[GitHub] [systemds] Iseratho commented on a change in pull request #993: [SYSTEMDS-265] Entity resolution pipelines and primitives.

[GitBox <gi...@apache.org>]

Iseratho commented on a change in pull request #993:
URL: https://github.com/apache/systemds/pull/993#discussion_r457584817



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File path: scripts/staging/entity-resolution/README.md
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+# Entity Resolution
+
+## Pipeline design and primitives
+
+We provide two example scripts, `entity-clustering.dml` and `binary-entity-resolution.dml`. These handle reading input 
+files and writing output files and call functions provided in `primitives/pipeline.dml`.
+
+### Input files
+
+The provided scripts can read two types of input files. The token file is mandatory since it contains the row identifiers, 
+but the embedding file is optional. The actual use of tokens and/or embeddings can be configured via command line parameters 
+to the scripts.
+
+##### Token files
+
+This file type is a CSV file with 3 columns. The first column is the string or integer row identifier, the second is the 
+string token, and the third is the number of occurences. This simple format is used as a bag-of-words representation.
+
+##### Embedding files
+
+This file type is a CSV matrix file with each row containing arbitrary-dimensional embeddings. The order of row identifiers
+is assumed to be the same as in the token file. This saves some computation and storage time, but could be changed with 
+some modifications to the example scripts.
+
+### Primitives
+
+While the example scripts may be sufficient for many simple use cases, we aim to provide a toolkit of composable functions
+to facilitate more complex tasks. The top-level pipelines are defined as a couple of functions in `primitives/pipeline.dml`.
+The goal is that it should be relatively easy to copy one of these pipelines and swap out the primitive functions used
+to create a custom pipeline.
+
+To convert the input token file into a bag-of-words contingency table representation, we provide the functions
+`convert_frame_tokens_to_matrix_bow` and `convert_frame_tokens_to_matrix_bow_2` in  `primitives/preprocessing.dml`.
+The latter is used to compute a compatible contigency table with matching vocabulary for binary entity resolution. 
+
+We provide naive, constant-size blocking and locality-sensitive hashing (LSH) as functions in `primitives/blocking.dml`.
+
+For entity clustering, we only provide a simple clustering approach which makes all connected components in an adjacency
+matrix fully connected. This function is located in `primitives/clustering.dml`.
+
+To restore an adjacency matrix to a list of pairs, we provide the functions `untable` and `untable_offset` in 
+`primitives/postprocessing.dml`.
+
+Finally, `primitives/evaluation.dml` defines some metrics that can be used to evaluate the performance of the entity
+resolution pipelines. They are used in the script `eval-entity-resolution.dml`. 
+
+## Testing and Examples
+
+There is a test data repository that was used to develop these scripts at 
+[repo](https://github.com/skogler/systemds-amls-project-data). In the examples below, it is assumed that this repo is 
+cloned as `data` in the SystemDS root folder. The data in that repository is sourced from the Uni Leipzig entity resolution 
+[benchmark](https://dbs.uni-leipzig.de/research/projects/object_matching/benchmark_datasets_for_entity_resolution).

Review comment:
       On the mentioned website there are no comparison values. 
   For the DBLP-ACM dataset we get a F1-score of 0.8948. 
   For the Affiliations dataset we get a F1-score of 0.1429. 
   Note that we primarily focused on building the primitives and a basic pipeline. 
   We suspect that these numbers can be improved a lot by focusing more on data preprocessing (e.g., stemming). 




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