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ESWC 2015论文阅读报告（两篇）
报告人：仇宏磊
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2. Requirements for and Evaluation of User Support for Large-scale Ontology Alignment
Valentina Ivanova1,2, Patrick Lambrix1,2(mail), and Johan Åberg1
1 Department of Computer and Information Science,
Linköping University, Linköping, Sweden
2 The Swedish e-Science Research Centre, Linköping University,
Linköping, Sweden
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Outline
Motivation
Related Work
Requirements for User Support
Literature Study
User Interface Evaluations
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Motivation
Short of user interface.
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Related Work
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Requirements for User Support
Requirements for users managing larger ontologies
Extracted from existing works and systems
Extracted from authors’ personal experience
Interaction & Representation
Manipulation
Inspection
Explanatory
#X.X – No. on the previous page and #5.X is listed on the next page
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Requirements for User Support
#5.X
#5.5 Environment
#5.1 Interruption of the alignment process
#5.6 Recommendations
#5.7 A debugging step during the alignment process
#5.2 Divide large-scale tasks into smaller tasks
#5.8 Configuring the matching process
#5.3 Reduce unnecessary user interventions
#5.9.1 Trial execution of mappings
#5.4 Social and collaborative matching
#5.9.2 Support for temporary decisions
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Literature Study
Well supported
The first four systems
Clustering of the content
Clustering during computation
Level: need a user study
Defects
Some cannot distinguish mapping and mapping suggestions
Reasons assists users in accepting mapping suggestions
Social and collaborative matching
Trial execution of mappings
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User Interface Evaluation
CogZ, COMA 3.0 and SAMBO
A heuristic evaluation
Usability issues
Nielsen’s ten heuristics
An observational study
3 master and 5 PhD (7 male, 1 female)
Each perform 11 and 17 tasks.
2 sessions lasted for 2h and 1h.
OAEI 2014-AMA (2737 concepts, 1807 asserted is-a relations)
NCI-A (3298 concepts, 3761 asserted is-a relations)
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User Interface Evaluation
Number of participants (max 8) successfully completed a task. /
Average task time per system in seconds.
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11. Semi-supervised Instance Matching Using Boosted Classifiers
Mayank Kejriwal(mail) and Daniel P. Miranker
University of Texas at Austin, Austin, USA
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Outline
Background
Core idea
Approach
Experiment
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Background
Blocking and Classification
Blocking: place instances into clusters
Classification: pair and classify instances
Supervised systems
Good performance
Heavy labeling effort
Minimally supervised
Less training data
Poor performance
Parameter tuning
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Core idea
Boost
Adaboost, ensemble classifier
Random Forest VS Multilayer Perception
Probabilistic instance matching
Iterative semi-supervised learning
Classification quality is certainly poor
Choose the most confidently labeled pairs
Iteratively self-train
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Approach
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Approach
Pre-classification Steps
Blocking approach: trigrams-based Attribute Clustering
Generating Restriction Sets: sets of class and property alignments (Q)
Extract Features: FEBRL package (G)
2 numeric features
8 string and token-based features, e.g. Common Token
18 phonetic features
Feature vector: |Q||G|
Classification Step
Parameters
Iteration number = 7
factor D & factor N = 2
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Experiment
Data
Persons1, Persons2, Restaurants from IAEI
Real-world benchmark: ACM-DBLP, Amazon-GoogleProducts, Abt-Buy
1k-2k matching pairs
Pre-classification Results
Perfect for IAEI benchmarks
Real-world: 95.44%, 97.43%, 83.54%
F-measure, re-weight F-measure
Training data
2% or 50 (whichever is less)
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Experiment
Random Forest
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Experiment
Multilayer Perceptron
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Experiment
Comparison to Minimally Supervised Approaches
Comparison to Supervised Approaches
10-fold cross: 79.25%
100 samples, FEBRL & Marlin
Amazon-GoogleProducts:30-40%, 50%; Abt-Buy, 20%, 60%
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Reference
Ivanova V, Lambrix P, Åberg J. Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment[M]//The Semantic Web. Latest Advances and New Domains. Springer International Publishing, 2015: 3-20.
Kejriwal M, Miranker D P. Semi-supervised Instance Matching Using Boosted Classifiers[M]//The Semantic Web. Latest Advances and New Domains. Springer International Publishing, 2015:
Falconer S M, Storey M A. A cognitive support framework for ontology mapping[M]. Springer Berlin Heidelberg, 2007.
Papadakis G, Ioannou E, Palpanas T, et al. A blocking framework for entity resolution in highly heterogeneous information spaces[J]. Knowledge and Data Engineering, IEEE Transactions on, 2013, 25(12):
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THANKS
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