The document presents a new approach for keyword search on graph-structured RDF data, focusing on efficiently interpreting complex information needs by translating keywords into expressive formal queries. It outlines the process of top-k exploration of query candidates, including the construction of summary graphs and the use of scoring functions to optimize query results. Evaluations demonstrate the effectiveness and usability of the approach, highlighting improved performance over traditional search methods.

1. Top-k Exploration of Query Candidates
for Efficient Keyword Search on Graph-
Shaped (RDF) Data
Thanh Tran1, Haofen Wang2, Sebastian Rudolph1,
Philipp Cimiano3
1Institute AIFB, University Karlsruhe, Germany
2APEX Lab, Shanghai Jiao Tong University, China
3Web Information Systems, TU Delft, Netherlands

2. Motivation
• Semantic search
– Access to KB facts and semantically described documents
– Support for expressive / precise information need
• How to capture the user’s information need?
– Expressive queries with difficult syntax (SQL, SPARQL) vs.
limited but intuitive queries (Keywords)
– Expressive power is crucial!
– Support the user in specifying information needs in an
intuitive way is also crucial!
• Goal: Interpreting Complex Information Needs by
Translating Keywords to Expressive Formal Queries

3. Related Work
• Translation of NL questions
– Can the user specify a precise question when the
information need is vague?
• Relaxed-structure query models
– Require some knowledge about the query syntax and
the structure of the underlying data
• Labeled query models
– Require some knowledge about schema elements
• In keyword search, the user does not need to
know about the query syntax and data schema
– Crucial for environment like the Web where most data
sources to be queried are unknown to the user

4. Scenario – Interpreting Information Needs
User Information Need
RDF Data Graph
Query Specification
„2006 Philipp Cimiano X-Media“
Query Translation
Query Processing
SELECT ?x , ? y , ? z WHERE {
? x type Publication . ? x year 2006 .
? x author ?y . ? y name ’P . Cimiano ’ .
? y worksAt ? z . ? z name ’AIFB’}

5. Keyword Search – An Overview
• Mapping of keywords to ”labels” of data elements
– Result in a set of keyword elements
– Through imprecise matching, user even does not need to know the
labels of data elements (c.f. precise matching in [G. Bhalotia et al.])
• Data Graph exploration
– Search for substructures (query graph) connecting keyword elements
– Query graph vs. answer trees [H. He et al.]
– Exploration of query graphs operates on summary of data graph only
• Top-k computation
– Search guided by a scoring function to output only the top-k results
– Guaranteed top-k vs. approximate top-k V. [V. Kacholia et al.]
• Mapping query graph to conjunctive query
• Processing the conjunctive query using standard query engine

6. Keyword Search – The Workflow
• Offline: Summarization, Scoring, Term Expansion
• Online: Query Computation, Query Processing

7. Graph Summarization
• Goal: preserve sufficient information to compute elements and
structure of the query, while reducing the exploration space
• Summary graph captures relations between entity classes, thus
preserve structural information of the original data graph
Summary Graph
Example RDF Graph

8. Keyword Mapping & Graph Augmentation
• Summary graph captures information for exploration of query structure
• Online augmentation with elements & scores obtained from keyword mapping
• Augmented graph contains further information for exploration of query elements
„2006
Philipp Cimiano
AIFB“
Keyword Query
Summary Graph Augmented Summary Graph

9. Top-k Graph Exploration
• Cost-directed exploration of the graph, starting from keyword elements Nk
• Explore all possible distinct paths starting from nk 2 Nk
• At each step, take cursor (“path”) from queues with lowest cost for exploration
• When a connecting element nc is found,
• Paths from nk to nc are merged to construct the query graph
• Top-k is invoked to add query graph to candidate list
• Top-k terminates when highest cost of the candidate list (the cost of the k-
ranked query graph) is found to be lower than the lowest possible cost that can
achieved with paths in the queues yet to be explored
Augmented Summary Graph Explored Paths

10. Mapping Query Graph to Conjunctive Query
• Conjunctive query obtained by exhaustive application of mapping rules
• Every value vertex vvertex  a term
• Every class vertex cvertex  a distinct variable
• Every A-edge e(cvertex, vvertex)  a query predicate e[var(cvertex), term(vvertex)]
• Every R-edge e(cvertex1, cvertex2)  a query predicate e[var(cvertex1), var(cvertex2)]
• Treat all query variables as distinguished
• Specific mechanisms can be provided for the user to choose distinguished variables
• Query chosen by the user finally translated to query formalism supported by the
query engine (SPARQL) for retrieving query answers
Query Graph Conjunctive Query

11. Rich Client Demo – xXploreKnow!
http://ontoware.org/projects/xxplore/

12. Web Demo – Q2Semantic
http://q2semantic.apexlab.org/UI.html

13. Evaluation – Effectiveness
• 12 users provide 30 keyword queries on DBLP, along with the
NL description of the information need
• Reciprocal Rank = 1/r, where r is the rank of the correct query
• A query is correct if it matches the information need
• Information need can be interpreted in most cases, in
particular when path length, matching score as well as
popularity of graph elements are incorporated into scoring
function (C3)
1
0.8
0.6 C1
0.4 C2
0.2 C3
0
Q1 Q3 Q5 Q7 Q9 Q11 Q13 Q15 Q17 Q19 Q21 Q23 Q25 Q27 Q29
MRRs of different Scoring Functions on DBLP

14. Evaluation – Usability of Query Interpretation
- Standard approaches return top-k results
- Our approach based on interpretation of keywords as
queries, i.e. compute top-k queries instead of top-k
answer trees [V. Kacholia et al.] [H. He et al.]
- Queries are then transformed to simple natural
language and presented to user
- 90% of users prefer to obtain question first, since it
facilitates understanding of results
- All user prefers to do refinement on the structured
query, rather than on the keywords, since the
structured query can be manipulated in a more
precise and predictable way

15. Evaluation – Efficiency
• Comparison with bidirectional search [V. Kacholia et al.] and search based on
graph indexing (1000 BFS, 1000 METIS, 300 BFS, 300 METIS in [H. He et al.])
• We measure time for query computation + time for processing several
queries until finding 10 answers
• Outperforms bidirectional search by at least one order of magnitude
• Performs fairly well when compared to indexing based approaches
100000
10000 Our Solution
1000 Bidirect
1000 BFS
100
1000 METIS
10 300BFS
1 300METIS
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10
Query Performance on DBLP Data

16. Conclusions and Future Work
• Conclusions
– A new approach for keyword search on graph-structured
data, RDF in particular
– Novel algorithms for the top-k exploration of subgraphs to
compute queries as an additional intermediate step
– Query computing is performed on an aggregated graph
while query processing can leverage optimization
capability of the database
• Future Work
– Indexing connectivity and scores for further speed up
– Consider special query operations (e.g. filters) as keywords

17. Thank you for your attention!
Q&A