1. QA Systems in QALD Hybrid Task
Qingxia Liu
2016/02/29

2. an Overview of the Hybrid Task
QALD-4 task3
25 training, 10 test, no system participate
Which anti-apartheid activist was born in Mvezo?
SELECT DISTINCT ?uri
WHERE {
?uri rdf:type text:"anti-apartheid activist" .
?uri dbo:birthPlace res:Mvezo . }
Which writers had influenced the philosopher that refused a Nobel Prize?
?x rdf:type dbo:Philosopher .
?x text:"refuse" text:"Nobel Prize" .
?x dbo:influencedBy ?uri .
?uri rdf:type dbo:Writer .

3. an Overview of the Hybrid Task
QALD-5 task2
40 training, 10 test, 5 systems

4. ISOFT
Authors
Seonyeong Park, Soonchoul Kwon, Byungsoo Kim and Gary Geunbae Lee
Pohang University of Science and Technology, South Korea
Idea
semantic answer type detection
search on multi-tagged text database
decompose question by contract right-most phrases
朴善英，浦项工科大学
SAT: train
3-level type ontology: 71.42%
2: 84.62%

5. if no good answer, generate SPARQL comparative -> prop + compare
SAT classification:
features
kw, literal AT, (not use NE)
libSVM training
tags:
coreference and disambiguation
NL tags(POS, dependency, SRL)
for each subquery
search on sentences
all NEs as candidates
if no good answer, generate SPARQL
comparative -> prop + compare
predicate mapping by vector
ESA lib
基于疑问词对问题进行分类

6. ISOFT Who is the architect of the tallest building in Japan?
Are there man-made lakes in Australia that are deeper than 100 meters?
In which city were Charlie Chaplin's half brothers born?

7. HAWK: ranking techniques
Authors
Ricardo Usbeck, Axel-Cyrille Ngonga Ngomo
University of Leipzig, Germany
Axel-Cyrille Ngonga Ngomo
among the organizers in QALD-3,4,5,6
Main Idea
Generate hybrid queries by BFS search on dependency tree
each node to a triple-pattern (at least one variable)
Rank queries by training
3 ranking methods

8. Which anti-apartheid activist was born in Mvezo?
Training:
Optimal Ranking
(determine bad parts)
Feature-based Ranking
(rank queries)
Overlap-based Ranking
(rank answers)
Which anti-apartheid activist was born in Mvezo?
remove meaningless nodes in the tree
by POS-tags
auxiliary tokens, e.g. did
link classes, properties
fuzzy string search
use lexicon (lemon.dbpedia)
born (
anti-apartheid activist,
dbr:Mvezo)
“born”:
dbo:birthPlace,
dbo:birthDate
1. SELECT ?proj {?proj text:query ’anti-apartheid activist’.
?proj dbo:birthPlace dbr:Mvezo.}
2. SELECT ?proj {?proj text:query ’anti-apartheid activist’.
?proj dbo:birthDate dbr:Mvezo.g}
3. SELECT ?proj {?proj text:query ’anti-apartheid activist’.
?const dbo:birthPlace ?proj.}
BFS search on pruned tree:
possible triple patterns
according to node tag
Pruning by features:
unbound triple pattern
e.g. (?s, ?p, ?o)
unconnected
cyclic
no projection
violating disjointness
ESWC 2015
反种族隔离的，曼德拉
FUSEKI: 支持特定uri上文本信息的检索
feature-based ranking: 节点数、triple数、type数

10. HAWK Optimal Ranking > overlap-based , feature-based Errors:
不考虑yago： 26/40, 8/10, train on QALD-4
F-measure
Errors:
failing entity annotation
e.g. Jane_T._Austion, G8, Los_Alamos
missing type information in the gold standard resource

11. YodaQA: search-based methods
Authors
Petr Baudiš and Jan Šedivy
Czech Technical University, Czech Republic
NLP, ML, speech recognition
Main Idea
A modular QA system pipeline
Search –based methods
generate answers by passage analysis
Tricks
Goal: factoid single-answer questions
List-questions: top15
Boolean questions：always return true
Extract focus: 6 simple hand-crafted heuristics
原本是基于非结构化数据, 捷克
篇章（这里即句子）

12. Who wrote Ender’s Game? Title Text Search: title matching
(logistic regression)
(get related sentences)
(type coerion)
(class, prop mapping)
Focus: who, SV: wrote, LAT: person
question Cluse: Ender’s Game, wrote
title: 对实体排序，取句子；
full-text：先对实体排序并取top6，再对各句子打分，取各文档内top3；
doc：直接取实体；
lexical answer type (LAT)
type coerion: wordNet hypernymy relation
Who wrote Ender’s Game?
Title Text Search: title matching
(first sentences in top 6 docs)
Full-text Search: title + article sentences
(top 3 passages in each doc in top 6 docs)
Document Search: search in wiki doc
(top 20 docs)

13. Errors:
missing type information in the gold standard resource

14. Error Cases id
ISOFT
HAWK 1
Where was the "Father of Singapore" born?
(unable to generate SPARQL query) 2
Which Secretary of State was significantly involved in the United States' dominance of the Caribbean?
(wrong answer) 3
Who is the architect of the tallest building in Japan? √ 4
What is the name of the Viennese newspaper founded by the creator of the croissant?
(predicate URI not found) 5
In which city were Charlie Chaplin's half brothers born?
partial 6
Which German mathematicians were members of the von Braun rocket group? 7
Which writers converted to Islam? 8
Are there man-made lakes in Australia that are deeper than 100 meters? 9
Which movie by the Coen brothers stars John Turturro in the role of a New York City playwright? 10
Which of the volcanoes that erupted in 1550 is still active?
(cannot generate appropriate query)
property
How many scientists graduated from an Ivy League university?
SELECT DISTINCT count (? uri)
WHERE {
?uri rdf: type dbo: Scientist .
?uri dbo: almaMater ? university .
? university dbo: affiliation dbr:Ivy_League . }
Which animals are critically endangered?
SELECT DISTINCT ? uri
WHERE {
?uri rdf: type dbo: Animal .
?uri dbo: conservationStatus 'CR ' . }

15. Summary Components ISOFT HAWK YodaQA basic NLP deep NLP
dependency tree
keywords, focus, LAT
deep NLP
co-reference
textual solution
phrase-based search
text:query
clue-based search
structural solution
SPARQL template trigged by words
dependency tree node to triple
class, property mapping
question decomposition
concatenating the two rightmost phrases and find answer
errors
missing type info in gold standard resource
failing entity annotation

16. Thank you ~