is#information_explicitation
supertype: information_precising__informationprecising
subtype: representing_information_in_a_formal_or_semi_formal_way
subtype: knowledge_representation__knowledgerepresentation__representing_knowledge__KR__knowledge_modelling representing information in a more or less formal way
subtype: knowledge_normalization representing knowledge in a precise, organized and scalable manner; this implies reducing the number of non-automatically comparable ways information is or can be written, and increasing the number of relations between objects (especially common/important relations such as generalization relations, partOf relations and case relations)
subtype: use_of_a_normalizing_KRL
subtype: re-use_of_a_top_level_or_large_ontology
subtype: following_of_an_ontological_principle
subtype: following_of_a_principle_of_the_Ontoclean_methodology
subtype: following_of_a_category_naming_principle lexical normalization involves following object naming rules such as "using English singular nouns or nominal expressions" and "avoiding the Intercap style"
subtype: following_of_the_InterCap_style_for_naming_categories
subtype: following_of_an_underscore_based_style_for_naming_categories
subtype: use_of_nouns_or_nominal_forms_for_naming_categories
subtype: use_of_singular_nouns_or_nominal_forms_for_naming_categories
subtype: following_of_a_phrasing_principle_for_category_annotations
subtype: following_of_a_knowledge_organization_principle Structural and ontological normalization involves following rules such as "when introducing an object into an ontology, relate it to all its already represented direct generalizations, specializations, components and containers", "use subtypeOf relations instead of or in addition to instanceOf relations when both cases are possible", "avoid the use of non binary relations" and "do not represent processes via relations"
subtype: use_of_a_graph-oriented-reading_convention
subtype: limiting_the_number_of_relation_types
subtype: following_of_an_ontological_principle
subtype: representing_knowledge_in_a_concise/organized/precise/readable_way
subtype: representing_knowledge_in_a_concise_way
subtype: representing_knowledge_in_an_organized_way setting or presenting many relations between categories or statements
subtype: increasing_the_number_of_explicit_conceptual_relations_between_conceptual_objects
subtype: increasing_the_number_of_explicit_conceptual_relations_between_relation_types
subtype: increasing_the_number_of_explicit_conceptual_relations_between_concept_types
subtype: increasing_the_number_of_explicit_conceptual_relations_between_objects_from_different_users
subtype: representing_knowledge_in_a_readable_way
subtype: representing_knowledge_in_a_precise_way precise or explicit
subtype: knowledge_modelling/classification/extraction__knowledgemodelling/classification/extraction__knowledge_acquisition__knowledgeacquisition__KA_task__KA this is "knowledge acquisition" in its restricted sense; in its broader sense, it is equivalent to "knowledge management"
subtype: KA_from_people
subtype: KA_from_data
subtype: semi_automatic_KA_from_data__knowledge_discovery__knowledgediscovery__data_mining
subtype: semi_automatic_KA_from_data_by_classification
subtype: concept_clustering_from_data
subtype: knowledge_extraction_from_documents
subtype: semantic_web_mining
subtype: knowledge-oriented_NLP
subtype: CG_extraction_by_NLP
subtype: ontology_extraction_from_documents
subtype: terminological_analysis
subtype: document_structure_analysis_or_discovery
subtype: knowledge_extraction_from_databases__knowledge_discovery_in_databases__KDD
subtype: FCA_based_KDD
subtype: classification
subtype: conceptual_clustering__concept_clusterization__conceptclusterization it can be used both for KA and IR, from knowledge or data
subtype: conceptual_clustering_via_a_generalization_hierarchy
subtype: conceptual_clustering_via_a_category_generalization_hierarchy
subtype: FCA_based_conceptual_clustering
subtype: FCA_attribute_exploration FCA technique addressing the problem of a context where the object set is not completely known a priori, or too large to be listed
subtype: FCA_concept_exploration FCA technique addressing the problem of a context where both the object set and the attribute set are not completely known a priori, or too large to be listed
subtype: type_classification
subtype: instance_classification__instance_learning assignement of instances to types of concepts/relations
subtype: conceptual_clustering_via_a_CG_generalization_hierarchy
subtype: conceptual_clustering_from_data
subtype: conceptual_clustering_from_database
subtype: conceptual_clustering_from_documents
subtype: conceptual_clustering_from_emails
subtype: classification_by_semantic_grids
subtype: language/structure_specific_knowledge_representation
subtype: CG_based_KR
subtype: methodology_specific_knowledge_representation_or_modelling
subtype: task_related_to_the_creation/update_of_the_KB_conceptual_model
subtype: creation/update_of_the_KB_conceptual_model
subtype: combination_and_instantiation_of_generic_task_models
subtype: selection_and_adaptation_of_domain_ontologies
subtype: making_an_informal_sentence_less_contextual
subtype: information_normalization__informationnormalization
subtype: data_normalization
subtype: representing_information_in_a_formal_or_semi_formal_way
15 statements are about indirect instances of information_explicitation: graph1_on_article, graph8_on_article, graph9_on_article, graph12_on_article, graph13_on_article, graph14_on_article, graph15_on_article, graph18_on_article, graph1_on_PhD_thesis, graph1_on_book, graph21_on_article, graph24_on_article, graph2_on_PhD_thesis, graph25_on_article, graph34_on_article click here to display them or click here for a search form or here to add a statement