From semantic networks, to ontologies, and concept maps: knowledge tools in digital libraries Marcos André Gonçalves Digital 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 choices Design choices ObjectiveExamples 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 hasAuthor hasChapter hasSubject occursInAuthor occursInAbstract occursInSubject term Section … 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 nGram ClassMgr

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 #2006:60812 Digital Library Parser (Morphological matcher) E. A. Fox #2007:74667 OccursIn Abstract Searcher {#6031:45634:1.0, #6031:5678:0.9, … } OccursIn Advisor Searcher {#6029:65655:1.00, #6029:989:0.74, … } {#6029:3000:0.85, #6029:65655:0.8 … } Summative Union 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, … } Summative Union Searcher Final result set

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 Model ModelsExamplesObjectives Stream Text; video; audio; imageDescribes 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 DLs 5SDefinition StreamsSequences of elements of an arbitrary type StructuresLabeled directed graphs SpatialSets and operations on those sets Scenariossequences of events that modify states of a computation in order to accomplish some functional requirement. SocietiesSets of communities and relationships among them

5S structuresstreamsspacesscenariossocieties structural metadata specification descriptive metadata specification repository collection indexing service structured stream digital object metadata catalog browsing service searching service digital library (minimal) services sequence graph function measurable, measure, probability, vector, topological spaces event state hypertext sequence transmission relation grammar tuple

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

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

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

Supplement to document abstracts both for one language and across language ----pilot experiment Group 1(14)Group 2 (14) English papersOriginal abstract concept map Spanish papersOriginal 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) Q2 (English) Q3 (Spanish) Q4 (Spanish) * Likert (English)N/A3.6, * Likert (English)N/A2.7, *

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

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