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

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





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





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





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:: Information Storage and Retrieval




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

Documents

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