Lu Yang, Biplab Sarker, Virendrakumar C. Bhavsar and Harold Boley bhavsar@unb

1. Range Similarity Measures between Buyers and Sellers in e-Marketplaces Lu Yang, Biplab Sarker, Virendrakumar C. Bhavsar and Harold Boley bhavsar@unb.ca Faculty of Computer Science University of New Brunswick (UNB) Fredericton, Canada IICAI, December 20, 2005

2. Motivation • Partonomy Tree Similarity Algorithm • Tree representation • Partonomy similarity • Non-semantic matching on nodes • Semantic Matching • Inner nodes vs. leaf nodes • Global similarity measure (for inner nodes) • Taxonomic class similarity • Encoding subtaxonomies into partonomy trees • Local similarity measures (for leaf nodes) • Conclusion Agenda

3. Motivation Main Server Agents User Info Web Browser User Profiles … User … User Agents … To other sites (network) e-Market … Matcher1 Matchern • e-business, e-learning … • Buyer-Seller matching • Metadata for buyers and sellers • Keywords/keyphrases • Trees • Tree similarity

4. Car Year Color 0.5 0.3 Make 0.2 Ford 2002 Black Partonomy Tree Similarity Algorithm─ Tree Representation • Treerepresentation for product/service descriptions[Bhavsar et al. 2004] • Characteristics of our trees • Node-labled, arc-labled and arc-weighted • Sibling arcs are labled in lexicographical order • Sibling arc weights sum to 1.0 A simple example “Car” tree:

5. t´ lom t lom technical general educational general technical 0.7 0.3 0.3334 0.3333 0.3333 tec-set gen-set tec-set edu-set gen-set format format platform platform language title language title 0.9 0.5 0.1 0.5 0.8 0.2 0.5 0.5 en en Basic Oracle * WinXP Introductionto Oracle HTML WinXP * : Don’t Care A(si) ≥ si (A(si)(wi + w'i)/2) (si (wi + w'i)/2) Partonomy Tree Similarity Algorithm─ Similarity Algorithm • Partonomy similarity[Bhavsar et al. 2004] Fragments of learning object trees [Boley et al. 2005] for learning object matching (http://www.cs.unb.ca/agentmatcher)

6. Number of identical words Maximum length of the two strings 2 = 0.5 4 Partonomy Tree Similarity Algorithm─ Non-Semantic Matching • Non-semantic matching on both inner and leaf nodes • Exact string matching • binary result 0.0 or 1.0 • Permutation of strings • “Java Programming” vs “Programming in Java” Example 1: For two node labels “a b c” and “a b d e”, their similarity is:

7. 1 = 0.5 2 Partonomy Tree Similarity Algorithm─ Non-Semantic Matching Example 2: Node labels “electric chair” and “committee chair” meaningful? • Semantic matching techniques are needed for the above problems

8. Semantic Matching • Inner nodes vs. leaf nodes • Inner nodes — class-oriented • Inner node labels can be classes • Classes are located in a taxonomy tree • Taxonomic class similarity measure (global similarity measure) • Leaf nodes — type-oriented • Address, currency, date, price and so on • Type similarity measures (local similarity measures)

9. Semantic Matching Non-Semantic Matching String Permutation(both inner and leaf nodes) Semantic Matching (Cont'd) Taxonomic Class Similarity(inner nodes) Exact String Matching(both inner and leaf nodes) Type Similarity(leaf nodes)

10. Semantic Matching ─ Global Similarity • Global similarity measure (for inner nodes)[Yang et al. 2005] Distributed Programming Object-Oriented Programming Tuition Tuition Credit Credit 0.4 0.2 0.2 0.1 Duration Duration Textbook Textbook 0.1 0.5 0.3 0.2 $1000 $800 2months 3months 3 3 “Introduction to Distributed Programming” “Objected-Oriented Programming Essentials” t1 t2 partonomy trees

11. Programming Techniques 0.7 0.6 Object-Oriented Programming 0.9 0.8 0.5 0.5 General Concurrent Programming Sequential Programming Applicative Programming Automatic Programming 0.5 0.7 Parallel Programming Distributed Programming Semantic Matching ─ A Taxonomy Tree • The taxonomy tree of “Programming Techniques” according to the ACM Computing Classification System(http://www.acm.org/class/1998/ccs98.txt)

12. Semantic Matching ─ Taxonomic Class Similarity • The arc weights can be determined by human experts or machine learning algorithms[Singh 2005] • Sibling arc weights do not need to add up to 1 • Three factors that affect the taxonomic class similarity • The shortest path length between two classes • Arc weights on the shortest path • Level difference of two classes

13. Semantic Matching ─ Taxonomic Class Similarity • Taxonomic class similarity computation[Yang et al. 2005] where TS(c1, c2) is the taxonomic class similarity of classesc1andc2 Ns: the number of edges of the shortest path Nt: the number of edges of the whole tree M: the product of the arc weights on the shortest path : the level difference factor where G’s value is in (0.0, 1.0) and is the absolute difference of the depths of classes c1andc2 (We assume G=0.5 here)

14. Semantic Matching ─ Taxonomic Class Similarity Example Programming Techniques 0.7 0.6 Object-Oriented Programming 0.9 0.8 0.5 0.5 General Concurrent Programming Sequential Programming Applicative Programming Automatic Programming 0.7 0.5 Parallel Programming Distributed Programming • red arrows stop at their nearest common ancestor

15. Semantic Matching ─ Encoding Subtaxonomies • Encoding subtaxonomy trees into partonomy trees • A converse taskComputes the similarity of pairs of taxonomies e.g. subtaxonomies of the background taxonomy, as required in our Teclantic project (http://teclantic.cs.unb.ca) • Allows the direct reuse of our partonomy similarity algorithm and permits weighted (or ‘fuzzy’) taxonomic subsumption with no added effort

16. Programming Techniques Applicative Programming Concurrent Programming Object-Oriented Programming Automatic Programming Sequential Programming 0.1 0.1 0.15 General 0.15 0.3 0.2 * * * * * * Distributed Programming Parallel Programming 0.4 0.6 * * Semantic Matching ─ Encoding Subtaxonomies Background Taxonomy tree of “Programming Techniques” for encoding • Sibling arc weights must sum up to 1.0 • Classes are represented as arc labels (lexicographical ordered) • All node labels except the root node label are changed into “Don’t Care”

17. Semantic Matching ─ Encoding Subtaxonomies Two course trees with encoded subtaxonomy trees course course Classification Tuition Classification Tuition Duration Title 0.65 0.05 Duration 0.65 Title 0.05 Credit Credit 0.15 0.1 0.05 0.05 taxonomy $800 taxonomy $1000 0.05 0.2 2months Distributed Programming 3 3months Object-Oriented Programming 3 Programming Techniques Programming Techniques 1.0 1.0 * * Sequential Programming Concurrent Programming Object-OrientedProgramming Sequential Programming 0.3 0.7 0.2 0.8 * * * * Parallel Programming Distributed Programming 0.4 0.6 * * • Weight assignment in the "Classification" branch (two options) • By human expert • By machine learning • Normalizes corresponding weights in the background taxonomy

18. Semantic Matching ─ Local Similarity • Local similarity measures (for leaf nodes) Special-purpose similarity measures for various data types realizing semantics to be invoked when computing similarity of any two of their instances • “Price” type • “Date” type [Yang et al. 2005] • . . .

19. Semantic Matching ─ Price Matching • Price • Price is the omnipresent factor that determines buyers’ and sellers’ decision-making • Price similarity seems to be asymmetric for buyers and sellers e.g. buyer asks $800and seller asks $1000 — Unsuccessful buyer asks $1000 and seller asks $800 — Successful The similarity of $800 and $1000 is different for the above cases

20. Semantic Matching ─ Price Matching • Transform the asymmetry to symmetry • Buyers and sellers always have price ranges in their minds[Bpref, Bmax] and [Smin, Spref]Bpref : buyer’s preferred priceBmax : buyer’s maximum acceptable priceSmin : seller’s minimum acceptable priceSpref : seller’s preferred price • Our price-range similarity measure is based on the intuition that the greater the overlap between the buyer’s and seller’s price ranges, the higher is their similarity value

21. Semantic Matching ─ Price Matching Algorithm • Pseudo code of the price-range similarity algorithm PriceRangeSim([Bpref, Bmax], [Smin, Spref]) Begin If Spref <= Bpref similarity = 1.0 else if Bmax < Smin similarity = 0.0 else if Bmax = Smin similarity = else { MIN = min{MIN, Smin} MAX = max{MAX, Bmax} similarity = } return similarity End. • This algorithm can be easily adapted to the “price”-typed attributes e.g. “salary range” in job seeking and recruiting e-Market

22. { if | d1 – d2 | ≥ 365 0.0 DS(d1, d2) = | d1–d2 | – otherwise 1 365 Semantic Matching ─ Date Matching • “Date”-typed leaf node similarity measure where DS(d1, d2) is the date similarity of two dates d1 and d2 Project Project 0.74 start_date start_date end_date end_date 0.5 0.5 0.5 0.5 Nov 3, 2004 May 3, 2004 Jan 20, 2004 Feb 18, 2005 t 2 t1

23. Conclusion • Weighted trees for product/service descriptions • Partonomy tree similarity algorithm • Synchronously traverses trees top-down • Aggregates intermediate similarity values bottom-up • Semantic Global and Local Matching • Taxonomic Class Similarity • Encoding Subtaxonomies into Partonomies • Leaf-Node Type Similarity Measures • Future Work • Improvement of Taxonomic Class Similarity • Generalization of Local Similarity Measures

24. References [1] Yang, L.,Ball, M., Bhavsar, V.C., and Boley, H. Weighted Partonomy-Taxonomy Trees with Local Similarity Measures for Semantic Buyer-Seller Match-Making, Journal of Business and Technology (to appear).[2] Boley, H., Bhavsar, V.C., Hirtle, D., Singh, A., Sun, Z., and Yang, L. A Match-Making System for Learners and Learning Objects. International Journal of Interactive Technology and Smart Education, August, 2005, 2(3):171-178.[3] Bhavsar, V.C., Boley, H., and Yang, L. A Weighted-Tree Similarity Algorithm for Multi-Agent Systems in e-Business Environments. Computational Intelligence, 2004, 20(4):584-602.[4] Singh, A., LOMGenIE: A Weighted Tree Metadata Extraction Tool, Master Thesis, Faculty of Computer Science, University of New Brunswick, Fredericton, Canada, September 2005.

25. Thank you !

26. Seller Weights • Advertisements on TV, Internet, and in newspaper • Sellers always emphasize specific product/service attributes to attract buyers • Our match-making system is buyer-seller-centric • Sellers also seek buyers having close preferences

27. Seller Weights (Cont’d) • Suppose sellers do not have weights buyer tree seller tree Car Car Year Year Color Color 0.8 0.0 0.1 0.0 Make Make 0.0 0.1 2002 2002 Ford Red White Ford Similarity=1/2(0.1+0.0)1.0 // for“Make” +1/2(0.8+0.0)1.0 // for“Year”= 0.45

28. Seller Weights (Cont’d) • Suppose sellers have identical weights buyer tree seller tree 0.7834 Car Car Year Year Color Color 0.8 0.3334 0.1 0.3333 Make Make 0.1 0.3333 Ford Ford White 2002 Red 2002

29. Seller Weights (Cont’d) • Sellers have arbitrary weights 0.925 buyer tree seller tree 1 Car Car Color Color Year Year 0.8 0.9 0.1 0.05 Make Make 0.1 0.05 Ford Ford White 2002 Red 2002 0.85 0.65 seller tree 3 seller tree 2 Car Car Year Year Color Color 0.3 0.6 0.6 0.2 Make Make 0.1 0.2 Red 2002 Red 2002 Ford Ford • All the seller trees above are identical except the arc weights • The buyer prefers to negotiate with seller 1 because they have closer preferences on the car attributes

30. Seller Weights (Cont’d) • Sellers can always select the averaged weights if they do not want to emphasize any attributes of their products/services • Using seller weights, both buyers and sellers can find the most promising trading partners • The negotiation space is decreased

31. Publications [1] Lu Yang, Marcel Ball, Virendrakumar C. Bhavsar, and Harold Boley, "Weighted Partonomy-Taxonomy Trees with Local Similarity Measures for Semantic Buyer-Seller Match-Making", Journal of Business and Technology (to appear).[2] Harold Boley, Virendrakumar C. Bhavsar, David Hirtle, Anurag Singh, Zhongwei Sun, and Lu Yang, "A Match-Making System for Learners and Learning Objects", International Journal of Interactive Technology and Smart Education, August, 2005, 2(3):171-178. [3] Jing Jin, Biplab K. Sarker, Virendrakumar C. Bhavsar, Harold Boley, and Lu Yang, "Towards a Weighted-Tree Similarity Algorithm for RNA Secondary Structure Comparison", In Proceedings of the 8th International Conference on High Performance Computing in Asia Pacific Region, IEEE Computer Society, December 2005. [4] Lu Yang, Marcel Ball, Virendrakumar C. Bhavsar, and Harold Boley, "Weighted Partonomy-Taxonomy Trees with Local Similarity Measures for Semantic Buyer-Seller Match-Making", In Proceedings of Workshop of Business Agents and the Semantic Web (BASeWEB'05), May 8, 2005, Victoria, British Columbia, Canada.[5] Lu Yang, Biplab K. Sarker, Virendrakumar C. Bhavsar, and Harold Boley, "A Weighted-Tree Simplicity Algorithm for Similarity Matching of Partial Product Descriptions", In Proceedings of ISCA 14th International Conference on Intelligent and Adaptive Systems and Software Engineering, Toronto 2005, pp.55-60.[6] Virendrakumar C. Bhavsar, Harold Boley, and Lu Yang, "A Weighted-Tree Similarity Algorithm for Multi-Agent Systems in e-Business Environments", Computational Intelligence, 2004, 20(4), pp.584-602.[7] Riyanarto Sarno, Lu Yang, Virendrakumar C. Bhavsar, and Harold Boley, "The AgentMatcher Architecture Applied to Power Grid Transactions", In Proceedings of the First International Workshop on Knowledge Grid and Grid Intelligence, Halifax, 2003, pp.92-99.[8] Virendrakumar C. Bhavsar, Harold Boley, and Lu Yang, "A Weighted-Tree Similarity Algorithm for Multi-Agent Systems in e-Business Environments", In Proceedings of 2003 Business Agents and the Semantic Web (BASeWEB'03) Workshop, Halifax, Canada, June 14, 2003.