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

2 Agenda 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

3 Main Server User Info User Profiles User Agents … … Agents … … Matcher 1 Matcher n To other sites (network) Web Browser User e-Market e-business, e-learning … Buyer-Seller matching Metadata for buyers and sellers Keywords/keyphrases Trees Tree similarity Motivation

4 Partonomy Tree Similarity Algorithm ─ Tree Representation Tree representation 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: 2002 Car Ford Black Make Color Year

5  (s i (w i + w' i )/2)  (A(s i )(w i + w' i )/2) A(s i ) ≥ s i lom educational 0.5 general format platform 0.5 Introduction to Oracle t t´t´ technical edu-setgen-set tec-set language en title HTMLWinXP lom 0.1 general format platform Basic Oracle technical gen-set tec-set language en title *WinXP * : Don’t Care Partonomy similarity [Bhavsar et al. 2004] Fragments of learning object trees [Boley et al. 2005] for learning object matching ( Partonomy Tree Similarity Algorithm ─ Similarity Algorithm

6 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” Number of identical words Maximum length of the two strings Example 1: For two node labels “a b c” and “a b d e”, their similarity is: 2 4 = 0.5 Partonomy Tree Similarity Algorithm ─ Non-Semantic Matching

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

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

10 Distributed Programming Credit “Introduction to Distributed Programming” Textbook Tuition Duration $800 2months t1t1 t2t2 Object-Oriented Programming Credit “Objected-Oriented Programming Essentials” Textbook Tuition Duration $1000 3months partonomy trees Global similarity measure (for inner nodes) [Yang et al. 2005] Semantic Matching ─ Global Similarity

11 Programming Techniques Applicative Programming General Automatic Programming Concurrent Programming Sequential Programming Object-Oriented Programming Distributed Programming Parallel Programming The taxonomy tree of “Programming Techniques” according to the ACM Computing Classification System ( Semantic Matching ─ A Taxonomy Tree

12 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 Semantic Matching ─ Taxonomic Class Similarity

13 Taxonomic class similarity computation [Yang et al. 2005] where TS(c 1, c 2 ) is the taxonomic class similarity of classes c 1 and c 2 N s : the number of edges of the shortest path N t : 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 c 1 and c 2 (We assume G=0.5 here) Semantic Matching ─ Taxonomic Class Similarity

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

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

16 Programming Techniques Applicative Programming General Automatic Programming Concurrent Programming Sequential Programming Object-Oriented Programming Distributed Programming Parallel Programming ** * * * ** * 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” Background Taxonomy tree of “Programming Techniques” for encoding Semantic Matching ─ Encoding Subtaxonomies

17 Credit Title Tuition Duration $800 2months Classification 0.65 taxonomy Distributed Programming course Sequential Programming Parallel Programming * * * * Programming Techniques * Distributed Programming Concurrent Programming Credit Title Duration $1000 3months Classification 0.65 taxonomy Object-Oriented Programming course Sequential Programming * * Programming Techniques * Tuition Object-Oriented Programming Two course trees with encoded subtaxonomy trees Semantic Matching ─ Encoding Subtaxonomies 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 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 $800 and seller asks $1000 — Unsuccessful buyer asks $1000 and seller asks $800 — Successful The similarity of $800 and $1000 is different for the above cases Semantic Matching ─ Price Matching

20 Transform the asymmetry to symmetry Buyers and sellers always have price ranges in their minds [B pref, B max ] and [S min, S pref ] B pref : buyer’s preferred price B max : buyer’s maximum acceptable price S min : seller’s minimum acceptable price S pref : 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 Semantic Matching ─ Price Matching

21 PriceRangeSim ([B pref, B max ], [S min, S pref ]) Begin If S pref <= B pref similarity = 1.0 else if B max < S min similarity = 0.0 else if B max = S min similarity = else { MIN = min{MIN, S min } MAX = max{MAX, B max } 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 Pseudo code of the price-range similarity algorithm Semantic Matching ─ Price Matching Algorithm

22 “Date”-typed leaf node similarity measure { 1 – | d 1 – d 2 | if | d 1 – d 2 | ≥ 365 otherwise DS(d 1, d 2 ) = 0.5 end_date Nov 3, t1t1 t 2 start_date May 3, 2004 Project 0.5 end_date Feb 18, start_date Jan 20, 2004 Project 0.74 where DS(d 1, d 2 ) is the date similarity of two dates d 1 and d 2 Semantic Matching ─ Date Matching

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): [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): [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 treeseller tree 2002 Car FordWhite Make Color Year Car Ford Red Make Color Year 0.0 Similarity=1/2( )1.0 // for “Make” +1/2( )1.0 // for “Year” = 0.45

28 Seller Weights (Cont’d) Suppose sellers have identical weights buyer treeseller tree 2002 Car Ford White Make Color Year Car Ford Red Make Color Year

29 Seller Weights (Cont’d) Sellers have arbitrary weights buyer treeseller tree Car Ford White Make Color Year Car Ford Red Make Color Year Car Ford Red Make Color Year seller tree Car Ford Red Make Color Year seller tree 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): [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 [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 [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 [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 [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.