Similarity of two trees having extreme different weight Similarity of two trees having missing information Similarity of two trees with different complete information

Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005 ISSN 1858-1633 2005 ICTS 52 • if both v1 and v2 is not fuzzy adjective variables, the similarity is 1 if v1=v2 and 0 otherwise • if both v1 and v2 is fuzzyadjective variables, and both v1 and v2 having fuzzy values, the similarity is 1 if v1=v2 and 0 otherwise • if both v1 and v2 is fuzzy adjective variables, and v1 having fuzzy value, but v2 having crisp value, the similarity is the membership function value fx of v1 for x=v2 • if both v1 and v2 is fuzzy adjective variables, and v1 having crisp value, but v2 having fuzzy value, the similarity is the membership function value fx of v2 for x=v1 • if both v1 and v2 is fuzzyadjective variables, and both v1 and v2 having crisp values, the similarity is the defuzzification value of both membership function of v1 and v2 using min-max method

3. EXPERIMENT RESULT

First, the correctness of algorithm is compared to the result of case analysis as listed in the last research of algorithm [Bha et. al.-2003]. The output of the last research can be tested using server http:serv- 4100.dfki.uni-kl.de:8000~vegacgi-binrfi. From the case analysis, the improvement has better result for the case of: a. Similarity of two trees having different node labels Car Low Price 0.5 Year 0.5 2003 Car 15000000 Price 0.5 Year 0.5 2002 t1 t2 Figure 4. Two trees having different node labels The original algorithm result is 0. When 15000000 is a representation of price car in Indonesian Rupiahs, it is common that such price is cheap for any common car. The result of improved algorithm is near or equal 1, more realistic than original algorithm.

b. Similarity of two trees having extreme different weight

Car Low Price 0.0 Year 1.0 2003 Car 15000000 Price 1.0 Year 0.0 2002 t5 t6 Figure 5. Trees having extreme different weight The original algorithm result is 0.1, but the improved algorithm result is 0.55. Again with assumption of common cheap price in Indonesian Rupiahs, the improved algorithm giving more realistic similarity.

c. Similarity of two trees having missing information

Car Low Price 1.0 Make 0.05 TOYOTA Car 15000000 Price 0.9 Year 0.05 2002 t11 t12 Car Low Price 1.0 Make 0.45 TOYOTA Car 15000000 Price 0.1 Year 0.45 2002 t9 t10 Figure 6. Pairs of trees having missing information Table 1. Result Comparison of case c. Similarity Tree String matching Fuzzy Logic t9 t10 0.2823 0.7773 t11 t12 0.1203 0.9752 Again with assumption of common cheap price in Indonesian Rupiahs, the improved algorithm giving more realistic similarity. Fuzzy Logics Incorporated to Extended Weighted-Tree Similarity Algorithm for Agent Matching in Virtual Market – Sholeh Hadi Setyawan Riyanarto Sarno ISSN 1858-1633 2005 ICTS 53

d. Similarity of two trees with different complete information

Car Low Price 0.5 t15 Car Low Price 1.0 Make 0.333 TOYOTA Car 15000000 Price 0.334 Year 0.333 2002 t13 t14 1999 Year 0.5 Figure 7. trees with different complete information The result comparison is listed in Table 2. Table 2. Result Comparison of case d. Similarity Tree String matching Fuzzy Logic t13 t14 0.2349 0.8352 t15 t14 0.1674 0.5427 The algorithm improvement is applied to a prototype of car virtual market, having dataset of 1016 types of car from 53 car make around the world, taken from various car web site portals. The car specification is converted to OORuleML format and store in database, represent a seller agent advertisement. The buyer request is generated using a web form interface, allowing buyer to define the requirement using fuzzy values for adjective variables, and also allowing buyer to define all or part of the specification required.

4. CONCLUSION