The document discusses the evolution of the web from documents to data, and introduces linked data which publishes machine-readable data on the web that is explicitly defined and linked to other datasets. It then discusses question answering systems that take natural language questions and locate answers from document collections, including both closed-domain systems with restricted knowledge bases and open-domain systems that retrieve answers from the web. The document also presents the linked data technology stack and some examples of linked open data clouds from 2007 to 2011 to demonstrate the growth of linked data on the web.

2. QALD
Question Answering
over
Linked Data
2

3. The Web of Documents
 Traditional Web, Hypertext Web
 Analogy
 A global file system
 Designed for
 Human consumption
 Primary objects
 Documents
 Links
 Untyped
 Between documents (or parts of documents)
 Degree of structure in object
 Fairy low
 Semantics of content and links
 implicit
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4. The Web of Documents
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5. The Web of Data
 Analogy
 A global data space
 Designed for
 Machines first, humans later
 Primary objects
 Things (description of things)
 Links
 Typed
 Between things
 Degree of structure in objects
 High
 Semantic of content and links
 Explicit
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6. The Web of Linked Data
6

7. Linked Data
• Is about using the Web to create typed links
between data from different sources
• Refers to data published on the Web in
such a way that
– It is machine-readable
– Its meaning is explicitly defined
– It is linked to other datasets
– It can be linked to from external datasets
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8. Properties of the Web of Data
• It is generic
– Can contain any type of data
• Data about anything
– Anyone can publish data
– No constraints on choice of vocabularies
– entities are connected by RDF links
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9. A Taste of Linked Data
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10. A Taste of Linked Data (Cont.)
10

11. Linked Data Technology Stack
URIHTTP
RDFS / OWL
RDF / RDF Links
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12. Ontology , RDF
• Ontology provides a means to vocabularies
and link’s semantics on linked data.
• RDF provides a generic, graph-based data
model to structure and link data that
describes things
• A triple [subject, predicate, object]
– Subject: a URI
– Predicate: a URI
– Object: a URI or a string literal
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13. LOD Cloud : May 2007
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14. LOD Cloud : July 2007
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15. LOD Cloud : August 2007
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16. LOD Cloud : November 2007
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17. LOD Cloud : February 2008
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18. LOD Cloud : March 2009
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19. LOD Cloud : July 2009
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20. LOD Cloud : September 2010
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21. LOD Cloud : September 2011
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22. Retrieval Process
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• a) Query construction
• b) Search algorithm of the system
• c) Presentation of the results
Search Engines
Query A lot of related Web Pages
QA Systems
Question Exact Answer

23. Retrieval Process (Cont.)
23
Query Analyzer
Query
A lot of
Web Pages
WWW
Crawler
RequestWeb Pages
Index
File
Indexer
Web Pages
Index TermsSearch
Ranking
Results Doc
Datastore
Web Pages
UI
Query
Ranked Results
Web Pages
• Traditional SE

24. Some Problems
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• Information search with search engines

25. Therefore, what is needed?
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• Assign meta data to information objects
• Content description with concepts and relations between
them
• Provision of background knowledge
• Provision of the semantics of relations for query
extension, ontology integration, etc.
RDF
RDF Schema, OWL, Rules

26. 26
Question Answering
• Question answering (QA) systems take users’
natural language questions and automatically locate
answers from large collections of documents.
• Two types of QA systems
– Closed-Domain (or restricted domain) Question Answering
– Open-Domain Question Answering

27. 27
Question Answering (Cont.)
• Open Domain QA System
Question
Analysis
Answer
Selection
Question Query
Answer
Type
Documents
Answer (s)
Question
Answer (s)
UI
Document
Analysis
Passages
Document Retrieval
Systems
Document
Retrieval
Open Domain
Ontology

28. 28
Question Answering (Cont.)
• Restricted Domain QA System
Question
Analysis
Answer
Post Processing
Question Query
Answer (s)
Question
Answer (s)
UI
Answer
Retrieval
Answer (s)
Data
Open Domain
Ontology
Lexicon
Domain
Ontology
Knowledge Base

29. Related Works
• AquaLog
– Vanessa Lopez, Victoria Uren, Enrico Motta, Michele Pasin.
• PowerAqua
– Vanessa Lopez, Andriy Nikolov, Marta Sabou, Victoria Uren, Enrico Motta,
Mathieu d’Aquin
• QASYO
– for YAGO Ontology
• AutoSPARQL
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30. FREyA Algorithm
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31. Questions?
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Email: R.Ramezani@ec.iut.ac.ir