Entity Aware Click Graph
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The document discusses the analysis of query logs to measure website similarity through an entity-aware click graph, which enhances understanding of how websites relate based on shared entities and modifiers. It highlights challenges like query sparsity and semantic distortion while emphasizing the need for robust similarity graphs. The research utilizes data from Yahoo! and Freebase to evaluate website relationships effectively and suggests future improvements using knowledge bases and more complex queries.
![Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 4/16
Problems: Sparsity
● 44% of queries occur only once even when
considering a full year of data [1]
● using “shared queries” as relatedness
measure relatedness becomes tough in the
long tail.
[1] Baeza-Yates. Relating content through web usage. In HT ’09, 2009.](https://image.slidesharecdn.com/cikm2012-entityawarewebsitesimilarity-121101190915-phpapp01/85/Entity-Aware-Click-Graph-4-320.jpg)
![Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 6/16
Introduction
● >62% of queries contain entity name or type [20]
[20] Pound, Mika, & Zaragoza. Ad-hoc object retrieval in the web of data. In WWW’10, 2010.](https://image.slidesharecdn.com/cikm2012-entityawarewebsitesimilarity-121101190915-phpapp01/85/Entity-Aware-Click-Graph-6-320.jpg)
Entity Aware Click Graph
- 1. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 1/16 Measuring Website Similarity using an Entity-Aware Click Graph Pablo N. Mendes1, Peter Mika2, Hugo Zaragoza2, Roi Blanco2 1. Freie Universität Berlin 2. Yahoo! Research Barcelona Nov 1st 2012, Maui, CIKM 2012
- 2. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 2/16 Introduction: query log analysis ● Query logs record user interaction with Web search engines ● Query log analysis has been proven critical to improving search ● For search engines – Ranking, autosuggest, “Also try”, etc. ● For site owners – insight into user needs, allows optimizing Web presence, etc.
- 3. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 3/16 Introduction: website similarity ● Click graph: relating queries and websites, edges are clicks Click graph Site similarity graph (SG) ● Allows modeling website relatedness based on shared queries leading to each website pair
- 4. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 4/16 Problems: Sparsity ● 44% of queries occur only once even when considering a full year of data [1] ● using “shared queries” as relatedness measure relatedness becomes tough in the long tail. [1] Baeza-Yates. Relating content through web usage. In HT ’09, 2009.
- 5. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 5/16 Problems: partial overlaps ● Breaking up into words distorts semantics – “Forest” vs “Forest Gump” – “Pitt” vs “Brad Pitt”
- 6. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 6/16 Introduction ● >62% of queries contain entity name or type [20] [20] Pound, Mika, & Zaragoza. Ad-hoc object retrieval in the web of data. In WWW’10, 2010.
- 7. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 7/16 Entity-aware Click Graph ● Websites can share entities and/or modifiers
- 8. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 8/16 Entity-aware Website Similarity Graph ● More connected ● Preserves semantics ● Allows analysis of how websites relate to entities and modifiers
- 9. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 9/16 Experiments ● Website similarity – Find top K similar sites – Evaluation: two sites are “similar” if they are in the same category in ODP (Open Directory Project) ● Website characteristics from the searcher POV – What entities lead to a website – What context words lead to a website
- 10. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 10/16 Dataset Statistics: Query Log ● 1 month of queries from Yahoo!, 45M sessions ● 5M entities from Freebase
- 11. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 11/16 Results 1 ● Similarity edge prediction
- 12. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 12/16 Results 1 ● Similarity edge prediction with credit to partial category overlap
- 13. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 13/16 Results 2 Many entities Few modifiers Many entities Many modifiers Entropy of distribution of entities Few entities Many modifiers Entropy of distribution of modifiers
- 14. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 14/16 Results 2
- 15. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 15/16 Conclusion ● Recognizing entities in Web search logs allows for click graphs that account for internal composition of queries ● New similarity graphs built from entity-aware click graphs allow enable more robust and flexible similarity analysis (evaluated for website similarity) ● Future: – Exploit the knowledge base (e.g. type hierarchy) – More complex queries – etc
- 16. Mendes, Mika, Zaragoza, Blanco. Measuring Website Similarity using an Entity-Aware Click Graph 16/16 Thank you! ● Web: http://pablomendes.com ● E-mail: [email protected] ● Twitter: @pablomendes ● Slideshare: slideshare.net/pablomendes Questions?