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Copyright (C) Shun Hattori Query Modification Based on Real-World Contexts for Mobile and Ubiquitous Computing Environments Shun Hattori, Taro Tezuka,

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Presentation on theme: "Copyright (C) Shun Hattori Query Modification Based on Real-World Contexts for Mobile and Ubiquitous Computing Environments Shun Hattori, Taro Tezuka,"— Presentation transcript:

1 Copyright (C) Shun Hattori Query Modification Based on Real-World Contexts for Mobile and Ubiquitous Computing Environments Shun Hattori, Taro Tezuka, Katsumi Tanaka Graduate School of Informatics Kyoto University, Japan MCISME 2006

2 Copyright (C) Shun Hattori2 Background The exponentially growing amount of information available on the Internet, increases the importance of IRS (Information Retrieval System) such as Web search engine. The improvement and maintenance of mobile and ubiquitous computing environments, allows us to access information anywhere at any time in our daily life. Mobile IRS will become very significant and more in the near future.

3 Problems IRS for fixed computing environments: A fixed user s query is generally too short and ambiguous to guess his/her information demand accurately. The retrieval result by user query D(q) has both acceptable documents which match his/her demand, noise documents which don t match his/her demand. Query Expansion by its related terms in order to refine the retrieval results based on e.g. context in the corpus of documents to be retrieved, his/her Information-world contexts such as text in the document being read or written. e.g., expanding harry potter by its related terms, magic, might filter only documents about harry potter. D(q) noise user Related terms

4 Copyright (C) Shun Hattori4 Advanced Problems IRS for mobile computing environments: A mobile user s query is shorter and more ambiguous, so its retrieval results would include its multiple subtopics. e.g., the retrieval result by harry potter has a mixture of documents about movie, book, video game, etc. but the user might want to get not results mixed with documents about any subtopic but documents about only one certain subtopic or documents exhaustively about all subtopics. How to filter documents about only one certain subtopic from the primitive retrieval results D(q) ? What term is added to the original user query ? D(q) noise / user or How?

5 Copyright (C) Shun Hattori5 Approaches (1) Utilize real-world contexts of mobile user: such as his/her current geographic location and the objects (things) surrounding him/her, in order to infer a user query s subtopic from among ones, aiming to enhance Context Awareness to the existing location-free IRS. (2) Keyword-based Query Modification: in order to consume less computational time for query modification. Relevance feedback or similarity-based retrieval does not suit mobile users. D(q) user or Contextual Related Term

6 Copyright (C) Shun Hattori6 Two Helps for Mobile Users Context-aware Query Expansion: When a user inputs a query in a situation, it is expanded by its related contextual words such as names of place or object, in order to restrict the retrieval results to its only one subtopic related with the situation. Context-aware Keyword Inference: When a user wants to input a keyword and already types its substring in a situation, it is guessed from among all candidate keywords which its substring matches the beginning of, based on his/her current real-world contexts such as geographic location or surrounding objects, in order to allow mobile users to compose his query easily.

7 Copyright (C) Shun Hattori7 Sensing Real-World Contexts Translating Contexts to C-Words DB GIS DB Ontology Assigning Weight to each C-Word Enforcing Query Modification Query ObjectsLocation Object-NamesPlace-Names Presentation System Overview (1/2)

8 Copyright (C) Shun Hattori8 System Overview (2/2) Step 1. Sensing Real-World Contexts: observes user s current geographic location by GPS and the surrounding objects by RFID. Step 2. Translating them to Contextual Words: converts real-world contexts to contextual words such as names of place or object, by using GIS and ontologies. Step 3. Assigning Weight to Contextual Word: assigns weight to each contextual word, based on the relevance between it and a user query. Step 4. Enforcing Query Modification: 1. expands a user query by its related contextual word 2. infers keyword with high weight as what keyword the user wants to input

9 (C) Shun Hattori9 Context Weighting We have been able to sense many various real-world contexts at a certain time, but all of them are not necessarily useful for query modification. We have to classify whether a context is useful or not, We assign weight to each context, based on the relevance R(c,q) between its contextual word c and a user query q by Web mining. R(c,q) = Pr(c|q) / Pr(c) {Pr(c|q) - Pr(c)} Local: Pr(c|q) = DF(c q) / DF(q) Global: Pr(c) = DF(c) / N D(q) D(c) N Comparison Proportion× Differential Local Global DF = Document Frequency D(cq)

10 Copyright (C) Shun Hattori10 abc comic shop store place-name p i in the Real world user u query: harry potter Expansion R pq (p i,q) query q harry potter harry potter AND bookstore bookstore Query Location 1. Example of Query Expansion

11 (C) Shun Hattori abc comic shop store place-name p i in the Real world user u keyword: harry potter substring: har Keyword Inference harley davidson R pq (p i,q k ) bookstore harriet tubman harvard candidate keyword q k harry potter Location 2. Example of Keyword Inference

12 Copyright (C) Shun Hattori12 Experimental Results Experiment 1: Query Expansion We justify that our system can improve the retrieval results by expanding an original query by its related contextual words such as names of place or object. Experiment 2: Keyword Inference We justify that our system can infer what keyword a mobile user is trying to input, based on the relevance between contextual word and each candidate keyword for its substring by Web Mining.

13 Copyright (C) Shun Hattori13 Experiment 1.1: Query Expansion Method: Calculate the relevance between various place-names and harry potter as a query. Expectation: harry potter has multiple subtopics such as book, movie, video game and so on. Therefore, we expected that harry potter has high relevance with bookstore, movie theater, and game store, when a mobile user inputs harry potter as a query in these each place, harry potter will be expanded by each place-name.

14 Copyright (C) Shun Hattori14 Results 1.1: Query Expansion Discussion: The same results as our expectation cannot be always obtained. For example, bookstore, video store zoo, park, school We will have to improve the method to calculate the relevance between contextual word and query.

15 Copyright (C) Shun Hattori15 Experiment 1.2: Query Expansion Method: Calculate the approximate precision in the top 20 documents retrieved by adding each name of place or object to harry potter as an original query. For example, Web pages about video game, card game or toy except free online mini game, are acceptable to game as a subtopic of harry potter.

16 16 Results 1.2: Query Expansion Discussion: Our system can improve to some extent. For example, adding bookstore to the original query improves from 0.50 to 0.75 as precision. Relevance 0.048 0.116 0.046 0.251 1.699 3.411 2.683 1.100

17 Copyright (C) Shun Hattori17 Experiment 2: Keyword Inference Method: Calculate the relevance between various names of place or object and each candidate keyword for har as a substring. 1. harry potter is expected to have highest relevance with bookstore, movie theater, book or movie. 2. harley davidson is expected to have highest relevance with automobile dealer or automobile. 3. harvard is expected to have highest relevance with school, university, textbook.

18 Copyright (C) Shun Hattori18 Results 2: Keyword Inference Discussion: The same results as our expectation cannot be always obtained, e.g. bookstore. However, our system can infer to some extent in the other cases, e.g. automobile dealer. harry … harley...harvard bookstore0.0480.0270.051 movie theater0.1160.4650.000 automobile dealer0.0230.3980.000 School0.1660.3581.326 ??? harley … harley … harvard In each place, Keyword inferred is

19 Copyright (C) Shun Hattori19 Conclusion and Future Work Conclusion: proposed query modification based on real-world contexts such as geographic location and surrounding objects tried to justify it by some experimental results, Keyword Inference is good in many cases. but Query Expansion is not good in several cases. Future Work: Carry about bigger experiments, Improve the method to calculate the relevance between contextual word and user query, e.g. by Blog mining or Query Log Analysis Utilize not only user s current contexts but also the history of continuous real-world contexts.

20 Thank you very much for your attention. Any question or comment ? Please send it to hattori @ dl.kuis.kyoto-u.ac.jp


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