Searching with context http://www2006.org/ Searching with context Presented by Ana Hilda Morales R Kraft, C.C. Chang, F. Maghoul, and R. Kumar. Proceedings of the Fifteenth International World Wide Web Conference NY, 2006. ACM Press 1/18/2019

Outline Introduction Algorithms Methodology Conclusions Query Rewriting Rank-Biasing Iterative, Filtering Meta Search Methodology Conclusions 1/18/2019

Introduction Contextual search refers to proactively capturing the information need of a user by automatically augmenting the user query with information extracted from the search context. 1/18/2019

Introduction Contextual search User interface Y!Q [Kraft et al, 2005] Extracting and representing the context Presentation layer User interface Front-end Logic layer Data layer Back-end 1/18/2019

Terminology Context: a piece of text (e.g., a few words, a sentence, a paragraph, an article) that has been authored by someone. Query: regular keyword search queries. Context term vector: subset of the words/phrases/entities in the content of the context. Contextual Search Query: a search query that comprises a keyword query and context. Query-less: when the keyword query is empty 1/18/2019

Terminology Families of Queries Simple Query SQ: keywords based search queries. Complex Query CQ: keyword based search queries and typically consist of keywords or phrases plus ranking operators. Standard Search Engine (Std. SE): refers to web search engine. Modified Search Engine (Mod. SE): refers to a web search engine that has been modified to support complex search using rank-biasing operators. Contextual Search Engine (CSE): is an application front-end that supports contextual search queries. . 1/18/2019

Algorithms Query Rewriting (QR) Rank Biasing (RB) Iterative, Filtering Meta-Search (IFM) 1/18/2019

Query Rewriting (QR) Send one simple query per contextual search query to a standard search engine Query Generator 1/18/2019

Process QR Creating queries composed of all query and context term vector using AND semantics Input: Simple Query Result set Parameter: The number of terms taken from the terms vector 1/18/2019

Example QR QR1 q (a) QR2 q (a b) q (a, b, c, d, e, f) QR5 q (a b c d e) q (a, b, c, d, e, f) Result set 1/18/2019

Advantages and Disadvantages QR This approach is simplicity, especially since conjunctive semantics is supported in all major search engines. Not requires a modified search engine back-end The more terms are added in a conjunctive way, the more restricted the query is, and the less results it will likely return. The query and context term vector together may comprise more terms than the search engine supports for evaluation. 1/18/2019

Rank-Biasing (RB) Send one complex query per contextual search query to a modified search engine. Query Generator 1/18/2019

Generating and sending Process RB Generating and sending a complex query that contains ranking instructions to modified search engine back-end Input: Parameters Result set Parameters: Number of selection terms to use Number of rank operators Weight multiplier for each RANK operator 1/18/2019

Example RB <query> = <selection=cat> <optional=persian, 2.0> Weight Selection terms Rank operators <query> = <selection=peugeot306> <optional=sale, 5.0><optional=blue, 2.0> 1/18/2019

Advantages and Disadvantages RB List of the results ordered sensibly Requires a modified search engine back-end The need for a specialized search engine for back-end support 1/18/2019

Iterative Filtering Meta-search (IFM) Send multiple, simple queries per contextual search query to a standard search engine. The model is based on the concept of meta-search. Query Generation Ranking/Filtering 1/18/2019

Structure IFM Query Generator 1/18/2019

Process IFM Parameters: Number of terms taken from the terms vector Creating queries composed of all query and context term vector using AND semantics Result Input: Multiple Simple Query Re-Rank Filter Result set Creating queries composed of all query and context term vector using AND semantics Result Creating queries composed of all query and context term vector using AND semantics Result Parameters: Number of terms taken from the terms vector 1/18/2019

Example IFM Combinations q (a) q (a b) q (a b c) q (a,b,c,d) q (b) q (b c) q (a b d) q (c) q (c d) q (a b c d) q (d) IFM-SW1 q (a), q (b), q (c), q (d) IFM-SW2 q (a b), q (b c), q (c d) IFM-SW4 q (a b c d) q (a, b, c, d) Result set 1/18/2019

Advantages and Disadvantages IFM Not requires a modified search engine back-end Dependency of the standard search engine 1/18/2019

Methodology 24,566 judgments from 28 expert judges Judgments arose out of a benchmark of 200 contexts. Is this result relevant to the context? “Yes” “Somewhat” “No” “Can’t tell” 1/18/2019

Conclusions QR, a simple technique that closely emulates human query reformulation and can be easily implemented on top of a commodity search engine, performs surprisingly well and is likely to be superior to manual reformulation. RB and IFM break the recall limitations of QR IFM is very effective and outperforms the others both in terms of recall and relevance at an added engineering cost. These three techniques offer a good design spectrum for contextual search implementors. 1/18/2019

References R Kraft, C.C. Chang, F. Maghoul, and R. Kumar. Searching with Context. In Proceedings of the Fifteenth International World Wide Web Conference (WWW-06), New York, NY, 2006. ACM Press. R. Kraft, C. C. Chang, and F. Maghoul. Y!Q: Contextual search at the point of inspiration. In Proceedings of the 14th Conference on Information and Knowledge Management (CIKM), pages 816–823, 2005. 1/18/2019

Thank you for your attention ? 1/18/2019