Keyword search approaches over RDF graphs have proven intuitive for users. However, these approaches rely on local copies of RDF graphs. In this paper, we present an algorithm that uses RDF keyword search methodologies to find information in the live Linked Data web rather than against local indexes. http://events.linkeddata.org/ldow2015/papers/ldow2015_paper_06.pdf

1. Keyword-Based Navigation and Search
over the Linked Data Web
Luca Matteis1, Aidan Hogan2, Roberto Navigli1
1 Sapienza University
of Rome
2 University of
Chile

2. General idea
• Browse the live linked data web using keywords
• Predicate resolution along the navigation to
increase matches
• Results are streamed back to users as quickly as
possible
• We measure how fast relevant triples are found at
each step of the navigation

4. Navigation
• Navigation starts from a list of starting URIs
• Users/agents provide keywords to search against
and guide the navigation
• Navigation is structured using a streaming
pipeline

5. Search
• Search occurs at each element of the pipeline
• Several RDF keyword search algorithms can be used
• Predicate resolution is used to increase number of
matches

8. SWGET comparison
• SWGET is an implementation of the NautiLOD
navigational language
• It allows to filter (through SPARQL) triples at each
step at the navigation
• We show that our pipeline streaming approach
results in faster response times

9. SWGET comparison

10. Results
• Total response time is under 10 seconds (varies
based on the number of keywords)
• Navigation hop time averages ~5 seconds
Discussion
• Results point to the fact that keyword-navigation
is achievable, although a bit sluggish.
• Experiments were on the live linked data web!
Servers optimized for concurrency and high-
throughput (triple pattern fragments) might yield
faster response times.

11. Final remarks
• Our approach incentives publishers to enrich their
structured data (using predicates with meaningful
descriptions)
• Concurrent resolution of many URIs at runtime to
find answers to queries is becoming more and
more viable; increase in bandwidth is going to
make this even more usable
• Upfront querying may not be the only way we
query the Web of Linked Data

12. Use case

13. Use case

14. Use case
dir suggestions
codirector (8)
redirection (4)
director (1)
nadir (1)
…

15. Use case
director 1 triple found (view)

16. Use case
director 1 triple found (view)
know suggestions
known for (17)
knows (6)
knowledge of (5)
…

17. Use case
director 1 triple found (view)
known for 17 triples found (view)

18. Use case
director 1 triple found (view)
known for 17 triples found (view)

19. Use case
director 1 triple found (view)
known for 17 triples found (view)
act suggestions
actor (56)
abstract (48)
…

20. Use case
director 1 triple found (view)
known for 17 triples found (view)
actor 56 triples found (view)

21. Users don't have to input URIs
(as they do when writing SPARQL)
Nor they have to know the exact
structure of the underlying dataset
(they simply type keywords)
SELECT * {
<http://viaf.org/viaf/177603646>
onto:mov100 ?movement .
?movement my:lab ?label .
}
http://viaf.org/viaf/177603646 /
movement /
name

22. Query federation is built-in
(we're simply following links)
http://viaf.org/viaf/177603646 /
movement /
same as /
movement of /
born < 1960 /
same as freebase /
name
} VIAF
} DBpedia
} Freebase

23. Future work
• Develop a functioning app (browser extension or
add-on to Tabulator)
• Use third-party services to assist the navigation by
matching synonyms or translations (BabelNet,
WordNet)
• Use other third-party services to assist in the
disambiguation of words using the context of the
data acquired along the navigation (Babelfy)
• Better methods for effectively crawling Linked
Datasets at runtime (that don't strain servers and
provide quick response times)

24. Thanks!
@lmatteis
http://lucaa.org