Building a More Meaningful Web: From Traditional Knowledge
Organization Systems to New Semantic Tools
Dagobert Soergel
College of Information Studies
University of Maryland

The 6th Networked Knowledge Organization Systems/Sources (NKOS) Workshop, organizedby Gail Hodge (gailhodge@aol.com), Dagobert Soergel (dsoergel@umd.edu), and Marcia Zeng(mzeng@kent.edu) was held on May 31, 2003 in Houston, TX as part of JCDL 2003. It focused on• transforming traditional knowledge organization systems (KOSs), such as classification schemes
and thesauri, to new forms of knowledge representation, such as ontologies and topic maps, thatcan be used for AI and semantic Web applications and for sophisticated retrieval and learning,thus• Leveraging the considerable intellectual capital available in existing KOS for lowering the cost ofdeveloping knowledge-intensive applications
The following papers were presented. (http://nkos.slis.kent.edu)
From legacy knowledge organization systems to full-fledged ontologies
Dagobert Soergel, U. of MD
Reengineering AGROVOC to Ontologies. Towards better semantic structure
F. Fisseha, A. Liang, J. Keizer, FAO
From semantic networks, to ontologies, and concept maps: knowledge tools in digital
libraries.
M. A. Gonçalves, Digital Library Res. Lab., VATech
Using the NASA Thesaurus to Support the Indexing of Streaming Media
Gail Hodge, Janet Ormes, Patrick Healey, NASA Goddard
Concept-based Learning Spaces. Apply domain-specific KOS principles for organizingcollections/services for given applications
Terence Smith, UC Santa Barbara, Marcia Lei Zeng, Kent State Univ.; Alexandria Digital LibraryProjectWeb Services and Terminology.
Adam Farquhar, SchlumbergerSema
Update on Revision to the NISO Z39.19 Thesaurus Standard and Other Terminology
StandardsAmy Warner, Lexonomy, Inc./consultant to NISOThis report gives highlights from four papers, two on reengineering legacy KOS and two on
applications of the rich structure of ontologies.

Reengineering thesauri to ontologies
Reengineering thesauri, classification schemes etc. into ontologies means to build on the information
contained in them and refining it as needed. Consider the following statements in a hypothetical
ontology (Soergel):Reading instruction
Reading instruction
Reading instructionisa
has domain
governed byInstruction
Reading
Learning standards

Reading ability isa
Ability
Reading ability has domain Reading
Reading ability supported by Perception
and the following rules
Rule 1
If X isa (type of) instruction and X has domain Zand Y isa ability and Y has domain Z
Then X should consider YRule 2
If X should consider Yand Y is supported by W
Then X should consider W
A system could infer that the designer of reading instruction should consider reading ability andperception. This inference relies on the detailed semantic relationships given in the ontology. But theERIC Thesaurus (ERIC = Educational Resources Information Center) gives us only the following:
Reading instruction
BT Instruction
RT Reading
RT Learning standards
Reading ability
BT Ability
RT Reading
RT Perception
The relationships are not differentiated enough to support inference. A few examples from FAO's
project to convert the AgroVoc Thesaurus into a richer ontology illustrate the point further (Fisseha):

Broader Term (BT) and Narrower Term (NT) relations in AGROVOC
BT and NT are typical hierarchical relations in athesaurus. However, their semantics is not explicitlydefined. It is common for BT/NT relations within
thesauri to include at least the following:Some examples from AGROVOCMILKNT Milk Fat
NT Colostrum
NT Cow’s Milk

Is-A (e.g. Milk/ Cow’s Milk;
Development Agency/IDRC)

Development Agencies
NT development banks
NT voluntary agencies
NT IDRC

Ingredient of (e.g. Milk/ Milk Fat)
Milk fat is an ingredient of milk

MAIZE
NT dent maize
NT flint maize
NT popcorn
NT soft maize
NT sweet corn
NT waxy maize

Property of (e.g. Maize/Sweet corn)
Sweetness is a property of corn

Related Term (RT) in AGROVOC
R T represents the associative relation. The RT
usually involves the most ambiguous semantics.
RT can include the following.

– causality– agency or instrument– hierarchy - where polyhierarchy has
not been allowed the missing
hierarchical relationships arereplaced by associative
relationships– sequence in time or space– constituency– characteristic feature– object of an action, process ordiscipline– location– similarity (in cases where two nearsynonyms have been included asdescriptors)– antonym

Some examples from AGROVOC
DEGRADATION••

RTchemical reactions
RT discoloration
RT hydrolysis
RT shrinkage

causalityIDRC••RT Canadalocation

Some ideas for reengineering AGROVOC
Most of the problems could be solved by:
1. Re-analyzing the existing relations to introduce explicit semantics. For instance,
BT/NT relationship could be resolved to ‘Is-A’ relation as the default and refined to more
specific relationships as needed
RT relationship could be refined to more specific relationships (such as produces, used by,made for).
2. Specifying composite concepts in terms of basic concepts that can be represented
unambiguously. For instance,
perishable product could be represented as product with attribute perishable
fencing sword could be represented as sword used for fencing
mother could be represented as parent with attribute female
From these ideas on reengineering a thesaurus, a three-step general procedure for converting legacy
Knowledge Organization Systems (KOS) into ontologies can be abstracted (Soergel):
1.

Define the ontology structure2.

Fill in values from one or more legacy KOS to the extent possible

3.

Edit manually using an ontology editor:• make existing information more precise• add new information

It was suggested that he laborious process of refining relationship could be streamlined by intelligent
conversion using “rules as you go” (Soergel). The idea is to have the editor watch out for patterns
and based on these patterns formulate rules that can be immediately applied to all similar cases as
illustrated in the following example:
If an editor has determined (or it is known from another source that there is a relationshipanimal has-part milk
it can be concluded that
cow NT cow's milk should become cow has-part cow's milk
since cow is an animal and cow's milk contains the word milk.
Similarly: rice RT moist soil becomes rice grows in moist soil (plant grows in soil type )This indicates that the reengineering effort should start with the topmost concepts.

Ontology applications
Zeng presents an educational environment built around a rich ontology>

Science learning spaces: Concept KOS
Concepts of science as basic knowledge granules• Sets of concepts form bases for scientific representation• DL and KOS technology can support organization of science learning materials in terms ofconcepts• Collections of models of science concepts (knowledge base)• Collections of learning objects (LO) cataloged with concepts• Collections of instructional materials organized by concepts
Organize learning materials as “trajectory through concept space”• Lecture, lab, self-paced materials• Services for creating/editing/displaying such materials

Learning environment display (lecture mode)

Concept window

Lecture window

Object windowconceptual model–relationships
Relationships

CotainedIn
SetMembership

HierarchicalPartitive

ScientificUse

Contains
IsPartOf
HasParts

Applications

ExplicitFull

HasRepresentation

ExplicitPartial

RepresentationImplicitFull
PartiallyRepresents

Defining
Operation

AbstractSyntactic

Property

HasProperty

ImplicitPartial

Methodological

PropertyOf
CoRelated
CausedBy
CausalCauses

otherFarquhar describes the use of an ontology in an organization.

Semantic Network Services
Sharing an integrated Ontology using Topic Maps and Web Services
Adam Farquhar (presenter)
KM Architect, Schlumberger, Austin, TX
Thomas Bandholtz
KM Solution Manager, SchlumbergerSema, Cologne (DE)Member, OASIS TC Published Subjects & GeoLang (Topic Maps)
Research project UFOPLAN-Ref. No. 20111612, promoted by BMU/Federal Environmental
Agency, Germany

Integration in a Topic Map
Topic
Topic

topic type
topic
association

Thesaurus
Thesaurus
Descriptor
Descriptor

Accident
Accident

Nuclear Energy

broader

Nuclear Accident

EventEvent

Location
Location

NationNationCommunity
Community

ex. USSR
UkraineChernobyl
radiation disaster1986-04-26

Chernobyl

situated in

where

what

occurrence

www.chernobyl.com/

sns: findTopics
search term
<findTopics><queryTerm>Mauersegler</queryTerm><searchType> contains</searchType><lang>de</lang>search method<path> /event</path><fields>names</fields></findTopics>

topic type pathfields to search

results in a list of matching topics

Conclusions
The workshop participants identified several follow-on activities:• Identify a core set of relationships that would further define the traditional RT with extensibility forspecific domain relationships.
• Develop data exchange/interchange formats in XML/RDF.
• Develop use cases to better define terminology Web services.
• Define a tool suite for converting traditional tools into intermediate formats that can support thedevelopment of new semantic tools.
• Evaluate the cost and benefits of reusing traditional tools versus building new tools "from scratch",particularly with regard to machine-to-machine in the KM environment.
• Investigate licensing versus public domain business models for terminology.
In summary, the workshop presented papers on how to convert legacy KOS to systems with richer,precisely defined semantics (ontologies) and papers on applications of such rich ontologies and shows a
direction the field should move in.
About NKOSNKOS is a community of researchers, developers and practitioners seeking to enable knowledgeorganization systems, such as classification systems, thesauri, gazetteers, and ontologies, as networked
interactive information services to support the description, retrieval, presentation, and use of diverse
information resources through the Internet. Presentations are available from the NKOS Web site(http://nkos.slis.kent.edu).