Large knowledge bases consisting of entities and relationships between them have become vital sources of information for many applications. Most of these knowledge bases adopt the Semantic-Web data model RDF as a representation model. Querying these knowledge bases is typically done using structured queries utilizing graph-pattern languages such as SPARQL. However, such structured queries require some expertise from users which limits the accessibility to such data sources. To overcome this, keyword search must be supported. In this paper, we propose a retrieval model for keyword queries over RDF graphs. Our model retrieves a set of subgraphs that match the query keywords, and ranks them based on statistical language models. We show that our retrieval model outperforms the-state-of-the-art IR and DB models for keyword search over structured data using experiments over two real-world datasets.

1. Keyword Search over RDF Graphs
Shady Elbassuoni* and Roi Blanco**
* Max-Planck Institute for Informatics
** Yahoo! Research, Barcelona

2. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
RDF Datasets
subject predicate object
Traffic hasWonPrize Academy_Award
Innerspace hasWonPrize Academy_Award
Innerspace hasGenre Comedy
Joe_Dante directed Innerspace
Toy_Story hasWonPrize Academy_Award
Road_Trip hasGenre Comedy
Toy_Story hasGenre Comedy
Tom_Hanks actedIn Toy_Story
Diner hasWonPrize Academy_Award
Diner type Comedy_films
Steve_Guttenberg actedIn Diner
The_Pink_Panther type Criminal_comedy_films
The_Pink_Panther hasWonPrize Academy_Award
Police_Academy type Comedy_films
Steve_Guttenberg actedIn Police_Academy
The_Darwin_Awards type Comedy_films

3. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Searching RDF Data
 Structured triple-pattern queries (SPARQL)
 Example: comedies that have won an
academy award
SELECT ?m
WHERE {?m hasGenre Comedy . ?m hasWonPrize Academy_Award}

4. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Searching RDF Data
 Triple-pattern queries are very expressive
but are not that useable
 Most users/ Search APIs prefer keyword queries
Support keyword search over RDF graphs

5. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Keyword Search over RDF Data
 How to process keyword queries?
 Translate keyword queries into SPARQL
 Directly process the queries over the RDF graph
 What are the results to a keyword query?
 Resources
 Triples
 Tuples of triples (subgraphs)

6. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Keyword Search over RDF Data
 How to process keyword queries?
 Translate keyword queries into SPARQL
 Directly process the queries over the RDF graph
 What are the results to a keyword query?
 Resources
 Triples
 Tuples of triples (subgraphs)

7. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Keyword Search over RDF Data
 How to process keyword queries?
 Translate keyword queries into SPARQL
 Directly process the queries over the RDF graph
 What are the results to a keyword query?
 Resources
 Triples
 Tuples of triples (subgraphs)

8. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Keyword Search over RDF Data
 How to process keyword queries?
 Translate keyword queries into SPARQL
 Directly process the queries over the RDF graph
 What are the results to a keyword query?
 Resources
 Triples
 Tuples of triples (subgraphs)

9. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Processing Keyword Queries
 Construct a document D(t) for each triple t
 D(t) contains all literals in t and any text
associated with the URIs in t
 Example:
t: Innerspace hasGenre Comedy
innerspace USA1987 science fiction comedy film Joe
Dante Michael Finnell Dennis Quaid Martin Short Meg
Ryan academy award best visual effects …
innerspace USA1987 science fiction comedy film Joe
Dante Michael Finnell Dennis Quaid Martin Short Meg
Ryan academy award best visual effects …
We can now create triple-term indexes

10. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Retrieving Query Results
 For each query keyword, retrieve a list of triples
 Join the triples from different lists based on their URIs
comedy award
Innerspace hasGenre Comedy
Road_Trip hasGenre Comedy
Toy_Story hasGenre Comedy
Diner type Comedy_films
Police_Academy type Comedy_films
The_Darwin_Awards type Comedy_films
...
Traffic hasWonPrize Academy_Award
Innerspace hasWonPrize Academy_Award
Toy_Story hasWonPrize Academy_Award
Diner hasWonPrize Academy_Award
The_Darwin_Awards type Comedy_films
...
`
T: Innerspace hasGenre Comedy . Innerspace hasWonPrize Academy_Award

11. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Retrieving Query Results
 Retrieve a list of triples matching a query keyword
 Join the triples from different lists based on their URIs
comedy award
Innerspace hasGenre Comedy
Road_Trip hasGenre Comedy
Toy_Story hasGenre Comedy
Diner type Comedy_films
Police_Academy type Comedy_films
The_Darwin_Awards type Comedy_films
...
Traffic hasWonPrize Academy_Award
Innerspace hasWonPrize Academy_Award
Toy_Story hasWonPrize Academy_Award
Diner hasWonPrize Academy_Award
The_Darwin_Awards type Comedy_films
...
`
T: Innerspace hasGenre Comedy . Innerspace hasWonPrize Academy_Award
T: Toy_Story hasGenre Comedy . Toy_Story hasWonPrize Academy_Award

12. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Retrieving Query Results
 Retrieve a list of triples matching a query keyword
 Join the triples from different lists based on their URIs
comedy award
Innerspace hasGenre Comedy
Road_Trip hasGenre Comedy
Toy_Story hasGenre Comedy
Diner type Comedy_films
Police_Academy type Comedy_films
The_Darwin_Awards type Comedy_films
...
Traffic hasWonPrize Academy_Award
Innerspace hasWonPrize Academy_Award
Toy_Story hasWonPrize Academy_Award
Diner hasWonPrize Academy_Award
The_Darwin_Awards type Comedy_films
...
`
Result Ranking is crucial!!
T: Innerspace hasGenre Comedy . Innerspace hasWonPrize Academy_Award
T: Toy_Story hasGenre Comedy . Toy_Story hasWonPrize Academy_Award
T: Police_Academy type Comedy_Films . The_Darwin_Awards type Comedy_Films

13. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Language Models for Triples
D(t)
t:Innerspace hasGenre Comedy
Esitmate from
w P(w|D(t))
innerspace 0.234
1987 0.123
science 0.012
fiction 0.020
comedy 0.111
film 0.179
classic 0.111
meg 0.019
ryan 0.019
oscar 0.148
. . . . . .
w
P(w)

14. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Ranking Model
comedy award
T: Innerspace hasGenre Comedy . Innerspace hasWonPrize Academy_Award
but we treat triples as bags of words!

15. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Ranking Model
comedy award
T: Innerspace hasGenre Comedy . Innerspace hasWonPrize Academy_Award
probability of the structure of triple t
being relevant to keyword w

16. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Estimating Structural Relevance
 For each keyword, construct a probability
distribution over predicates
 Example: award
r P(r|w)
hasWonPrize 0.459
wasNominatedFor 0.387
type 0.112
directed 0.020
actedIn 0.021
producedIn 0.025
bornIn 0.008
. . . . . .
estimated from the whole dataset
P(Innerspace hasWonPrize Academy_Award|award) = P(hasWonPrize|award)

17. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Example Ranked Query Results
comedy award
Bag of Words
Combat_Academy type Comedy_films . The_Darwin_Awards type Comedy_films
Police_Academy type Comedy_films . The_Darwin_Awards type Comedy_films
Innerspace hasGenre Comedy . Innerspace hasWonPrize Academy_Award
Structure Aware
Innerspace hasGenre Comedy . Innerspace hasWonPrize Academy_Award
Toy_Story hasGenre Comedy . Toy_Story hasWonPrize Academy_Award
Shrek hasWonPrize Academy_Award_Best_Animated_Feature . Shrek hasGenre Comedy

18. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Experimental Setup
 User study over two RDF datasets:
 movies from IMDB
 books from LibraryThing
 Models compared:
 Structure Aware Approach
 Bag of Words Approach
 Language-model-based Object Retrieval
 BANKS (keyword search over databases)

19. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Experimental Setup
 30 evaluation queries
 Gathered relevance assessments for the top-
50 results retrieved by each model

20. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Experimental Results
P-value < 0.05

21. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Conclusion
 Keyword Search over RDF data is crucial
 To support keyword search over RDF data
 Combine structured triples with text
 Construct a document for each triple
 Retrieve meaningful query results
 Tuples of joined triples
 Can be extended to larger subgraphs of the RDF
graph
 Rank the retrieved results
 A language model approach that uses both text and
structure

22. Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011
Ranking Model

23. RDF Graphs
Shady Elbassuoni, Keyword Search over RDF Graphs, CIKM 2011