graphai.core.ontology package
- class graphai.core.ontology.OntologyData(test_mode=False, **kwargs)
Bases:
object- load_data()
- load_ontology_concept_names()
- load_ontology_categories()
- load_non_ontology_concept_names()
- load_concept_concept_graphscore()
- load_category_category()
- load_category_concept()
- load_anchor_page_dict()
Loads category to anchor page list dictionary using the direct category-anchor table and the category child-parent relations table. :returns: None
- compute_symmetric_concept_concept_matrix()
Loads the concept-concept matrix and creates its index dictionary :returns: None
- compute_precalculated_similarity_matrices()
Precomputes the matrices and index dictionaries for similarities between concepts, clusters, and categories. :returns: None
- get_concept_concept_similarity(concept_1_id, concept_2_id)
Returns the similarity score between two concepts :param concept_1_id: ID of concept 1 :param concept_2_id: ID of concept 2
- Returns:
Similarity score
- get_concept_cluster_similarity(concept_id, cluster_id, avg='linear')
Returns the similarity score between a concept and a cluster :param concept_id: ID of concept :param cluster_id: ID of cluster :param avg: Averaging method
- Returns:
Similarity score
- get_cluster_cluster_similarity(cluster_1_id, cluster_2_id, avg='linear')
Returns the similarity score between two clusters :param cluster_1_id: ID of cluster 1 :param cluster_2_id: ID of cluster 2 :param avg: Averaging method
- Returns:
Similarity score
- get_concept_category_similarity(concept_id, category_id, avg='linear', coeffs=(1, 4))
Returns the similarity score between a concept and a category :param concept_id: ID of cluster :param category_id: ID of category :param avg: Averaging method :param coeffs: Coefficients for anchors and concepts
- Returns:
Similarity score
- get_cluster_category_similarity(cluster_id, category_id, avg='linear', coeffs=(1, 4))
Returns the similarity score between a cluster and a category :param cluster_id: ID of cluster :param category_id: ID of category :param avg: Averaging method :param coeffs: Coefficients for anchors and concepts
- Returns:
Similarity score
- get_category_category_similarity(category_1_id, category_2_id, avg='linear', coeffs=(1, 4))
Returns the similarity score between two categories :param category_1_id: ID of category 1 :param category_2_id: ID of category 2 :param avg: Averaging method :param coeffs: Coefficients for anchors and concepts
- Returns:
Similarity score
- get_concept_closest_concept(concept_id, top_n=1)
Finds the closest concept to a given concept :param concept_id: Concept ID :param top_n: Number of top concepts to return
- Returns:
Top concepts and their scores
- get_concept_closest_concept_embedding(concept_id, top_n=1)
- get_concept_closest_category(concept_id, avg='log', coeffs=(1, 10), top_n=1, use_depth_3=False, return_clusters=None, adaptive_threshold=None)
Finds the closest category to a given concept :param concept_id: Concept ID :param avg: Averaging method. Options are (‘linear’, ‘log’, and ‘none’) :param coeffs: Coefficients for averaging of the scores anchors and concepts :param top_n: Number of top categories to return :param use_depth_3: Whether to go through depth-3 or directly use depth-4 :param return_clusters: Number of top clusters to return for each top category. If None, clusters are
not returned.
- Returns:
Top categories, their scores, parent depth-3 category if use_depth_3==True, and top clusters of each top category if return_clusters is not None.
- get_concept_category_closest_embedding(concept_id, avg='log', coeffs=(1, 10), top_n=5, return_clusters=None)
- get_cluster_closest_category(cluster_id, avg='log', coeffs=(1, 10), top_n=1, use_depth_3=False)
Finds the closest category to a given cluster :param cluster_id: Cluster ID :param avg: Averaging method. Options are (‘linear’, ‘log’, and ‘none’) :param coeffs: Coefficients for averaging of the scores anchors and concepts :param top_n: Number of top categories to return :param use_depth_3: Whether to go through depth-3 or directly use depth-4
- Returns:
Top categories, their scores, and parent depth-3 category if use_depth_3==True.
- get_custom_cluster_closest_category(concept_ids, avg='log', coeffs=(1, 10), top_n=1, use_depth_3=False)
Finds the closest category to a custom cluster, provided as a list of concepts :param concept_ids: List of concept IDs :param avg: Averaging method. Options are (‘linear’, ‘log’, and ‘none’) :param coeffs: Coefficients for averaging of the scores anchors and concepts :param top_n: Number of top categories to return :param use_depth_3: Whether to go through depth-3 or directly use depth-4
- Returns:
Top categories, their scores, and parent depth-3 category if use_depth_3==True.
- get_cluster_closest_category_embedding(cluster_id, avg='log', coeffs=(1, 10), top_n=1)
Finds the closest category to a custom cluster, provided as a list of concepts :param concept_ids: List of concept IDs :param avg: Averaging method. Options are (‘linear’, ‘log’, and ‘none’) :param coeffs: Coefficients for averaging of the scores anchors and concepts :param top_n: Number of top categories to return :param use_depth_3: Whether to go through depth-3 or directly use depth-4
- Returns:
Top categories, their scores, and parent depth-3 category if use_depth_3==True.
- get_custom_cluster_closest_category_embedding(concept_ids, avg='log', coeffs=(1, 10), top_n=1)
Finds the closest category to a custom cluster, provided as a list of concepts :param concept_ids: List of concept IDs :param avg: Averaging method. Options are (‘linear’, ‘log’, and ‘none’) :param coeffs: Coefficients for averaging of the scores anchors and concepts :param top_n: Number of top categories to return :param use_depth_3: Whether to go through depth-3 or directly use depth-4
- Returns:
Top categories, their scores, and parent depth-3 category if use_depth_3==True.
- get_category_closest_category(category_id, avg='log', coeffs=(1, 10), top_n=1, use_depth_3=False)
Finds the closest category to a given category. As with the category-category similarity method, the similarity is composed of between-anchor and between-concept similarity, and there is no anchor-concept crossover. :param category_id: Category ID :param avg: Averaging method. Options are (‘linear’, ‘log’, and ‘none’) :param coeffs: Coefficients for averaging of the scores anchors and concepts :param top_n: Number of top categories to return :param use_depth_3: Whether to go through depth-3 or directly use depth-4
- Returns:
Top categories, their scores, and parent depth-3 category if use_depth_3==True.
- get_ontology_concept_names_table(concepts_to_keep=None)
- get_ontology_category_names()
- get_ontology_category_info(cat_id)
- get_non_ontology_concept_names()
- get_concept_concept_graphscore_table(concepts_to_keep=None)
- get_category_to_category()
- get_category_parent(child_id)
- get_category_branch(category_id)
- get_category_children(parent_id)
- get_cluster_parent(cluster_id)
- get_cluster_children(cluster_id)
- get_concept_parent_category(concept_id)
- get_concept_parent_cluster(concept_id)
- get_category_cluster_list(cat_id)
- get_category_concept_list(cat_id)
- get_cluster_concept_list(cluster_id)
- get_category_concept_table(concepts_to_keep=None)
- get_category_cluster_table()
- get_category_anchor_pages(category_id)
- get_cluster_concepts(cluster_id)
- get_concept_name(concept_id)
- get_concept_names_list(concept_ids)
- get_test_concept_names()
- get_test_category_concept()
- get_test_cluster_concept()
- get_root_category()
- generate_tree_structure(start=None)
- generate_category_concept_dict()
- graphai.core.ontology.recompute_clusters(ontology_data_obj, n_clusters, min_n)
- graphai.core.ontology.get_concept_category_closest(ontology_data_obj, concept_id, avg, coeffs, top_n, use_depth_3, return_clusters, use_embeddings=False)
- graphai.core.ontology.get_cluster_category_closest(ontology_data_obj, cluster_id, avg, coeffs, top_n, use_depth_3, use_embeddings=False)
- graphai.core.ontology.get_concept_concept_closest(ontology_data_obj, concept_id, top_n, use_embeddings=False)
- graphai.core.ontology.break_up_cluster(ontology_data_obj, cluster_id, n_clusters)
- graphai.core.ontology.get_openalex_nearest(category_id=None, topic_id=None)