1. Property consolidation for entity browsing徐江

2. Scenario
In fact, due to the fusion of entity descriptions from different sources, it frequently happens that many different properties from a variety of sources are semantically equivalent.

3. Property consolidation
Property consolidation, namely to consolidate a set of semantically equivalent properties into a feature.
Currently, SView enables personalized property consolidation.
How to automatically discover equivalent properties to alleviate user involvement ?

4. Related work
Cheatham, Michelle, and Pascal Hitzler. "The Properties of Property Alignment." Proceedings OM-2014, The Ninth International Workshop on Ontology Matching, at the 13th International Semantic Web Conference, ISWC
An in-depth exploration of the performance of current alignment systems on the only commonly accepted alignment benchmark that involves matches between properties.
A benchmark involving properties is also proposed: YAGO-DBPedia
core concept: either the first verb in the label that is greater than four characters long or, if there is no such verb, the first noun in the label, together with any adjectives that modify that noun.

5. Related work linguistic techniques used in ontology matching:
Edit-Distance-Based Strategy: isub, WordNet…
Vector Distance (VD)-Based Strategy:
- virtual document of a URIref declared in an ontology
- Document similarity can be computed by traditional
vector space techniques( tf*idf … )

6. Related work
Existing approaches address entity coreference mainly from two directions:
- equivalence inference mandated
by OWL semantics (sameas, ifp, fp, cardinality)
- similarity computation between property-value pairs

7. Related work
Hu, Wei, Jianfeng Chen, and Yuzhong Qu. "A self-training approach for resolving object coreference on the semantic web." Proceedings of the 20th international conference on World wide web. ACM, 2011.
For an RDF graph G, the matchability between two properties pi, pj in a kernel set D for an object URI is computed by:

8. Related work
Gong, Saisai, Wei Hu, and Yuzhong Qu. "Leveraging Distributed Human Computation and Consensus Partition for Entity Coreference." The Semantic Web: Trends and Challenges. Springer International Publishing,
Finds other entity descriptions that may be consolidated with it by using machine learning and will be confirmed by the user.

9. Matching schemas in online communities: A web 2.0 approach ICDE 2008
Why need interaction
The fundamental reason is that matching is an inherently knowledge-intensive activity
Challenges and Solution
which questions to ask users
Verify intermediate predictions
bday is of type DATE
Learn simple domain integrity constraints
Verify final match predictions
For complex matching
indirectly evaluation
evaluating community users’ reliability and combining their answers
Simple trusted user and untrusted user
How to post question
mix user evaluation question with true question
Post the same question to different users follow a distribution model

10. Matching schemas in online communities: A web 2.0 approach ICDE 2008

11. A hybrid machine-crowdsourcing system for matching web tables ICDE 2014
Challenge:
What constitutes a “beneficial” column and should therefore be crowdsourced to determine the right concept for that column
Solution
Utility function
Matching difficulty of a column
Influence of column

12. A hybrid machine-crowdsourcing system for matching web tables ICDE 2014
α: prior probabilities of the machine
1−α: crowdsourcing influence

13. Large-scale Interactive Ontology Matching: Algorithms and Implementation ECAI 2012
it is crucial to reduce the number of questions to the human expert
automatic decisions based on
users’ feedback can significantly
reduce the number of questions
in practice
Ambiguity
Conflicts with semantic index

14. Overview
Given an entity identified by a set of coreferent uris with properties and corresponding property values, get a list of features
Three steps of the approach
Discover matchable properties from their meta-information and values
Highly confident features are to be confirmed by the user
Collect all users’ results to optimize the model.

15. Prop consolidation wizard (1)

16. Prop consolidation wizard (2)

17. Thanks