scholarly journals Using Semantics for morphological Descriptions in Morph•D•Base

2018 ◽  
Vol 2 ◽  
pp. e25535
Author(s):  
Christian Köhler ◽  
Roman Baum ◽  
Peter Grobe ◽  
Sandra Meid ◽  
Björn Quast ◽  
...  

Providing data in a semantically structured format has become the gold standard in data science. However, a significant amount of data is still provided as unstructured text - either because it is legacy data or because adequate tools for storing and disseminating data in a semantically structured format are still missing. We have developed a description module for Morph∙D∙Base, a semantic knowledge base for taxonomic and morphologic data, that enables users to generate highly standardized and formalized descriptions of anatomical entities using free text and ontology-based descriptions. The main organizational backbone of a description in Morph∙D∙Base is a partonomy, to which the user adds all the anatomical entities of the specimen that they want to describe. Each element of this partonomy is an instance of an ontology class and can be further described in two different ways: as semantically enriched free-text description that is annotated with terms from ontologies, and semantically through defined input forms with a wide range of ontology-terms to choose from. To facilitate the integration of the free text into a semantic context, text can be automatically annotated using jAnnotator, a javascript library that uses about 700 ontologies with more than 8.5 million classes of the National Center for Biomedical Ontology (NCBO) bioportal. Users get to choose from suggested class definitions and link them to terms in the text, resulting in a semantic markup of the text. This markup may also include labels of elements that the user already added to the partonomy. Anatomical entities marked in the text can be added to the partonomy as new elements that can subsequently be described semantically using the input forms. Each free text together with its semantic annotations is stored following the W3C Web Annotation Data Model standard (https://www.w3.org/TR/annotation-model). The whole description with the annotated free text and the formalized semantic descriptions for each element of the partonomy are saved in the tuplestore of Morph∙D∙Base. The demonstration is targeted at developers and users of data portals and will give an insight to the semantic Morph∙D∙Base knowledge base (https://proto.morphdbase.de) and jAnnotator (http://git.morphdbase.de/christian/jAnnotator).

2017 ◽  
Vol 62 (2) ◽  
pp. 715-720 ◽  
Author(s):  
K. Regulski

AbstractThe process of knowledge formalization is an essential part of decision support systems development. Creating a technological knowledge base in the field of metallurgy encountered problems in acquisition and codifying reusable computer artifacts based on text documents. The aim of the work was to adapt the algorithms for classification of documents and to develop a method of semantic integration of a created repository. Author used artificial intelligence tools: latent semantic indexing, rough sets, association rules learning and ontologies as a tool for integration. The developed methodology allowed for the creation of semantic knowledge base on the basis of documents in natural language in the field of metallurgy.


2018 ◽  
Vol 7 (4.27) ◽  
pp. 67
Author(s):  
Abdul Syafiq Abdull Sukor ◽  
Ammar Zakaria ◽  
Norasmadi Abdul Rahim ◽  
Rossi Setchi

Activity recognition plays a major role in smart home technologies in providing services to users. One of the approaches to identify activity is through the use of knowledge-driven reasoning. This paper presents a framework of semantic activity recognition, which is used to support smart home systems to identify users’ activities based on the existing context. The framework consists of two main components: a semantic knowledge base and an activity recognition module. The knowledge base is represented using ontology and it is used to provide a semantic understanding of the environment in order to classify users’ patterns of activities. Experimental results show that the proposed approach can support the classification process and accurately infer users’ activities with the accuracy of 90.9%.  


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