Computing robust explainable clusters for a set of entities. Clusters' explanations are produced based on the facts surrounding these entities in the KG. Furthermore, ExCut reuses the explanations to enhance the clustering quality.

Technical and experimental details can be found in ExCut's Technical Report: https://mpi-inf.mpg.de/~gadelrab/downloads/ExCut/ExCut_TR.pdf

Folder [envs] contain dump from the required dependencies using pip and conda. We provide two dumps excut_env.yml with Tensorflow CPU usage and excut-gpu_env.yml with Tensorflow-gpu

1. Install env

   conda env create -f excut_env.yml

   or if GPU is required

   conda env create -f excut-gpu_env.yml

2. Activate the env (if it is not the default) before running the code:

conda activate <env>

A triple store with sparql endpoint is required. For that we used Virtouso 7.2 Community. Easy way to run Virtouso is using docker container as provided in docker-compose.yml as follows:

1. Edit data-volume location in docker-compose.yml file (Optional: if you would like to persist the KGs)
2. Run command: docker compose up

Alternatively, The tool can be downloaded from Virtouso 7.2 Community. Then 'SPARQL_UPDATE' permission should be assigned to the SPARQL endpoint. Details can be found here!

ExCut supports Multicut algorithm which is implemented in C++ and hosted as a binary inside excut/excut/clustering/multicut/ folder. If it will be used the correct permissions, should be granted by running

chomd +x excut/excut/clustering/multicut/find-clustering

Note: this binary might not work under macos

Run the tests via

pytest ./tests

cli/main.py file is the main entrance of the explainable clustering approach. Files run_yago.sh shows and example code to run:

Parameters are:

usage: python -m cli.main.py [-h] [-t TARGET_ENTITIES] [-kg KG] [-o OUTPUT_FOLDER] [-steps]
               [-itrs MAX_ITERATIONS] [-e EMBEDDING_DIR] [-Skg]
               [-en ENCODING_DICT_DIR] [-ed EMBEDDING_ADAPTER]
               [-em EMBEDDING_METHOD] [-host HOST] [-index INDEX] [-index_d]
               [-id KG_IDENTIFIER] [-dp DATA_PREFIX] [-dsafe]
               [-q OBJECTIVE_QUALITY] [-expl_cc EXPL_C_COVERAGE]
               [-pr_q PREDICTION_MIN_Q] [-us UPDATE_STRATEGY]
               [-um UPDATE_MODE] [-ud UPDATE_DATA_MODE]
               [-uc UPDATE_CONTEXT_DEPTH] [-ucf CONTEXT_FILEPATH]
               [-uh UPDATE_TRIPLES_HISTORY] [-ulr UPDATE_LEARNING_RATE]
               [-c CLUSTERING_METHOD] [-k NUMBER_OF_CLUSTERS]
               [-cd CLUSTERING_DISTANCE] [-cp CUT_PROB] [-comm COMMENT]
               [-rs SEED] [-ll MAX_LENGTH] [-ls LANGUAGE_STRUCTURE]

optional arguments:
  -h, --help            show this help message and exit
  -t TARGET_ENTITIES, --target_entities TARGET_ENTITIES
                        Target entities file
  -kg KG, --kg KG       Triple format file <s> <p> <o>
  -o OUTPUT_FOLDER, --output_folder OUTPUT_FOLDER
                        Folder to write output to
  -steps, --save_steps  Save intermediate results
  -itrs MAX_ITERATIONS, --max_iterations MAX_ITERATIONS
                        Maximum iterations
  -e EMBEDDING_DIR, --embedding_dir EMBEDDING_DIR
                        Folder of initial embedding
  -Skg, --sub_kg        Only use subset of the KG to train the base embedding
  -en ENCODING_DICT_DIR, --encoding_dict_dir ENCODING_DICT_DIR
                        Folder containing the encoding of the KG
  -ed EMBEDDING_ADAPTER, --embedding_adapter EMBEDDING_ADAPTER
                        Adapter used for embedding
  -em EMBEDDING_METHOD, --embedding_method EMBEDDING_METHOD
                        Embedding method
  -host HOST, --host HOST
                        SPARQL endpoint host and ip host_ip:port
  -index INDEX, --index INDEX
                        Index input KG (memory | remote)
  -index_d, --drop_index
                        Drop old index
  -id KG_IDENTIFIER, --kg_identifier KG_IDENTIFIER
                        KG identifier url , default
                        http://exp-<start_time>.org
  -dp DATA_PREFIX, --data_prefix DATA_PREFIX
                        Data prefix
  -dsafe, --data_safe_urls
                        Fix the urls (id) of the entities
  -q OBJECTIVE_QUALITY, --objective_quality OBJECTIVE_QUALITY
                        Object quality function
  -expl_cc EXPL_C_COVERAGE, --expl_c_coverage EXPL_C_COVERAGE
                        Minimum per cluster explanation coverage ratio
  -pr_q PREDICTION_MIN_Q, --prediction_min_q PREDICTION_MIN_Q
                        Minimum prediction quality
  -us UPDATE_STRATEGY, --update_strategy UPDATE_STRATEGY
                        Strategy for update
  -um UPDATE_MODE, --update_mode UPDATE_MODE
                        Embedding Update Mode
  -ud UPDATE_DATA_MODE, --update_data_mode UPDATE_DATA_MODE
                        Embedding Adaptation Data Mode
  -uc UPDATE_CONTEXT_DEPTH, --update_context_depth UPDATE_CONTEXT_DEPTH
                        The depth of the Subgraph surrounding target entities
  -ucf CONTEXT_FILEPATH, --context_filepath CONTEXT_FILEPATH
                        File with context triples for the target entities
  -uh UPDATE_TRIPLES_HISTORY, --update_triples_history UPDATE_TRIPLES_HISTORY
                        Number iterations feedback triples to considered in
                        the progressive update
  -ulr UPDATE_LEARNING_RATE, --update_learning_rate UPDATE_LEARNING_RATE
                        Update Learning Rate
  -c CLUSTERING_METHOD, --clustering_method CLUSTERING_METHOD
                        Clustering Method
  -k NUMBER_OF_CLUSTERS, --number_of_clusters NUMBER_OF_CLUSTERS
                        Number of clusters
  -cd CLUSTERING_DISTANCE, --clustering_distance CLUSTERING_DISTANCE
                        Clustering Distance Metric
  -cp CUT_PROB, --cut_prob CUT_PROB
                        Cutting Probability
  -comm COMMENT, --comment COMMENT
                        just simple comment to be stored
  -rs SEED, --seed SEED
                        Randomization Seed for experiments
  -ll MAX_LENGTH, --max_length MAX_LENGTH
                        maximum length of description
  -ls LANGUAGE_STRUCTURE, --language_structure LANGUAGE_STRUCTURE
                        Structure of the learned description

This explains code in example file: examples/simple_clustering_pipeline.py

1. Load the KG triples :

   from excut.kg.kg_triples_source import load_from_file
   
   kg_triples=load_from_file('<file_path>')

   Note: a) prefix is required if the data does not has valid URIs; b) when loading Yago data safe_url argument should be set to True as Yago URIs have special characters.

2. Index KG triples : Current Explanation mining requires the KG triples to be indexed eitehr in remote sparql endpoint (eg. Virtouso) or in memory.

   from excut.kg.kg_indexing import Indexer
   
   kg_indexer=Indexer(store='remote', endpoint='<vos endpoint url>', identifier='http://yago-expr.org')
   kg_indexer.index_triples(kg_triples, drop_old=False)

   the KG identifier is a url-like name for the graph (http://yago-expr.org) it is required in the mining process.

3. Loading clustered Entities;

   After clustering, the results should be loaded into one of the implemenations of EntityLabelsInterface in Module clustering.target_entities. Loading methods are provided in the module.

   Example:

   from excut.clustering.target_entities import load_from_file
   
   clustering_results_as_triples=load_from_file('<file_path>')

4. Explain clusters:

   Example:

   #a) The explaining engine requires creating two interfaces: interface to index labels,
   #  and interfac to query the whole kg triples and the labels as well.
    
   from excut.kg.kg_indexing import Indexer
   from excut.kg.kg_query_interface_extended import EndPointKGQueryInterfaceExtended
   
   query_interface=EndPointKGQueryInterfaceExtended( sparql_endpoint='<vos endpoint url>', 
                   identifiers=['http://yago-expr.org', 'http://yago-expr.org.extension'],
                   labels_identifier='http://yago-expr.org.labels'
                   )
   
   #b) Create Explaning Engine 
   
   from excut.explanations_mining.explaining_engines_extended import PathBasedClustersExplainerExtended
   explaining_engine= PathBasedClustersExplainerExtended(query_interface,
                                                   quality_method=objective_measure, min_coverage=0.5)
   
   #c) explain the clusters
   explanations_dict = explaining_engine.explain(clustering_results_as_triples,'<output file path>')
   
   
   #d) compute aggregate quality of the explanations
   import excut.evaluation.explanations_metrics as explm
   #evalaute rules quality
   explm.aggregate_explanations_quality(explanations_dict)

   Note: QueryInterfaceExtended is the interface to the indexed KG triples and the labels of the target entities. It requires as an input the identifiers of the KG to mine over. It is possible that a single KG can be stored in several subgraphs each with a different identifier. Then all should be listed as shown in the above code,

   We recommend using a fresh identfier for the entities-labels different than the used for the original KG, e.g. http://yago-expr.org.labels (where .labels was appended).

These are some important modules while developing in ExCut:

• Package explanations_mining
  • Module .explanations_quality_functions: contains quality functions used to score explanations.
• Package evalaution
  • Module .clustering_metrics Traditional Clustering quality
  • Module .explanations_metrics Aggregating Explanations quality
  • Module .eval_utils Some useful scripts for evaluation such as exporting to csv and ploting.

Please Contact gadelrab [at] mpi-inf.mpg.de for further questions

ExCut is open-sourced under the Apache 2.0 license. See the LICENSE file for details.