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maliedvp/
dblpacm_neer_match_DL

Name: dblpacm_neer_match_DL
Availability: InStock

entity-matching

similarity-comparison

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Preprocessing

Before training, the DBLP-ACM dataset was preprocessed using the prepare.format function from the neer-match-utilities library. The following preprocessing steps were applied:

Numeric Harmonization:
- Missing numeric values were filled with 0.
- The year column was converted to numeric format.
String Standardization:
- Missing string values were replaced with placeholders.
- All string fields were capitalized to ensure consistency in text formatting.

These preprocessing steps ensured that the input data was harmonized and ready for training, improving the model's ability to compare and match records effectively.

Similarity Map

The model uses a SimilarityMap to compute similarity scores between attributes of records. The following similarity metrics were applied:

python

similarity_map = {
    "title": ["levenshtein", "jaro_winkler", "partial_ratio", "token_sort_ratio", "token_set_ratio", "partial_token_set_ratio"],
    "authors": ["levenshtein", "jaro_winkler", "partial_ratio", "token_sort_ratio", "token_set_ratio", "partial_token_set_ratio"],
    "venue": ["levenshtein", "jaro_winkler", "partial_ratio", "token_sort_ratio", "token_set_ratio", "partial_token_set_ratio", "notmissing"],
    "year" : ["euclidean", "gaussian", "notzero"],
}

Fitting the Model

The model was trained using the fit method and the focal_loss loss function with alpha=0.15 and gamma=15.

Training Configuration

The training parameters deviated from the default values in the following ways:

Epochs: 60
Mismatch Share: 1.0

Before training, the labeled data was split into training and test data, using the split_test_train method of neer_match_utilities with a test_ratio 0f .8

maliedvp/dblpacm_neer_match_DL

Preprocessing

Similarity Map

Fitting the Model

Training Configuration

maliedvp/
dblpacm_neer_match_DL