Question answering system plays a vital role in search engine optimization model. Natural language processing methods are typically applied in QA system for inquiring user’s question and numerous steps are also followed for alteration of questions to query form for receiving a precise answer. This presentation analyzes diverse question answering systems that are based on semantic web technologies and ontologies with different formats of queries.It ends by addressing various reasoning alternatives.

1. QUESTION ANSWERING
OVER LINKED DATA
REASONING ISSUES
Michael Petychakis
Semantic Web Technologies

2. Overview
 Problem Description
 What is Question Answering
 Question Answering Challenges
 Linked Data Challenges
 Systems Analysis
 Data Linking
 Question Answering
 Question Answering Evaluation Metrics
 Reasoning Analysis
 Reasoning Challenges
 Conclusion

3. What is Question Answering?
3
QA
System
Knowledge
Bases
Question: From which
university did the wife of
Barack Obama graduate?
Answer: Harvard Law
School

4. Querying Data over the Web
 (a) natural language query over two search engines;
 (b) corresponding SPARQL representation;
 (c) semantic gap between the user’s information needs and data

5. Expressivity-Usability Trade-Off
 Expressivity–usability trade-off for querying over structured data.
 Blue dots indicate an ideal query mechanism for linked data must provide both high
expressivity and high usability

6. Structured Queries Vs. Q&A
Structured Queries:
 A priori user effort in
understanding the schemas
 Effort in mastering the syntax of
a query language
 Satisfying information needs may
depend on multiple querying
operations
 Input: Structured query
 Output: data records,
aggregations etc.
6
Q&A:
 Delegates more ‘interpretation
effort’ to the machines
 Input: natural language query
 Output: direct answer

7. Ability to query datasets by referencing elements in data model structure,
as well as to operate over the data (aggregate results, express conditional
statements, etc.)
Easy-to-operate, intuitive, and task-efficient query interface
Matches entities expressed in the query to semantically equivalent dataset
entities
Ability to answer queries not supported by explicit dataset statements
e.g. “Is Natalie Portman an Actress?” can be supported by the statement “Natalie Portman starred Star
Wars,” instead of an explicit statement “Natalie Portman occupation Actress,” which might not be present in
dataset
Ability to semantically match user query terms to dataset vocabulary-level
terms
Question Answering Challenges
Query
Expressivity
Usability
Entity
Reconciliation
Semantic
Tractability
Vocabulary-
level Semantic
Matching

8. Linked Data Challenges
 Linked Data,
 Is huge
 Is not “pure”
 Is inconsistent
 Is evolving
 Needs more than RDFS and OWL
 What is needed from the Semantic Web Pie?
 What parts of RDFS and OWL do people use?

9. Data Linking Systems
 Normalize information to common
vocabularies
 Structured/Semi-Structured Information to
Linked Data
 Automated/Semi-Automated Mechanisms
 Solve the Semantic Tractability, the
Vocabulary-level Semantic Matching, and the
inconsistency problems.

10. Data Level Knowledge
Level
System Internal External
LN2R String matching - Word Net synonyms
dictionary
COREF String matching - -
OKKAM String matching Translation service Entity names vocabulary
LDMAPPER String lookup search - -
SILK String matching numerical similarity - -
LIMES String matching on metric spaces - -
Knofuss String matching - -
RDF-AL String matching Translation service Word Net taxonomic
distance
Zhishi.links String matching - Abbreviations list
Data Linking Systems Analysis
(1/2)

11. Data Level Knowledge
Level
System Internal External
Serimi String matching - -
Knofus String matching on numerical similarity - -
Limes String matching Translation service Word Net
Querix String matching on metric spaces Translation service Word Net
Quacid String matching - -
SWSE String matching - -
SMART String matching - -
ORAKEL String matching - -
AQALOG String matching - -
Data Linking Systems Analysis
(2/2)

12. Question Answering
Methodology
Phrase
Detection
Phrase
Mapping to
KB
Q-Unit
Generation
Query
Generation

13. Popular Question Answering
Systems
 FREyA
 SemSek
 Alexandria
 MHE
 QAKIS
 SWIP
 BELA
 Intui2
 OntoNL

14. Question Answering System
Example
e.g. FreyA

15. Question Answering Evaluation
Metrics

16. Reasoning Challenges on Q&A
 Abrupt and strict answers ( Not statistical
approach)
 Inefficient
 Not Scaling
 Modeling Complexity

17. Reasoning Approaches for LD
 Context-Dependent Reasoning
 Authoritative Reasoning
 LiDaQ
 Link Traversal Based Query Execution(LTBQE)
extension with Reasoning

18. Conclusion
 Open Questions
 Graph Inference Algorithms
 Scaling Reasoning on the Web
 Context Dependent Reasoning
 Are there better standards for Linked Data?
 Future Work

19. Questions and Discussion

20. The End