In expert systems, hierarchical reasoning can provide better accuracy and understandability. Here, we develop a method of learning hierarchical knowledge from a case library, in which each training instance is described by low level features and high level concepts (e.g., manifestations and diseases) but not by intermediate concepts (e.g., disease states). Learning intermediate knowledge involv...

One of the main challenges human space exploration is development artificial ecosystems, which can be used as life support systems (LSSs) to enable long-duration missions. In an open LSS, no food generation or waste treatment provided in space, and supplies from Earth are necessary. According Figure 1, considering approximate metabolic consumables hygiene water well number crewmembers [1], a hu...

OBJECTIVES To quantify semantic inconsistency in UMLS concepts from the perspective of their hierarchical relations and to show through examples how semantically-inconsistent concepts can help reveal erroneous synonymy relations. METHODS Inconsistency is defined in reference to concepts from the UMLS Metathesaurus. Consistency is evaluated by comparing the semantic groups of the two concepts ...

In this paper, we propose a hierarchical spatial concept formation method based on the Bayesian generative model with multimodal information e.g., vision, position and word information. Since humans have the ability to select an appropriate level of abstraction according to the situation and describe their position linguistically, e.g., "I am in my home" and "I am in front of the table," a hier...

The structure of people's conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, superordinate concepts (vegetable) are assumed to reside at a higher level than basic-level concepts (carrot). A feature-based attractor network with a single layer of semantic features developed representations of both basic-level and superordi...

OBJECTIVES To contrast the coverage of diseases between the Disease Ontology (DO) and SNOMED CT, and to compare the hierarchical structure of the two ontologies. METHODS We establish a reference list of mappings. We characterize unmapped concepts in DO semantically and structurally. Finally, we compare the hierarchical structure between the two ontologies. RESULTS Overall, 4478 (65%) the 69...