1. Template-based Question Answering over RDF Data
Christina Unger , Lorenz Bühmann , Jens Lehmann
Axel-Cyrille Ngonga Ngomo ,Daniel Gerber , Philipp Cimiano
Yanan Zhang

2. background
Intuitive ways of accessing RDF data become more and more important.
Question answering approaches have been proposed as a good compromise between intuitiveness and expressivity.
general way: a triple-based representation 
e.g. Who wrote The Neverending Story? (PowerAqua):
<[person,organization], wrote, Neverending Story>.
<Writer, IS_A, Person>
<Writer, author, The Neverending Story>

3. 1. (a) Which cities have more than three universities?
(b) <[cities], more than, universities three>
(c) SELECT ?y WHERE {
?x rdf:type onto:University .
?x onto:city ?y . }
HAVING (COUNT(?x) > 3)
2. (a) Who produced the most films?
(b)<[person,organization], produced, most films>
?x rdf:type onto:Film .
?x onto:producer ?y .
ORDER BY DESC(COUNT(?x)) OFFSET 0 LIMIT 0
the original semantic structure of the question can not be faithfully captured using triples.

4. contribution a domain-independent question answering approach
the question (parse) a SPARQL template
Identify domain specific entities
SELECT ?x WHERE {
?x ?p ?y .
?y rdf:type ?c . }
ORDER BY DESC(COUNT(?y)) LIMIT 1 OFFSET 0

5. POS tagger

6. Who produced the most films?
POS tagger
(a) who/WP produced/VBD the/DT most/JJS films/NNS
Parsing and template generation
Domain independent lexicon:
107 entries: light verbs ,question words ,determiners, negation words, coordination and the like.
(b) Covered tokens: who, the most, the, most
Domain dependent lexicon: built on-the-fly.
POS tag ——> syntactic and semantic properties.
(c) Building entries for: produced/VBD, films/NNS

7. POS tag ——> syntactic and semantic properties.
Heuristics:
Named entities , resources.
Nouns , classes, properties.
Verbs, properties. If no contribution, instead by noun
(Which cities have more than 2 million inhabitants?)
syntactic representation semantic representation

8. Who produced the most films?
SPARQL templates:
Who produced the most films?
(a) SELECT ?x WHERE {
?x ?p ?y .
?y rdf:type ?c . }
ORDER BY DESC(COUNT(?y)) LIMIT 1 OFFSET 0
Slots:
<?c, class, films>
<?p, property, produced>
(b) SELECT ?x WHERE {
<?p, property, films>

9. String s——knowledge base K ——similar entity
Entity identification
String s——knowledge base K ——similar entity
Generic approach S
Property detection
Large number of expressions can be used to denote the same predicate.
(X, the creator of Y and Y is a book by X )
BOA pattern library
WordNet
Label(e)
Entities e
S(s)

10. Sentences: …"label(x) *label(y)" or "label(y) * label(x)"…
Pairs: I(p)={(x,y):(x p y)∈K}
NLE Ѳ : the form ?D? representation ?R? or ?R? representation ?D?
Distinguish patterns that are Specific to property p .
Support
Typicity
Specificity
pairs
x p y
Sentences: …"label(x) *label(y)" or "label(y) * label(x)"…
NLE Ѳ
Pairs (p, Ѳ)
BOA patterns

11. the highest scored query with a non-empty result.
Query ranking and selection
String similarity, prominence of entities and the schema of the knowledge base to score a query.
Entities score:
type checks on queries . (?x p e e p ?x )
Return:
the highest scored query with a non-empty result.

12. Evaluation and discussion
The QALD benchmark on Dbpedia: 50 questions annotated with SPARQL queries and answers.
Metric: Precision recall
Preliminary remark: manually corrected erroneous POS tags in seven questions.
11 questions rely on namespaces which we did not incorporate for predicate detection: FOAF ,YAGO

13. Unknown domain-independent expressions
Results:
19 p:1.0 r:1.0
P>0.8 r>0.8
Precision recall
Mean

14. The key advantage : Incorrect templates
The semantic structure of the natural language input is faithfully captured.
e.g. Complex questions containing quantifiers , comparatives, superlatives.
Don’t need any user feedback.
Incorrect templates
No sensible template is constructed.
Is there a video game called Battle Chess?
Property slot: title or name Rdfs:label
The structure of the templates is sometimes too rigid.
Join the EU prop:accessioneudate
The sporadic failure of named entity recognition.
Battle of Gettysburg

15. Entity identification
Class or property cannot be found on the basis of the slot.
Give me all soccer clubs in the Premier League. Onto:league
Give me all movies with Tom Cruise Onto:starring
Hard to match
Which cities have more than inhabitants
prop:populationTotal
Who owns Aldi onto:keyPerson
Which mountains are higher than the Nanga Parbat prop:elevation

16. Query selection others
A query with the wrong entity instantiating the slot is picked.
The slot contains too little information in order to decide among candidates.
Founded: prop:foundation, prop:foundingYear, prop:foundingDate,onto:foundationPerson, onto:foundationPlace
Which organizations were founded in 1950
When was Capcom founded
Which software has been developed by organizations founded in California
others
Namespace overlap and chosing one over the other often leads to different results of different quality.
…….

17. Future work Rigid templates: a preprocessing step
a more flexible fallback strategy
Provide robust question answering for large scale heterogeneous knowledge bases.

18. Thanks for your listening!