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Dynamic Collective Entity Representations for Entity Ranking

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at the Ninth ACM International Conference on Web Search and Data Mining (WSDM 2016)

Published in: Science
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Dynamic Collective Entity Representations for Entity Ranking

  1. 1. Dynamic Collective Entity Representations for Entity Ranking David Graus, Manos Tsagkias, Wouter Weerkamp, Edgar Meij, Maarten de Rijke
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  4. 4. 4 Entity search?  Index = Knowledge Base (= Wikipedia)  Documents = Entities  “Real world entities” have a single representation (in KB)
  5. 5. 5 Representation is not static  People talk about entities all the time  Associations between words and entities change over time
  6. 6. 6 Example 1: News events
  7. 7. 7 Example 2: Social media chatter
  8. 8. 8 Dynamic Collective Entity Representations  Use “collective intelligence” to mine entity descriptions to enrich representation.  Is like document expansion (add terms found through explicit links)  Is not query expansion (terms found through predicted links)
  9. 9. 9 Advantages  Cheap: Change document in index, leverage tried & tested retrieval algorithms  Free “smoothing”: (e.g., tweets) may capture ‘newly evolving’ word associations (Ferguson shooting) and incorporate out-of-document terms  “move relevant documents closer to queries” (= close the gap between searcher vocabulary & docs in index)
  10. 10. 10 Haven’t we seen this before?  Anchors & queries in particular have been shown to improve retrieval [1]  Tweets have been shown to be similar to anchors [2]  Social tags, same [3]  But:  in batch (i.e., add data, see how it affects retrieval)  single source [1] T. Westerveld, W. Kraaij, and D. Hiemstra. Retrieving web pages using content, links, urls and anchors. TREC 2001 [2] G. Mishne and J. Lin. Twanchor text: A preliminary study of the value of tweets as anchor text. SIGIR ’12 [3] C.-J. Lee and W. B. Croft. Incorporating social anchors for ad hoc retrieval. OAIR ’13
  11. 11. 11 Description sourcesAnthropornis nordenskjoeldi Anthropornis Nordenskjoeld's Giant Penguin Eocene Oligocene Animal Chordate Aves Sphenisciformes Spheniscidae ... emperor penguin Nordenskjoeld's Giant Penguin Anthropornis nordenskjoeldi Nordenskjoeld's giant penguin Anthropornis Eocene birds Oligocene birds Extinct penguins Oligocene extinctions Bird genera KB Anchors KB Categories KB Redirects KB Links Anthropornis nordenskjoeldi Anthropornis nordenskjoeldi Web Anchors megafauna Tags Tweets biggest penguin anthropornis extinct penguin prehistoric birds Queries
  12. 12. 12 Challenge  Heterogeneity 1. Description sources 2. Entities  Dynamic nature  Content changes over time
  13. 13. 13 Method: Adaptive ranking  Supervised single-field weighting model  Features:  field similarity: retrieval score per field.  field “importance”: length, novel terms, etc.  entity “importance”: time since last update.  (Re-)learn optimal weights from clicks
  14. 14. 14 Experimental setup 1. Data:  MSN Query log (62,841 queries + clicks (on entities))  Each query is treated as a time unit  For each query:  Produce ranking  Observe click  Evaluate ranking (MAP/P@1)  Expand entities (w/ dynamic descriptions)  [re-train ranker]
  15. 15. 15 Main results  Comparing effectiveness of diff. description sources  Comparing adaptive vs. non-adaptive ranker performance
  16. 16. 16 Description sources MAP No. of queries
  17. 17. 17 Feature weights over time Relativefeatureimportance No. of queries
  18. 18. 18 Non-adaptive vs. adaptive ranking
  19. 19. 19 In summary  Expanding entity representations with different sources enables better matching of queries to entities  As new content comes in, it is beneficial to retrain the ranker  Informing ranker of “expansion state” further improves performance
  20. 20. 20 Thank you  (Also, thank you WSDM & SIGIR travel grants)

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