information Storage and Retrieval
CCS:: Online Information ServicesCCS:: Online Information Services
CCS:: Library AutomationCCS:: Library Automation
CCS:: Digital LibrariesCCS:: Digital Libraries
super class ofsuper class of
CoRR:: Digital LibrariesCoRR:: Digital LibrariesCoRR:: Digital Libraries
cross mapping tocross mapping to
Future Work Improve the quality of automatic created
concept maps Create repository of maps Provide services over the repository