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Entity Ranking Using Wikipedia as a Pivot

(CIKM 10’) Rianne Kaptein, Pavel Serdyukov, Arjen de Vries, Jaap Kamps

2010/12/14 Yu-wen,Hsu

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Outline

IntroductionFrom Wikipedia Entities to Web Entities

and backEntity Ranking on WikipediaEntity Ranking on WebConclusion

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Introduction

Entity ranking is the task of finding documents representing entities of a correct type that are relevant to a query.presenting a ranked list of entities directly,

rather than a list of web pages with relevant but also potentially redundant information about these entities.

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Differs from document retrieval on at least three points: i) returned documents have to represent an

entity ii) this entity should belong to a specified entity

typeiii) to create a diverse result list an entity should

only be returned once.

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Main Goal

To Rank Web entities1. Associate target entity types with the query2. Rank Wikipedia pages according to their

similarity with the query and target entity types3. Find web entities corresponding to the

Wikipedia entities

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Using Wikipedia as a pivotentities: Wikipedia pagesthe name of the entity: the title of the page the content of the page: the representation of

the entityEach Wikipedia page is assigned to a number

of categories: topical, type, and administrative categories.

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From Wikipedia Entities to Web Entities and back

From Web to Wikipediathese repositories provide enough clues to find

the corresponding entities on theWeb?they contain enough entities that cover the com

plete range of entities needed to satisfy all kinds of information needs?

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From Wikipedia to WebUse External Link

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Entity Ranking on Wikipedia* Entity Types

Entity Type Assignmentexploit the existing Wikipedia categorization of

documentsPseudo-relevance feedback of the top retrieved

documents we extract the categories that are most frequently

assignedthe top 10 results, and look at the 2 most frequently

occurring categories belonging to these documents

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*Entity Types -Scoring Entities

estimate background probabilities

smooth the probabilities of a term occurring in a category name with the background collection

: the name of the category : the category

: the query terms: the document: the entire Wikipedia document collection

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Similarity between two categories

The entity type score for a document in relation to a query topic

Score Normalization

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Entity Ranking on Wikipedia*Experimental Setup

Data Set:INEX: specific, ex countries, national parks.. TREC: people, organization, product

Advantage: clear, few options, could be easily selected

Disadvantage: cover a small part of all possible entity ranking queries

manually assigned more specific entity types

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rerank the top 2,500 results of the baselineManually assigned (author)Automatically assigned (PRF)

evaluation2009 TREC:P10 and NDCG@20INEX:P10 and MAP

INEX 2006-2008 consisting of 79 topics INEX 2009 topics consisting of a selection of 55 topics

from the 2006-2008 topics.

only count the so-called ‘primary’ pages

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Entity Ranking on The Web

We have three approaches for finding web pages associated with Wikipedia pages.1. External links:

the External links section of the Wikipedia page2. Anchor text:

Wikipedia page title as query retrieve pages from the anchor text index

3. Combined:not all Wikipedia pages have external linksnot all external links of Wikipedia pages are part of the

Clueweb collectionless than 3 webpages are found, we fill up the results to 3

pages using the top pages retrieved using anchor text

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Conclusion

Our experiments show that our wikipedia-as-a-pivot approach outperforms a baselines of full-text search.

Both external links on Wikipedia pages, and searching an anchor text index of the web are effective approaches to find homepages for entities represented by Wikipedia pages.