Augmented Hyperbooks through Conceptual Integration G. Falquet L. Nerima J.-C. Ziswiler Information System Interfaces – University of Geneva cui.unige.ch/isi

FNZ - Augmented Hyperbooks 2 Context: Hyperbook model domain ontology information fragments interface = generated hyperdocuments interface specification Virtual document model

FNZ - Augmented Hyperbooks 3 F O F O F O F O HT HT+ Context: Library of Hyperbooks Augmented reading in a digital library Enrich one hyperbook with contents coming from others

FNZ - Augmented Hyperbooks 4 Augmentation through Ontology Alignment 1. Align the book ontologies –compute concept similarities F O similarity links s ij

FNZ - Augmented Hyperbooks 5 Augmentation through Ontology Alignment 2. Use similarity links to infer hypertext links F O F O HT HT+ similarity link

FNZ - Augmented Hyperbooks 6 Problem Ontology alignment algorithms need well structured ontologies BUT –Hyperbook writers are not knowledge engineers –HT’04 reviewer "... nice approach … doubts about feasibility … " –C. Marshall, F. Shipman. Which semantic web ? "The difficulty of knowledge acquisition, representation and reasonsing has a long history of being underestimated …"

FNZ - Augmented Hyperbooks 7 This study Alignment method that works with less formalized (incomplete) ontology Test on two "realistic" hyperbooks Sources –Multifunctional agriculture site –World Trade Organization (WTO)

FNZ - Augmented Hyperbooks 8 The Hyperbooks "1-hour" ontology page extracts from WTO site WTO partial ontology "domestic support" Multifunctional agriculture web site

FNZ - Augmented Hyperbooks 9 Alignment technique Adapted from [Rodriguez & Egenhofer] concept comparison method Word Matching –compare the words in the terms "agricultural training service" "agricultural landscape" –stop word filtering –synonym resolution Fragment matching (feature matching) –compare the fragments connected to both concepts

FNZ - Augmented Hyperbooks 10 Compare the sets of words | A  B | | A  B | +  | A – B | + (1 -  ) | B – A |  depends on the relative depths of a and b in the concept hierarchy min { depth(a), depth(b) } depth(a) + depth(b) Similarity measure

FNZ - Augmented Hyperbooks 11 Sample similarity values Word matching alone is not sufficient –same words used for different concepts

FNZ - Augmented Hyperbooks 12 Similarity link creation Create link if FM > FMt and WM > WMt For the test hyperbooks : WMt = FMt = 0 FM WM SIM(a, {b 1, …, b n }) WMt FMt

FNZ - Augmented Hyperbooks 13 Reading the Augmented Hyperbook Generate interface documents and links –original contents (fragments) –inferred links to relevant concepts & contents –inferred transclusion and expand-in-place (stretch text)

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FNZ - Augmented Hyperbooks 17 Conclusion Alignment technique for "light" ontologies –augment hyperbooks written by non knowledge engineers –read hyperbooks "in a context" Further experiments (Oct. 2005) –e-learning: integrate/augment hyperbooks written by students –collection of hyperized scientific papers (in physics) Perspectives –characterization of other alignment algorithms / ontologies –hyperbook interface design: usability testing