{"id":774931,"date":"2021-09-15T09:00:32","date_gmt":"2021-09-15T16:00:32","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=774931"},"modified":"2021-09-15T09:00:32","modified_gmt":"2021-09-15T16:00:32","slug":"discovering-order-in-chaos-using-a-heuristic-ontology-to-derive-spatio-temporal-sequences-for-cadastral-data","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/discovering-order-in-chaos-using-a-heuristic-ontology-to-derive-spatio-temporal-sequences-for-cadastral-data\/","title":{"rendered":"Discovering Order in Chaos: Using a Heuristic Ontology to Derive Spatio-Temporal Sequences for Cadastral Data"},"content":{"rendered":"<p>A heuristic ontology is an ontology that describes concepts using heuristics (\u2018rules of thumb\u2019 or guidelines) rather than axioms as in a classical ontology. The heuristics can be used to establish whether a given instance is a member of the concept defined by the heuristics in a data-driven manner. The approach may be particularly useful if data is poor, irregular or incomplete. A heuristic ontology was used to determine whether given collections of land parcels are valid spatio-temporal sequences and thus automatically derive sequence data that would otherwise have to be maintained manually, achieving levels of precision of 94% and recall of 67%.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A heuristic ontology is an ontology that describes concepts using heuristics (\u2018rules of thumb\u2019 or guidelines) rather than axioms as in a classical ontology. The heuristics can be used to establish whether a given instance is a member of the concept defined by the heuristics in a data-driven manner. The approach may be particularly useful [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Roberto Santos","user_id":"40750"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"2","msr_journal":"Spatial Cognition & Computation","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"115","msr_page_range_end":"141","msr_series":"","msr_volume":"15","msr_copyright":"Kristin Stock, Didier Leibovici, Luciene Delazari & Roberto Santos (2015) Discovering Order in Chaos: Using a Heuristic Ontology to Derive Spatio-Temporal Sequences for Cadastral Data, Spatial Cognition & Computation, 15:2, 115-141, DOI: 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