Handling Dynamicity and Temporality of Web Data

1. Handling Dynamicity and Temporality of
Web Data
Hady Elsahar
hadyelsahar@gmail.com
Jean Monnet University
Saint-Étienne, France

2. First try with Question Answering
Weet it : Natural language interface for Linked Data (ElSahar et al. ‘11 )

3. ● Most of the current knowledge bases focus on static facts and ignore
the temporal dimension of facts.
● Aspects of temporality and Dynamicity of Datasets :
○ Aspect 1 : Many facts are valid only during a particular time period.
○ Aspect 2 : New extracted facts can contradict with, verify or modify new ones
○ Aspect 3 : Some Facts are collectively induced from a series of Events
Handling Dynamicity of Data

4. Challenges and Motivations (1) :
Stephen Hawking
Many facts are valid only during a particular time
period.
Use Case : Questions about Temporal facts
● Who is first Wife of Stephen Hawiking ?
● Who is the 10th President of France ?
● Who is the past CEO of google ?

5. Extraction and Represenation of Temporal data
Extraction and representation of Temporal Facts and Events
❏ Representation :
❏ Keeping the last updated fact is not enough (DBpedia)
❏ Higher order fact (Erdal and Weikum ‘11)
❏ f1:Bill_Clinton isPresidentOf USA.
❏ f2:f1 startedOnDate 20-01-1993
❏ Wikidata Qualifiers (Vrandečić ‘12)
❏ Temporal fact and event extraction:
❏ Free Text and structured data from wikipedia (patterns and pattern induction)
(Erdal and Weikum ‘11)

6. Annotation of temporal facts in documents for Question answering
SemEval-2015 Task 5: QA TempEval

7. SemEval-2015 Task 5: QA TempEval
Question Examples in the Evaluation Dataset :
Yes / No:
● “Did the the Indonesian stock market rise again after it’s last fall ?
List:
● “What happened after the crash?”
● “What happened between the crash and yesterday?”
When (Factoid):
● “When did the Oscar ceremony end yesterday ?”
Applications ?

8. Challenges and Motivations (2) :
Stephen Hawking
In Highly dynamic datasets, new extracted facts
can contradict with, verify or modify new ones.
Existing facts New Extracted Fact
Matt Smith
is dbo:starring of
■ dbr:Womb_(film)
■ dbr:Lost_River_(film)
■ dbr:Bert_and_Dickie
■ dbr:The_Science_of_Doctor_Who
“Matt Smith is the doctor”
(Matt Smith, occupation, Medicine)
confidence : 0.1

9. (Frank Sinatra, profession, Singer) confidence : 0.9
(Jared leto, influenced_by, Frank Sinatra) confidence : 0.8
● People influenced by Writers are probably writers as well
● people are probably born at the same place of their siblings
Challenges and Motivations (2) :
Stephen Hawking
In Highly dynamic datasets, new extracted facts
can contradict with, verify or modify new ones.

10. Evaluation of new facts using Link prediction
Link Prediction
● Add new facts without extra knowledge
● Assess the validity of an unknown fact

11. Embedding Models for knowledge bases
TransE : Modeling Relations as Translations (Bordes et al. ’13):
● Modeling Facts as translations between vectors of entities
VSubject
+ VRelation
≅ VObject
● distance is used to Quantify confidence in facts
● Training objective: Find the representations that Minimizes distances across all true facts and
maximize across “corrupted” facts ( s’ , o’ ):

12. Other Embedding Models:
● Structured Embeddings (SE) (Bordes et al ‘11 )
● Collective Matrix Factorization (RESCAL) (Nickel et al., ’11)
● Neural Tensor Networks (socher et al. ‘13)
● TATEC (Garcia-Duran et al., ’14)
Embedding Models for Text + Knowledge bases:
● Joint Learning of Words and Meaning Representations (Bordes et al. ‘12)
● Knowledge Graph and Text Jointly Embedding (Wang et al ‘14)
Link prediction using Embedding Models

13. Applications ?
● Verification of new Extracted Facts
● Completeness of new added datasets
● Modeing literals dataypes (length, date ..etc ) not only relations and
entities.
Embedding Models other benefits ? (collaboration potential)
● Entity Disambiguation for Fact Extraction and QA (Bordes et al. ‘12)
● Paraphrase Detection for Questions, (PARALEX) (Fader et al. ‘13)

14. Challenges and Motivations (3) :
Reasoning with more than one supporting
facts ● Reasoning about positions (ex: Geo Data)
● Reasoning about Counts
● Reasoning about sizesFact 1 : 55 passengers crammed into the smuggler’s boat.
Fact 2 : The boat made it to the Greek island.
Question : Where are the passengers ?
Stephen Hawking
Facts induced from a series of Events
● Towards AI-Complete QA: A Set of Prerequisite Toy Tasks (Wetson et al ‘15)
● Memory Networks (Wetson et al ‘14)