Query Phrase Expansion using Wikipedia for Patent Class Search

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1 Query Phrase Expansion using Wikipedia for Patent Class Search 1 Bashar Al-Shboul, Sung-Hyon Myaeng Korea Advanced Institute of Science and Technology (KAIST) December 19 th, 2011 AIRS 11, Dubai, UAE

2 OUTLINE 2 Introduction Motivations Problem Statement Issues in Related Works Research Goals Proposed Method Experimental Results & Key Findings Conclusions

3 INTERNATIONAL PATENT CLASSIFICATION (IPC) 3 A04A Sub-Class (SC) A04A 17 Main Group (MG) A04A 17/01 Sub-Group (SG)

4 Patent Class Search 4 Patent Class Search can be defined as finding the relevant IPCs to the query trying to perform one of the patent search tasks (i.e. survey state-of-theart, check claims validity, etc.), and/or trying to classify the query document over the IPC hierarchy.

5 What? Query Expansion Modifying terms of the baseline query, and generating a new query 5 Why? Disambiguate the query context, Increase query coverage, and Overcome vocabulary mismatch between the query and search collection.

6 Issues in QE 6 Causes of an inappropriate QE: 1. Number of expansion terms 2. Term selection method (#doc (i.e. PRF), distribution, statistics, etc) Selection Bias Context-Free Selection Consequences: Topic Drift: when the user s information need is changed after the QE process.

7 Motivations 7 Considering context using phrases have been under research for a while in QE. Problems exist in extracting/selecting appropriate expansion phrases Different statistical distributions compared to words So far, no significant effectiveness have been shown

8 Problem 8 Selecting appropriate QE terms is a challenging issue especially considering factors such as context, and phrase distribution over documents. While phrases can be considered a type of contextual information, using phrases is still challenging considering the aforementioned reasons. Our focus is proposing a method to consider query context through phrases, from which it can select expansion terms that will enhance the effectiveness.

9 Relevance Model (RM) 9, log, 1 New Query Search BaseQry Index Search Retrieved Set Expansion Terms Selection #weight( w 1 Baseline_Query w 2 Expansion_Terms )

10 Related Works & Related Issues Using WordNet for query expansion limitations: (Bhogal et. al., 2007) WordNet is incomplete Limited to words Context-Free 10 Using Wikipedia for query expansion has several problems: (Gabriliovich et. al., 2006)(Arguello et. al., 2008) Using the whole page for search is wrong (multiple sections with different topics) Using in/out links can lead to retrieval bias towards pages with higher number of links (i.e. PageRank) Using different types of phrases in QE, or even IR in general, didn t report significant improvement over using words alone. (Arampatzis et. al., 1997)(Papka & Allan, 1998)(Koster & Seutter, 2003)

11 Research Goals Study the effect of phrases on queries 11 Study the effect of Word-based QE ( i.e. using PRF, WordNet ) Explore the effect of utilizing Wikipedia for query phrase expansion Analyze the interaction between WordNet-based expanded words and Wikipedia-based expanded phrases, Finding an optimal weight ratio between Words & Phrases for our proposed QE method The roles of each QE method in generalizing/specifying the query topic

12 Proposed Method 12 Phrase-based QE Query Patent Phrase Query Extraction Query Wikipedia Search Word-based QE Combined Query Phrase-based QE Cat 2 nd Cat Titles P(Cat) P(2 nd Cat) P(Titles) X Evaluation Patent Search Wiki. Ret. Docs Top n Ranked Wiki Pages Expansion Phrase

13 Wikipedia Wikipedia is: a collective, open-source knowledge-base several millions of pages, tens of languages structured contents, heavily cross-linked constantly updated information, with up to date topics well written documents with minor amounts of noise no clear hierarchical structure 13 Normally large scale hierarchies tend to be unbalanced as some branches tend to be disproportionally small or large. (i.e. ODP) Possibility of several existing relations rather than only generalization relation in hierarchies.

14 Category Example from Wikipedia 14

15 Category Secondary Categories Category Pages

16 Wiki-Pages vs. Wiki-Categories 16 Page Title: IR Category Title: IR In-Links: AI, CS, DBMS, Google, NLP, Information Theory, Supervised Learning, Secondary Categories: In-Links: Digital Libraries, Searching, Indexing,.. Out-Links: Information Science Out-Links: Bayes Theory, Classification, Co- Occurrences, Data Mining, Document Retrieval Category Pages: IR, Search Engine, QA, Stemming, LSA, Stopwords, Topic-based Vector Space Model,..

17 Generating the Combined Query 17 Q,, w: word, p: phrase, r: a WN relation R: all WN relations considered for word expansion d: # of documents considered for phrase expansion n: # of words in baseline query, m: # of phrases in baseline query

18 Wikipedia-Based QE 18 Phrase-based QE Documents are scored for each phrase as: p(ph D) = λ Cat p(ph ) + λ Sec.Cat p(ph ϴ. ) + λ Titles p(ph ϴ ) Phrase Wikipedia Search where: and:. = 1 p(ph ϴ i ) = c (ph,θ i), where: ϴ i Cat 2 nd Cat Titles P(Cat) P(2 nd Cat) P(Titles) X Wiki. Ret. Docs, %.. _ j: is document rank in the retrieved set d: # of documents considered for phrase expansion Top n Ranked Wiki Pages Expansion Phrase

19 Environment Wikipedia USPTO Number of Documents Average Document Length Number of Terms Number of Unique Terms Size on Disk 26.6 GB 52.1 GB Wikipedia Dump of Aug. 17 th,2010 NTCIR-6 USPTO patents of years patents were selected to generate queries Selected query patents were mapped to their corresponding IPCv9 (crawled from USPTO website) to generate the relevance judgment 19

20 IPC-Based P/R Evaluation 20 Relevant_Retrieved_IPCs q All_Retrieved_IPCs q Recall Distinct_Relevant_Retrieved_IPCs All_Relevant_IPCs Relevant_Retrieved_IPCs All_Retrieved_IPCs Distinct_Relevant_Retrieved_IPCs All_Relevant_IPCs (i.e. # of docs with relevant IPCs) (i.e. From the top ranked 1000 documents) (i.e. # of distinct IPCs correctly retrieved) (i.e. # of IPCs of the query document) As we conducted our experiments on a pre-known granted set of patents randomly selected from USPTO collection, we already have the relevant IPCs of each of the patents, from which we generated the relevance judgment.

21 Results 60 Mean Average Precision Recall SG MG SC SG MG SC

22 Results 22 SC MG SG RM OM RM OM RM OM β β β β β β β β β

23 Key Findings Patent Titles and Abstract where the best sections for generating a query. (After testing the 14 combinations) RM Our Method SC MG SG SC MG SG MAP Recall MAP Recall MAP Recall MAP Recall MAP Recall MAP Recall T A TA C TAC TAS APP

24 Key Findings Best Weight balance between the baseline and expansion parts of the expanded query was found based on the following experiment. RM SC MG SG Baseline Expansion MAP Recall MAP Recall MAP Recall

25 Key Findings Word to Phrase weight ratio in the query was determined after the following experiment. OM SC MG SG KW Weight KP Weight MAP Recall MAP Recall MAP Recall

26 Key Findings Trying to understand the effect of WordNet expansion, and the effectiveness of expansion words in the retrieved set of documents, it was found that for 87% of expanded queries, WN Words exist in IRRELEVANT documents more than relevant ones. OM Better (1128) Rel > IRRel Relevant Irrelevant (%) 3.11% 4.07% 13.56% RM Better (652) Rel > IRRel Relevant Irrelevant (%) 3.20% 3.92% 14.87% i.e. 3.11% is the average ratio between RM words count and document length in unique words

27 Key Findings Studying Terms Weights Queries where OM has better MAP (1128) OM Query RM Query Original Words Weight % (including WN) Phrase Weight % RM Words Weight % Average Word Count Average Phrase Count Average Word to Phrase Ratio Queries where RM has better MAP (652) OM Query RM Query Original Words Weight % (including WN) Phrase Weight % RM Words Weight % Average Word Count Average Phrase Count Average Word to Phrase Ratio

28 Key Findings Phrases exist in average more frequently in relevant documents (76%) OM Better Rel > IRRel Relevant Irrelevant (%) % RM Better Rel > IRRel Relevant Irrelevant (%) %

29 Why Wikipedia-based Query Phrase Expansion Might FAIL? 29 Wikipedia titling policy which requires that category titles are topic descriptors specific enough to be distinguished from each other but general enough to cover more specific concepts in each page. Wikipedia categories can be unreliable sometimes because it is possible to arrive at complete different sets of categories from similar pages (i.e. Wikipedia pages precision and recall and accuracy and precision ).

30 Conclusions Conclusions & Future Works 30 We have proposed a new method of using Wikipedia categories and WordNet at the same time for query word and phrase expansion in the task of patent class search. Our analysis of the experimental results reveals that added phrases control topic drift caused by PRF. Our future work includes: Incorporating Wiki-Links to our proposed expansion method, An IPC re-ranking model is being researched, Devising a more accurate method for keywords expansion by using a more accurate synset recommender method, so that we can pinpoint where the expanded phrases play an essential role.

31 Thank You 31 QUESTIONS?

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