The document discusses an approach to entity disambiguation that combines textual and graph-based features. It presents an undirected factor graph model that captures dependencies between entity annotations. The model combines features like PageRank, term frequency, edit distance, and document similarity. It is trained on labeled datasets and evaluated against state-of-the-art systems using the GERBIL framework, achieving comparable results. The approach performs collective disambiguation of named entities.

1. Combining Textual and
Graph-based Features for Entity
Disambiguation
Sherzod Hakimov
Semantic Computing Group
CITEC, Bielefeld University
DBpedia Community Meeting 2016
Leipzig

2. is the capital of and the largest city inIstanbul Turkey EU
Problem Definition - Named Entity Disambiguation

3. is the capital of and the largest city inIstanbul Turkey EU
Problem Definition - Named Entity Disambiguation

4. is the capital of and the largest city inIstanbul Turkey EU
Problem Definition - Named Entity Disambiguation

5. is the capital of and the largest city inIstanbul Turkey EU
Problem Definition - Named Entity Disambiguation

6. Outline
● Candidate Retrieval

7. Outline
● Candidate Retrieval
● Undirected Factor Graphs
● Inference Strategy
● Combining different features
● Comparison with state-of-the-art

8. Outline
● Candidate Retrieval
● Undirected Factor Graphs
● Inference Strategy
● Combining different features
● Comparison with state-of-the-art
● NERFGUN - Named Entity disambiguation by Ranking with Factor Graphs
over Undirected edges
● Published* EKAW 2016, Bologna, Italy

9. Candidate Retrieval
● Lucene index of DBpedia & Wikipedia data with Frequency values
○ DBpedia label properties (rdfs:label, dbo:firstName, etc.)
○ Wikipedia anchors

10. is the capital of and the largest city inIstanbul Turkey EU
Ambiguity

11. Candidate Retrieval
● Lucene index of DBpedia & Wikipedia data with Frequency values
○ DBpedia label properties (rdfs:label, dbo:firstName, etc.)
○ Wikipedia anchors

12. Undirected Factor Graphs
Given a document with annotation spans

13. Undirected Factor Graphs
Given a document with annotation spans

14. Inference - Markov Chain Monte Carlo
● Initial document Creates samples of partial solutions

15. Inference - Markov Chain Monte Carlo
● Randomly initialized

16. Inference
● Creates samples of partial solutions

17. Inference
● Creates samples of partial solutions

18. Inference - Sampling Step 1
● Creates samples of partial solutions (aka Sampling)

19. Inference - Sampling Step 2
● Creates samples of partial solutions
Istanbul
Istanbul
Istanbul
Istanbul
Istanbul

20. Features

21. Features
● PageRank - compute for all DBpedia resources using random walk (Babelfy)

22. Features
● PageRank - compute for all DBpedia resources using random walk (Babelfy)
● Term Frequency - frequency values between surface form and URI

23. Features
● PageRank - compute for all DBpedia resources using random walk (Babelfy)
● Term Frequency - frequency values between surface form and URI
● Edit distance - Levenshtein distance between URI and surface form

24. Features
● PageRank - compute for all DBpedia resources using random walk (Babelfy)
● Term Frequency - frequency values between surface form and URI
● Edit distance - Levenshtein distance between URI and surface form
● Document Similarity - Text similarity of the given document and DBpedia
abstracts of each annotation

25. Features
● PageRank - compute for all DBpedia resources using random walk (Babelfy)
● Term Frequency - frequency values between surface form and URI
● Edit distance - Levenshtein distance between URI and surface form
● Document Similarity - Text similarity of the given document and DBpedia
abstracts of each annotation
● Topic Specific PageRank - compute for all DBpedia resources using random
walk (Babelfy)

26. Features
Edit distance
Term Frequency
PageRank
Topic Specific PageRank

27. Features
Edit distance
Term Frequency
Document Similarity
PageRank
Topic Specific PageRank
Istanbul dbo:abstract “Istanbul once known as Constantinople, is the most populous city in Turkey, ... “

28. Model Training
● SampleRank - learning weights for features
● Datasets : AIDA/CoNLL Training & MicroPost 2014 Training

29. Model Training - Local Evaluation

30. Model Training - Local Evaluation

31. Model Training - Local Evaluation

32. Comparison
● GERBIL - framework for benchmarking named entity disambiguation and
recognition, question answering
● State-of-the-art systems : AGDISTIS, AIDA, DBpedia Spotlight, TagMe,
Babelfy, etc.

34. Conclusion
● Collective disambiguation of named entities
● Model based on factor graphs to capture dependencies between annotations
● Impact of combining different features
● Comparable results to state-of-the-art

35. Conclusion
● Collective disambiguation of named entities
● Model based on factor graphs to capture dependencies between annotations
● Impact of combining different features
● Comparable results to state-of-the-art
Thanks