Welcome to POI-Interlinking’s documentation!¶
LGM-POInterlinking¶
This Python code implements and evaluates the proposed LinkGeoML models for POI classification-based interlinking.
In this setting, we consider several of the attributes connected to each spatio-textual entity to decide whether two POIs refer to the same physical spatial entity. Specifically, we utilize the following attributes which can be categorized as: (i) spatial, i.e., its coordinates, (ii) textual, i.e., its name and address name and (iii) number, i.e., its address number. For the textual ones, we utilize and adapt the meta-similarity function, called LGM-Sim, that was developed for the LGM-Interlinking problem. Consequently, we utilize the available richer spatial information to derive a set of informative training features to capture domain specificities and, thus, more accurately describe each POI. The proposed method and its derived features are used in various classification models to address the POI interlinking problem. Additionally, we perform a full machine learning workflow that involves the grid-search and cross-validation functionality, based on the scikit-learn toolkit, to optimize and optimally fit each examined model to the data at hand.
The source code was tested using Python 3 (>=3.6) and Scikit-Learn 0.22.2.post1 on a Linux server.
Setup procedure¶
Download the latest version from the GitHub repository, change to the main directory and run:
pip install -r pip_requirements.txt
It should install all the required libraries automatically (scikit-learn, numpy, pandas etc.).
Usage¶
The input dataset need to be in CSV format. Specifically, a valid dataset should have at least the following fields/columns:
The names for each of the candidate POI pairs.
The addresses for each of the candidate POI pairs.
The coordinates, i.e., latitude and longitude, for each of the candidate POI pairs.
The label, i.e., {True, False}, assigned to each POI pair.
The library implements the following distinct processes:
- Features extraction
The build function constructs a set of training features to use within classifiers for toponym interlinking.
- Algorithm and model selection
The functionality of the fineTuneClassifiers function is twofold. Firstly, it chooses among a list of supported machine learning algorithms the one that achieves the highest average accuracy score on the examined dataset. Secondly, it searches for the best model, i.e., the optimal hyper-parameters for the best identified algorithm in the first step.
- Model training
The trainClassifier trains the best selected model on previous process, i.e., an ML algorithm with tuned hyperparameters that best fits data, on the whole train dataset, without splitting it in folds.
- Model deployment
The testClassifier applies the trained model on new untested data.
A complete pipeline of the above processes, i.e., features extraction, training and evaluating state-of-the-art classifiers, for toponym interlinking can be executed with the following command:
$ python -m poi_interlinking.cli tune --train_set <path/to/train-dataset> --test_set <path/to/test-dataset>
Additionally, help is available on the command line interface (CLI). Enter the following to list all supported commands or options for a given command with a short description.
$ python -m poi_interlinking.cli -h
Usage: cli.py [OPTIONS] COMMAND [ARGS]...
Options:
-h, --help Show this message and exit.
Commands:
evaluate evaluate the effectiveness of the proposed methods
extract_frequent_terms create a file with ranked frequent terms found in corpus
hyperparam_tuning tune various classifiers and select the best hyper-parameters on a train dataset
learn_sim_params learn parameters, i.e., weights/thresholds, on a train dataset for similarity metrics
Documentation¶
Source code documentation is available from linkgeoml.github.io.
