is#information_precising__informationprecising
  supertype:  information_indexation__informationindexation
  subtype:  information_explicitation
     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_precising: 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


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