1. Trank: Ranking Entity Types Using the Web of Data
Alberto Tonon1
Michele Catasta,
Gianluca Demartini
Philippe Cudré-Mauroux
Karl Aberer

2. Motivation Entity-centric Type is import More than one type
The most relevant types

3. Task Definition Task: entity type ranking Context types:
Entity e, document d, types Te={t1,…tn}
relevance to textual context ce from d
<rdfs:type>, <owl:sameAs>
Context types:
Three paragraphs
One paragraph
Sentence
Entity itself

4. Approaches

5. Approaches Entity-Centric Context-Aware Hierarchy-Based
FREQ,WIKILINK,LABEL
Context-Aware
SAMETYPE,PATH
Hierarchy-Based
DEPTH,ANCESTORS,ANC_DEPTH

6. Experiments NYT,Feb 21- Mar 7 2013 A ground truth: crowdsource
128 articles: each 12 entities, average 10.2 types
A ground truth: crowdsource
relevant
Relevance score

7. Experiments Evaluation Measures: 4 datasets MAP
NDCG Cumulated gain-based evaluation of IR techniques
4 datasets
770 distinct entities
Sentence : 419, average 32 words, 2.45 entities
Paragraph: 339, average 66 words, 2,72 entities
3-Paragraph: 339, average 165 words, 11.8 entities

8. Results

9. Results
TRank
MapReduce 71TB
8 servers, 12 cores 2.33GB, 32GB

10. Conclusions Type hierarchy Regression modle Interaction among entities
User impact

11. Thanks!