This document outlines a presentation on web query expansion based on association rules mining with eHownet and a Google Chrome extension. It introduces the background and purpose, which is to improve misuse of Chinese synonyms in queries by providing suggested keywords. The related works section discusses eHownet and the Apriori algorithm. The system architecture and experimental results are also outlined.

1. Web query expansion based on
association rules mining with
eHownet and Google chrome
extension
楊曜年 Paul Yang

2. Outline
 Introduction
◦ Background
◦ Purpose
 Related works
◦ eHownet
◦ Aprori algorithm
◦ Relevant research
 Aprori-based query expansion for Chinese IR
◦ System arch
◦ Chinese Word Segmentation / Feature selection
◦ Query Expansion by eHownet
◦ Aprori-based noise word filter
 Experimental results
 Conclusions and future works

3. Introduction

4. Background
According to the marketing survey report (中文搜索引擎
存在问题, 2009, 北京正望咨询; 搜索引擎市場調查報告專題, 新浪網)
 In Google and Baidu, 17% and 24% of user can’t find
the web-pages they want
 58.6% of user just checks the first few pages and skips
the later pages
 50% of user has a little or no knowledge background
for topic they’re going to query

5. Background - cont
Why Search Engines Fail to Search ?
Relevant Documents?
• Users do not give sufficient number of
keywords
Ex. T1 (query)  T1 + T2 + T3 (expect to see)
• Users do not give good keywords
◦ Vocabulary gaps
◦ Lack of domain knowledge
Ex. T1 + T2 (query)  T3 + T4 (expect to see)

6. Background - cont
Users, particularly, children, may be suffered with
the problem in Google because of misusing Chinese
synonyms as search query to cause a decrease in
precision
Ex
Wanna find”深夜食堂” but misuse ” 深夜酒家” as query

7. Background - cont
the search engine like Google that claims it has
many text mining techs !

8. Background - cont
The actual test on Google (10 results per page, total 4 pages )
Ex 1
• User wants to find “隱形飛機 ”but uses “私密飛行器”as query, the results
shows its precision rate only reaches 5% (2/40)
• If we evaluate based on whether user can get the result in the first page, the
precision rate reaches 0% (2/40)
Ex 2
• find”深夜食堂” but use ” 深夜酒家” as query, the results shows its precision
rate only reaches 4%
• Get NONE of the related results in first page 0% ( 0/10)

9. Background - cont
Solutions to improve this problem:
 Global methods
◦ Query expansion/reformulation
 Thesauri (ex. WordNet)
 Automatic thesaurus generation
 Local methods
◦ Relevance feedback
◦ Pseudo relevance feedback

10. Background - cont
Based on the our observation, compared with English
WorldNet (109,000 synonym sets), Chinese
WorldNet provides insufficient info for query
expansion.

11. Background - cont
Another major problem if we use the Thesauri like
WordNet for query expansion
“too many noise words which cannot be found in search engine”
Ex. “私密飛行器” as an input for query expansion
After expansion :
(a1 a2 a3, + b1, b2,……,b5)
秘密 鬼祟 暗地 隱蔽 隱形 私密
噴氣機 座機 飛行器 飛機
隱秘

12. Background - cont
Google Chrome Extension

13. Purpose
The idea of this paper is to:
Improve misuse of Chinese synonym by giving user our
suggested keywords based on Google
browser extension using CKIP’s eHownet for query
expansion and data mining algorithm “Aprori” to analyze
the retrieved web-pages to get the association rules for
filtering the noise word to improve the overall precision.

14. Related works

15. 廣義知網知識本體（Extended-
HowNet Ontology）
 E-HowNet is an entity-relation model for
lexical semantic representation extended
from HowNet.
 {clothing|衣物} [衣衫]
 – 鞋子|shoes [木屐, 木鞋,球鞋, 溜冰鞋, 靴子]
 – 褲子|trousers [褲子, 運動褲]
 – 內衣|underwear [內衣]
 – 禮服|ceremonial robe/dress [禮服, 白紗,婚
 紗]

16. The Apriori Algorithm
An influential algorithm for mining frequent itemsets for boolean
association rules.
Key Concepts :
• Frequent Itemsets: The sets of item which has minimum support
(denoted by Li for ith-Itemset).
• Apriori Property: Any subset of frequent itemset must be frequent.
• Join Operation: To find Lk , a set of candidate k-itemsets is
generated by joining Lk-1 with itself.
State University of New York, Stony
Brook 16

17. Web Query Expansion by WordNet
Zhiguo Gong, Chan Wa Cheang, and Leong Hou U 2005
 Aim for a Web Image queries
 Web query expansion by using WordNet and
TSN to extend the scope of the original query
 TSN works as Keyword Filtering based on
Aprori
Relevant researches

18. Relevant researches – cont

19. Aprori-based query expansion for
Chinese IR

20. Architecture

21. Interface

22. Interface - cont

23. Interface – cont

24. Interface – cont

25. Interface – cont

26. Query expansion through ehownet
May use query of “秘密噴氣機”for “隱形飛機”
• After Chinese word segment: 秘密(VH) 噴氣機(Na )
• Query eHowNet by “秘密” and “噴氣機”

27. Feature selection
• Use Jieba (结巴中文分词) with 繁體 dictionary rather
than CKIP due to performance and prototype
integration concern
• Extract only Verb, noun and ADJ of POS tag from
every sentence
深夜/a 食堂/n 维基百科/nz 自由/a 的/uj 百科全书/nz
深夜/a 食堂/n 深夜/a 食堂/n 漫画/n 深夜/t 食堂/n 在线漫画/n 动漫/n 之家/r 漫画
网/n
EX.

28. Feature selection - cont
• Use TF/IDF to pick the words with high
values, which is able to filter some “綴詞”
ex. 年、月、日、你、我、他、段

29. Association Rules from webpage using
Aprori
隱形 戰機 维基百科
自由 百科全书 维基
百科 飛機
Webpage A
美 部署 隱形 飛機
對抗 中國
Webpage B
隱形 飛機 是 降低
飛機 電 光 聲 可 探
測 特徵 使 雷達 探
測器
Webpage C
隱形 飛機
Curial info (Rules)

30. Noise word filter
Before the filtering process :
After the filtering process (min_support: 0.1 & mid_confidence:
0.75) :
秘密 鬼祟 暗地 隱蔽 隱形 私密
噴氣機 座機 飛行器 飛機
隱秘
隱形飛機 秘密飛機

31. Experiment Result

32. Experiment Setup
 Simulate that user may use imprecise query
 Base on 4 topics (隱形飛機, 飢餓遊戲, 威力
彩, 深夜食堂) to download the webpage from
Google (24 pages per possible query, totally 1320
pages) and manually review all pages to count the
precision against 4 concepts
 Use solely precision measure, no recall and F-
measure to estimate the performance.

33. Experiment Setup – cont
使用者預查詢的主題 深夜食堂 飢餓遊戲 威力彩 隱形飛機
使用者可能誤用的查詢字
深夜酒家 飢餓玩樂 神力色彩 隱形飛行器
深夜飯館 飢餓遊憩 神力顏色 隱形座機
深夜飲食店 飢餓嬉遊 神力彩 隱秘飛機
夜深食堂 飢餓嬉鬧 威力彩色 隱秘飛行器
夜深酒家 挨餓遊戲 威力色彩 隱秘座機
夜深飯館 挨餓玩樂 威力顏色 暗地飛機
夜深飲食店 挨餓遊憩 神力彩色 暗地飛行器
半夜食堂 挨餓嬉遊 暗地座機
半夜酒家 挨餓嬉鬧 私密飛機
半夜飯館 口燥遊戲 私密飛行器
半夜飲食店 口燥玩樂 私密座機
深更食堂 口燥遊憩 秘密飛機
深更酒家 口燥嬉遊 秘密飛行器
深更飯館 口燥嬉鬧 秘密座機
深更飲食店 焦渴遊戲 鬼祟飛機
焦渴玩樂 鬼祟飛行器
焦渴遊憩 鬼祟座機
焦渴嬉遊
焦渴嬉鬧

34. Experiment Setup - cont
The scenarios to validate:
 Users have pre-knowledge to concept and
just uses imprecise word as query.
 Users have a few pre-knowledge but can’t
determine query by our suggesting terms

35. Experiment result – First
Hit rate: 100% (隱形飛機 , 飢餓遊戲 威力彩
深夜食堂) , the correct terms are included
after query expand .
可能誤用的查詢字 神力彩 挨餓遊憩 暗地座機 深更酒家
Aprori 過濾後的建議結
果
威力彩, 威
力顏色, 神
力色彩,
神力彩色,
威力彩色
飢餓玩樂,
挨餓遊
戲,挨餓嬉
鬧,
挨餓遊憩
飢餓遊
戲,飢餓嬉
遊
隱形飛機,
秘密飛機
夜深酒家,
深夜食堂,
深夜飲食店,
夜深食堂

36. Experiment result – Second
0%
10%
20%
30%
40%
50%
60%
隱形飛機 深夜食堂 飢餓遊戲 威力彩
Before
After
Average precision

37. Conclusion and future work

38. Summary
 For user having a few knowledge, the query expansion can let user have
more option to choose and modify its imprecise query.
 Query expansion with online dictionary ehownet + noise filter improves
the average precision around 20%
 Improve the keyword set containing many topics and concepts
 Use the validated dataset of data-mining with the full search engine
function to validate based on precision/recall measure
 Improve the case that user may misuse spoken word (口語字) by other
dictionary
 Improve the mining performance by other algorithms like FP-growth

39. Backup

40. Problem observations
深夜食堂:
關聯主題:
戲劇 漫畫 小說 安倍夜郎
FOOD eHownet同義字組合:
深夜 深更 更深 夜深 半夜 半夜三更 三
更半夜
食堂 飲食店 飯館 酒家 啤酒屋
8x 5 = 40
The possible query Google Search result
occurs in first page
(within 10 links) hit rate % 中文斷詞
深夜食堂 40/40 1 100%
深夜(Nd) 食堂
(Nc)
夜深食堂 40/40 1 100%
半夜食堂 34/40 1 85%
三更半夜食堂 28/40 1 70%
深夜飯館 26/40 1 65%
深更食堂 18/40 found in 10 1 45%
深夜啤酒屋 10/40 1 25%
更深食堂 10/40 1 25%
深夜酒家 2/40 0 5%
深夜飲食店 1/40 0 2%
半夜飯館 1/40 0 2.00%
深更飲食店 0/40 0 0.00%
深更啤酒屋 0/40 0 0.00%

41. 更深酒家 0/40 0 0.00%
夜深酒家 0/40 0 0.00%
夜深啤酒屋 0/40 0 0.00%
三更半夜飲食店 0/40 0 0%
半夜三更酒家 0/40 0 0%
半夜飲食店 0/40 0 0%
AVG 42% 28%
The query over 60% hit (high
covergae) 05:19 26.30%
EXCEPTION
半夜居酒屋漫畫 6/40 15.00%
Key index in the highest weight
食堂 5/40 Y 13%
深夜 16/40 Y 40%
Problem observations - cont

42. 隱形飛機
關聯主題: 隐形軍事科技, 隱形UFO, 无人驾驶,
ehownet 同義字
組合:
隱秘 隱蔽 秘密 鬼祟 暗地 私密 鬼鬼祟祟
航空器 座機 噴氣機 航空器
飛行器
7 X 5 = 35
可能query word
Google Search
result (All)
Google Search
result 軍事
occurs in first
page (within 10
links) hit rate % (All)
hit rate %
(specific) 中文斷詞
隱形飛機 40/40 40/40 1 100% 100%隱形(VH) 飛機(Na)
隱形飛行器 39/40 39/40 1 97.50% 97.50%隱形(VH) 飛行器(Na)
隱形噴氣機 35/40 35/40 1 87.50% 87.50%隱形(VH) 噴氣機(Na)
隱形噴氣機 34/40 34/40 1 85% 85%隱形(VH) 噴氣機(Na)
隱形座機 32/40 32/40 1 80% 80%隱形(VH) 座機(Na)
隱秘飛機
20/40 (重複一樣的
文章:17/20)
20/40 (重複一樣的
文章:17/20) 1 50.00% 50.00%隱秘(VH) 飛機(Na)
隱秘噴氣機
19/40 (重複一樣的
文章: 16/19)
19/40 (重複一樣的
文章: 16/19) 0 48% 48%秘密(VH) 噴氣機(Na)
暗地飛行器 7/40 1/40 0 17.50% 2.00%暗地(D) 飛行器(Na)
隱蔽飛機 6/40 4/40 0 15.00% 10.00%隱蔽(VH) 飛機(Na)
私密飛行器 5/40 2/40 0 12.50% 5.00%私密(VH) 飛行器(Na)
秘密飛機 6/40, 无人驾驶, 9 2/40 0 7.50% 5.00%秘密(VH) 飛機(Na)
暗地噴氣機 3/40 2/40 1 7.50% 5.00%暗地(D) 噴氣機(Na)
鬼祟飛機 0/40 0/40 0 0.00% 0.00%鬼祟(VH) 飛機(Na)
Problem observations - cont

43. Problem observations - cont
私密飛機 0/40 0/40 0 0.00% 0.00%私密(VH) 飛機(Na)
鬼鬼祟祟飛機 0/40 0/40 0 0.00% 0.00%
鬼鬼祟祟(VH) 飛機
(Na)
隱蔽座機 0/40 0/40 0 0.00% 0.00%隱蔽(VH) 座機(Na)
鬼祟噴氣機 0/40 0/40 0 0.00% 0.00%鬼祟(VH) 噴氣機(Na)
Avg 41% 36% 34%
The query over 60% hit
(high covergae) 05:17 29.00% 29.00%
EXCEPTION
看不見的飛機 3/40 7.50%
看(VC) 不見(VH) 的
(DE) 飛機(Na)
Key index in the highest
weight
隱形 1/20 Y 5%
飛機 0/40 0%

44. 0 0.2 0.4 0.6 0.8 1 1.2
深夜食堂
夜深食堂
半夜食堂
三更半夜食堂
深夜飯館
深更食堂
深夜啤酒屋
更深食堂
深夜酒家
深夜飲食店
半夜飯館
深更飲食店
深更啤酒屋
更深酒家
夜深酒家
夜深啤酒屋
三更半夜飲食店
半夜三更酒家
半夜飲食店
topic coverage %
Occurs in first page Yes/No
Problem observations - cont