Serene Benchmark

The Serene Benchmark project provides a common framework to evaluate different approaches for schema matching and mapping. Currently, the framework supports evaluation of three approaches for semantic labeling of relational data sources.

Prerequisites

To run evaluation benchmark,

1. The server for Karma DSL needs to be installed and started.
2. The server for Serene needs to be started.
3. Neural nets: tensorflow and keras python packages.
4. Serene Python client should be installed.

Decompress sources and labels in the data folder.

To install the package 'serene-benchmark', run

python setup.py install

Run

There are three different approaches for semantic typing which can be currently evaluated in this project:

1. DSL: domain-independent semantic labeller
2. DINT: relational data integrator
3. NNet: deep neural nets MLP and CNN. Additionally, there is an implementation of a random forest using scikit.

Deep learning

For NNetModel, allowed model types are: 'cnn@charseq' (CNN on character sequences) and 'mlp@charfreq' (MLP on character freqs + entropy). There is also 'rf@charfreq' which uses scikit implementation of random forests while DINT uses Spark mllib.

DINT

DINT feature configuration is explained here, and resampling strategy here.

DSL

DSL can be run in two modes: "normal" when labeled data is used only from one domain or "enhanced" when labeled data from other domains is used. Its approach is exaplained in the paper.

benchmark.py provides a running example of two experiments to evaluate different approaches for semantic labeling.

To add another semantic labeling approach to the benchmark, one needs to create a class which inherits from SemanticTyper and implements 4 methods: degifine_training_data, train, reset and predict. One can also modify initialization of the class instance.

How to test

Nose unittests needs to be installed. To run the tests:

nosetests