Location-aware applications: keyword clustering

Keyword suggestion (query relaxation) •To suggest keywords –No need to type whole word, less typing errors, … •Problem –Input: keywords, iterations, location, … –Distance: approximate and semantic similarity –Prototype and Representation: keyword(s) •Solution: Using words semantic tree •Updating clusters for new words •Clustering of photos’ descriptions in photo keyword search 2

Approximate String Matching •Edit distance •Q-grams •Cosine distance –Input: (string1, string2) –Output: a value

Compared StringsEdit Distance (%) Q_Grams Q =2 (%) Q_Grams Q =3 (%) Q_Grams Q =4 (%) Cosin Distance (%) Pizza Express Café Pizza Express 72%78.79%74.29%70.27%81.65% Lounasravintola Pinja Ky – Ravintoloita Lounasravintola Pinja 54%67.74%67.19 %65.15%63.25% Kioski Piirakkapaja Kioski Marttakahvio 47%45.00%33.33%31.82%50.00% Kauppa Kulta Keidas Kauppa Kulta Nalle 68%66.67%63.41 %60.47%66.67% Ravintola Beer Stop Pub Baari, Beer Stop R-kylä 39%41.67%36%30.77%50.00% Ravintola Beer Stop Pub Baari, Wanha Mestari R-kylä 19%7.69%0%0.00% Ravintola Foxie s Bar Siirry hakukenttään Baari, Foxie Karsikko 31%25.00%15.15%11.76%23.57% Play baari Ravintola Bar Play – Ravintoloita 21%31.11%17.02%8.16%31.62%

Semantic: WordNet •It’s a large lexical database of English. Synsets are interlinked by means of conceptual-semantic and lexical relations. •Freely and publicly available for download. •API available.

Wordnet •A lexical database for the English language. •List of words grouped according to meaning •Hierarchical organization

Wu & Palmer  Calculates relatedness by considering the depths of the two words in the WordNet taxonomies, along with the depth of the LCS (Lowest Common Subsumers).  The formula is: score = 2*depth(lCS) / (depth(s1) + depth(s2)).

Example Example: semantic relatedness between bird and fish HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 bird#n#1 (depth=11) HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 aquatic_vertebrate#n#1 fish#n#1 (depth=12) LCS: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 (depth=10) •Relatedness = 2*depth(lCS) / (depth(s1) + depth(s2)) =2*10/(11+12)=0.8695

Clustering on words