The document discusses the vision of the Semantic Web and how it has evolved since 2001. It describes how Linked Data has helped address earlier issues with consistency and scalability by providing a structured data representation at web scale. It also discusses how natural language processing, information retrieval, and distributional semantics can help bridge the gap between structured Linked Data and flexible natural language by semantically matching queries to knowledge graphs. The document concludes by outlining semantic application patterns that can be used to build intelligent systems by maximizing knowledge, allowing dynamic databases, and incorporating user feedback.

1.  Copyright 2009 Digital Enterprise Research Institute. All rights reserved.

2.  The Semantic Web vision & Linked Data
 Multi-disciplinary perspective
 Linked Data, IR, NLP
 Case study: Treo
 Talking to the Linked Data Web
 Semantic application patterns
 Take-away message

4. 2001:
 Software which is able to
understand meaning
(intelligent, flexible)
 Leveraging the Web for
information scale

5.  What was the plan to
achieve it?
 Build a Semantic Web
Stack
 Which covers both
representation and
reasoning

6.  Adoption:
 No significant data
growth
 Ontologies are not
straightforward to
build:
 People are not
familiriazed with the
tools and principles
 Difficult to keep
consistency at Web scale
 Scalability

7.  Problems:
 Consistecy
 Scalability
Logic World Web World

8. 2006:
 The Web as a Huge Database
 Fundamental step for data
creation

9.  Where is the intelligence and
flexibility?
 We will be back to this point
in a minute

10.  Data Model Features:
 Graph-based data model
 Extensible schema
 Entity-centric data integration
 Specific Features:
 Designed over open Web standards
 Based on the Web infrastructure (HTTP, URIs)

11.  Positives:
 Solidadoption in the Open Data context
(eGovernment, eScience, etc,...)
 Existing data is relevant (you can build real
applications)
 Negatives:
 Data consumption is a problem
 Datageneration beyond databases
mapping/triplification is also a problem
 Still far from the Semantic Web vision

13.  How to address the previous challenges?
 Linked Data:
 Web-scale structured data representation
 Information Retrieval:
 Search, approximation, ranking strategies
 Scalability
 Natural Language Processing (NLP):
 Analysing natural language
 Semantic approximation (distributional semantics)

14.  IBM Watson approach

16.  With Linked Data we are still in the DB world
From which university did the wife of
Barack Obama graduate?

17.  With Linked Data we are still in the DB world
 (but slightly worse)

19. From which university did the wife of Barack Obama graduate?

20. ): Direction, path
Demonstration

30.  Transform natural language queries into triple patterns
 Steps:
 Entity Recognition
“From which university did the wife of Barack Obama graduate?”
 Dependency parsing
 Query Pattern detection prep(graduate-10, From-1) From/IN
det(university-3, which-2) which/WDT
 Query Planning pobj(From-1, university-3) university/NN
aux(graduate-10, did-4) did/VBD
det(wife-6, the-5) the/DT
nsubj(graduate-10, wife-6) wife/NN
prep(wife-6, of-7) of/IN
nn(Obama-9, Barack-8) Barack/NNP
pobj(of-7, Obama-9) Obama/NNP
root(ROOT-0, graduate-10) graduate/VB
Using NLP ?/.

31. Query:
Using NLP

32.  Entity Search:
 Build an entity index (instances)
 Extract terms from URIs and index the terms using your
favourite IR framework
 Search instances by keywords
Using IR

33. Query
Linked Data
Web
Using IR

34.  Use distributional semantics to semantically match
query terms to predicates and classes
 Distributional principle: Words that co-occur together
tend to have related meaning
 Allows the creation of a comprehensive semantic model from
unstructured text
 Based on statistical patterns over large amounts of text
 No human annotations
 Distributional semantics can be used to compute a
semantic relatedness measure between two words
Using NLP
and IR

35.  Computation of a measure of “semantic proximity”
between two terms
 Allows a semantic approximate matching between
and
 It supports a reasoning-like behavior based on the
knowledge embedded in the corpus
Using NLP
and IR

36. Query
Which properties are
semantically related to ‘wife’?
Linked Data
Web
Using NLP
and IR

37. Query
Linked Data
Web
Using NLP
and IR

38. Query
Linked Data
Web
Using NLP
and IR

39. Query
Linked Data
Web
Using NLP
and IR

40.  Semantic approximation in databases (as in any IR
system): semantic best-effort
 Need some level of user disambiguation,
refinement and feedback
 As we move in the direction of semantic systems
we should expect the need for principled dialog
mechanisms (like in human communication)
 Pull the the user interaction back into the system
Using NLP
and IR

44.  Derived from the experience developing Treo
 Not restricted to queries over Linked Data
 The following list is not intended to be complete

45.  Pattern #1: Maximize the amount of knowledge in
your semantic application
 Meaning interpretation depends on knowledge
 Using LOD: DBpedia, Freebase, YAGO can give you
a very comprehensive set of instances and their
types
 Wikipedia can provide you a comprehensive
distributional semantic model

46.  Pattern #2: Allow your database to grow
 Dynamic schema
 Entity-centric data integration

47.  Pattern #3: Once the database grows in complexity
use semantic search instead of structured queries
 Instances can be used as pivot entities to reduce
the search space
 They are easier to search
 Higher specificity and lower vocabulary variation

48.  Pattern #4: Use distributional semantics and
semantic relatedness for a robust semantic
matching
 Distributional semantics allows your application to
digest (and make use of) large amounts of
unstructured information
 Multilingual solution
 Can be complemented with WordNet

49.  Pattern #5: POS-Tags, Syntactic Parsing + Rules will
go a long way to interpret natural language queries
and sentences
 Use them to explore the regularities in natural
language
 Define a scope for natural language processing in
your application (restrict by domain, syntactic
complexity)
 These tools are easy to use and quite robust (at
least for English)

50.  Pattern #6: Provide a user dialog mechanism in the
application
 Improve the semantic model with user feedback

51.  Part of the Semantic Web vision can be addressed
today with a multi-disciplinary perspective
 Linked Data, IR and NLP
 You can build your own IBM Watson-like application
 Both data and tools are available and ready to use:
the barrier is the mindset
 Large opportunity for new solutions

52.  NLP  Datasets
 WordNet  DBpedia
 VerbNet  Freebase
 Stanford parser  YAGO
 C&C parser/Boxer
 NLTK
 Tools that will be
 DBpedia Spotlight available soon:
 Gate  Treo
 UIMA  Treo-ESA
 IR  Graphia
 Lucene/Solr
 Terrier

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International Journal of Semantic Computing (IJSC),
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André Freitas, Sean O'Riain, Edward Curry,
. 27th ACM Applied Computing Symposium, Semantic Web and Its
Applications Track, 2012.
André Freitas, João Gabriel Oliveira, Sean O'Riain, Edward Curry, João Carlos Pereira da
Silva, In
Proceedings of the 16th International Conference on Applications of Natural Language to
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André Freitas, Danilo S. Carvalho, João Carlos Pereira da Silva, Sean O'Riain, Edward Curry, A
Semantic Best-Effort Approach for Extracting Structured Discourse Graphs from Wikipedia. In
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54. andrefreitas.org
andre (dot) freitas – at – deri (dot) org