The document summarizes research on automatically cleaning and refining relationships in AGROVOC, an agricultural ontology. Key points: 1. Researchers developed a system that uses multiple techniques like expert rules, learning from examples, noun phrase analysis and WordNet alignment to detect incorrectly defined relationships and suggest refinements. 2. An evaluation showed the system was able to correctly refine 72-100% of relationships tested, depending on the technique used. 3. Future work includes collaboratively training the system to automatically extract more relationship rules from data.

1. Automatic Term Relationship Cleaning and Refinement for AGROVOC * Asanee Kawtrakul, Aurawan Imsombut , Aree Thunkijjanukit, ** Dagobert Soergel, *** Anita Liang, Margherita Sini, Gudrun Johannsen, and Johannes Keizer * Department of Computer Engineering, Kasetsart University, Bangkok, Thailand ** College of Library and Information Services, University of Maryland, College Park *** FAO, Library & Documentation Systems Division, Rome, Italy 6th AOS Workshop - EFITA/WCCA2005 Portugal , 26 Jul 2005

5. System Overview Examples Rules Define Explicit Rules Using Training statistics-based Rules Verification Learning WordNet Alignment Noun Phrase Analysis AGROVOC Annotation NP Rules Detection and Suggestion Module Rules Acquisition Module Verification Module Using Expert-Defined Rules Rules WordNet

8. Learning-from-Examples Rules madeFrom / usedToMake Ex. Mutton< madeFrom >Sheep / Sheep< usedToMake >Mutton usedToMake / madeFrom Ex. Rice <usedToMake> Rice flour / Rice flour <madeFrom> Rice memberOf / hasMember Ex. FAO <memberOf> UN / UN <hasMember> FAO containsSubstance / SubstanceContainedIn Ex. Milk < containsSubstance > Milk fat / Milk fat < substanceContainedIn > Milk componentO f / hasComponent Ex. Engine part <componentOf> Engine / Engine <hasComponent> Engine part 896 RT 4826 4828 NT 4826 4954 RT 25784 8069 RT 2791 25500 RT 6599 7030 RT 5015 Concept 2 Relation Concept 1 Engine Parts 25784 Engines 4954 Mlik Fat 4828 Milk 4826 FAO 2791 Rice flour 25500 Rice 6599 UN 8069 Sheep 7030 Mutton 5015 Concept Type Data Concept Concept Code

11. Detection and Suggestion Module Examples Rules Define Explicit Rules Using Training statistics-based Rules Verification Learning WordNet Alignment Noun Phrase Analysis AGROVOC Annotation NP Rules Detection and Suggestion Module Rules Acquisition Module Verification Module Using Expert-Defined Rules Rules WordNet

15. Detection and Suggestion Module Rules Using Training statistics-based Rules WordNet Alignment Noun Phrase Analysis AGROVOC NP Rules Using Expert-Defined Rules Rules WordNet USE/UF AGROVOC Using Training statistics-based Rules WordNet Alignment Using Training statistics-based Rules BT/NT RT

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