1. Query Construct Interfaces of RDF Data an introduction
Qingxia Liu

2. Motivation Problem Functions in need Requirements
How to get useful information in RDF dataset
Functions in need
Browse + Query
Requirements
Easy to use
Easy to understand ( no need for training )
Portable (domain-independent)
Support complex query

3. Approaches of Construct Structured Query
Nature Language Input
NLP-Reduce, Ginseng(CNL)
Menu based
visKWQL, Explorator, GRQL
Form based
K-Search
Graph based
Visor, Semantic-Crystal, Affective Graph, NITELIGHT
Other
VisiNav

4. Ginseng A guided input natural language search engine Advantages
Base on a grammar
Input full sentence by selecting the possible completions
Advantages
Easy to understand and use
Disadvantages
Too restrictive : cause to repeatedly reformulate in a kind of back tracking behavior

5. Ginseng

6. visKWQL
A visual interface for the KWQL language to support constructing queries on semantic wiki
Advantages
Support complex query: conjunctions, disjunctions and negations of values or qualifier or resource terms;
Allows for creation of rules: reshape and aggregate results
Disadvantages
Hard to read when there is too much layer

7. ci(text:Java OR (tag(name:XML) AND author:Mary))
visKWQL
Select documents that either have “Java” in their text or that have the tag “XML” and were authored by Mary
KWQL
ci(text:Java OR (tag(name:XML) AND author:Mary))
visKWQL

8. Visor Overview-first data-on demand approach to browsing data
Advantages
Graphical representation
Allows explore from multiple points in the graph
Disadvantages
Cannot construct complex query ( only support binary relations between collections)

9. Visor
在出生地拍过电影的演员

10. VisiNav
Path traversal: select an object property to perform o set-based focus change
Advantages
Construct queries using navigation (object property)
Easy to refine
Disadvantages
Only support star queries

11. VisiNav

12. Problems of querying RDF data
Limited knowledge about target dataset
exploratory: offering surrounding information at each step
Difficult to use and understand Structured Query Language
controlled natural language
Structure ambiguous of NL
e.g. “Find courses which are prerequisites of courses whose course department is computer Science and whose course credit is 3.”[1]
tree-structured constructing procedure and represent

13. Our Method Interactive model Representation
Construct structured query while exploring on schema level
Representation
Controlled Nature Language
Avoid illegal query
Structured (tree-like) lay out
Easy to understand

14. Construct Procedure Init create the root node Modify Project
Node Setting
rename current node
delete current node
Value Constraint
class constraint: for uri / blanknode/literal resource
filter: value range
binary constraint
relation to existing variable
Link
add an arc to a new node
Project

15. Controlled Nature Language
Head:
Find All (x)
select all x that exists any patterns stated in the Query body. x is a list of projected variables.
Body:
that (statements)
leading of the body part
(x) has property…
a button leading to recommends of available links
(x) relate to …
a button leading to recommends of available binary constraints
(x) has (p y)/ (y) is p of (x)
stating an added pattern (?x p ?y)
x(C)
C displays value constraints on variable x, and x is a button leading to node setting and value constraint

16. Example reconstruct: maintain an exploring tree Information Need:
what do you want to find?
restart
reconstruct:
maintain an exploring tree
Find all ?film1 , ?runtime, ?director, ?writer, ?film2, ?runtime2,
that ?film1 (Film)
has runtime ?runtime (from 0 to 7200)
has director ?director (Director)
has made ?film2 (Film)
has property…
relate to…
has writer ?writer (Writer)
is writer of ?film1
has runtime ?runtime2 (from 7200 to 10800)
Information Need:
Find two films that has the same director and writer ,and the first film’s runtime between 0 and 7200 seconds, the second film’s runtime between 7200 and seconds;

17. References
[1] Thompson, Craig W., et al. "Building Usable Menu-Based Natural Language Interfaces To Databases." VLDB [2] A. Harth: VisiNav: A system for visual search and navigation on web data. Journal of Web Semantics, 8(4): , 2010 [3] E. Kaufmann and A. Bernstein. How useful are natural language interfaces to the semantic web for casual end-users? In 6th International Semantic Web Conference,pages 281–294, Nov [4] Nikolaos Athanasis, Vassilis Christophides, Dimitris Kotzinos: Generating On the Fly Queries for the Semantic Web: The ICS-FORTH Graphical RQL Interface (GRQL). International Semantic Web Conference 2004: [5] Samur Araújo, Daniel Schwabe: Explorator: A tool for exploring RDF data through direct manipulation. LDOW 2009 [6] Khadija Elbedweihy, Stuart N. Wrigley, Fabio Ciravegna. (2012). Evaluating semantic search query approaches with expert and casual users. In The Semantic Web–ISWC 2012 (pp ). Springer Berlin Heidelberg. [7] I．Popov, M. Schraefel, W. Hall, N. Shadbolt: Connecting the Dots: A Multi-pivot Approach to Data Exploration. In: Proc. of International Semantic Web Conference, , 2011 [8] Andreas Hartl, Klara A. Weiand, Fran?ois Bry: visKWQL, a Visual Renderer For a Semantic Web Query Language. Proceedings of the 19th international conference on World wide web(pp ). ACM, 2010.