1、Brief report on NKOS at JCDL2003,NKOS ECDL 2003Dagobert Soergel College of Information Studies University of Maryland,Theme,How to transform traditional KOS into systems for AI and semantic Web applications, thus Leveraging the large pool of knowledge available in existing KOS for lowering the cost
2、of developing knowledge-intensive applications,Presentations,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 ontolog
3、ies, and concept maps: knowledge tools in digital libraries. M. A. Gonalves, 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
4、 for organizing collections/services for given applications Terence Smith, UC Santa Barbara, Marcia Lei Zeng, Kent State Univ.; Alexandria Digital Library Project Web Services and Terminology. Adam Farquhar, SchlumbergerSema Update on Revision to the NISO Z39.19 Thesaurus Standard and Other Terminol
5、ogy Standards (Amy Warner, Lexonomy, Inc./consultant to NISO),Example 1,Consider Reading instruction isa Instruction Reading instruction has domain Reading Reading instruction governed by Learning standards Reading ability isa Ability Reading ability has domain Reading Reading ability supported by P
6、erception,Example 1, cont.,Can use the rules Rule 1 If X isa (type of) instruction and X has domain Z and Y isa ability and Y has domain Z Then X should consider YRule 2 If X should consider Y and Y is supported by W Then X should consider W,Example 1, continued,ERIC Thesaurus entries Reading instru
7、ction BT Instruction RT Reading RT Learning standards Reading ability BT Ability RT Reading RT Perception,Broader Term (BT) and Narrower Term (NT) relations in AGROVOC,BT and NT are typical hierarchical relations in a thesaurus. However, their semantics is not explicitly defined.It is common for BT/
8、NT relations within a thesauri to include at least the following: Is-A (e.g. Milk/ Cows Milk; Development Agency/IDRC) Ingredient of (e.g. Milk/ Milk Fat) Milk fat is an ingredient of milkProperty of (e.g. Maize/Sweet corn) Sweetness is a property of corn,Some examples from AGROVOCMAIZENT dent maize
9、NT flint maize NT popcorn NT soft maize NT sweet corn NT waxy maize MILKNT Milk Fat NT ColostrumNT Cows MilkDevelopment AgenciesNT development banks NT voluntary agencies NT IDRC,Related Term (RT) in AGROVOC,RT represents the associative relation. The RT usually involves the most ambiguous semantics
10、. RT can include the following.causality agency or instrument hierarchy - where polyhierarchy has not been allowed the missing hierarchical relationships are replaced by associative relationships sequence in time or space constituency characteristic feature object of an action, process or discipline
11、 location similarity (in cases where two near-synonyms have been included as descriptors) antonym,Some examples from AGROVOCDEGRADATIONRTchemical reactions RT discoloration RT hydrolysis RT shrinkage IDRC RT Canada,causality,location,Some ideas for reengineering AGROVOC,Most of the problems could be
12、 solved by: Re-analyzing the existing relations to introduce explicit semantics: for instance, BT/NT relationship could be resolved to Is-A relation RT relationship could be refined to more specific relationships (such as “produces”, “used by”, “made for”). Specifying composite concepts in terms of
13、basic concepts that can be un-ambiguously represented: 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 an attribute female”,Steps in converting a
14、 legacy KOS,Define the ontology structure Fill in values from one or more legacy KOS to the extent possible Edit manually using an ontology editor: make existing information more precise add new information,Intelligent conversion using “rules as you go”,If an editor has determined (or it is known fr
15、om another source, such as FDAs food vocabulary) that there is a relationshipanimal has-part milk it can be concluded that cow NT cows milk should becomecow has-part cows milk since cow is an animal and “cows milk“ contains the word “milk“. This clearly indicates that the reengineering effort should
16、 start with the topmost concepts.,Application in education,From the Smith and Zeng Paper,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 m
17、aterials in terms of concepts 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-pace
18、d materials Services for creating/editing/displaying such materials,Learning environment display (lecture mode),The lecture is presented on three projection screens, showing the Concept window (left) Lecture window (center) Object window (right),Semantic Network Services Sharing an integrated Ontolo
19、gy 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 En
20、vironmental Agency, Germany,6th NKOS Workshop May 31, 2003 Houston, TX, US,Integration in a Topic Map,Descriptor,Topic,Event,Location,Accident,Community,Nation,Nuclear Accident,Chernobyl radiation disaster 1986-04-26,Chernobyl,ex. USSR,situated in,broader,where,what,Nuclear Energy,occurrence findTo
21、pics,Mauerseglercontainsde/eventnames ,results in a list of matching topics,search term,search method,topic type path,fields to search,Conclusion,Papers on how to convert legacy KOS to systems with richer, precisely defined semantics (ontologies?) Papers on applications of such rich ontologies Shows a direction the field should move in,
