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Global minimal diagnosis algorithm for repair incoherent mappings in ontology alignment
Husein I.G.a, Sitohang B.b, Akbar S.b, Azizah F.N.b
a School of Applied Science Universitas Telkom, Bandung, Indonesia
b School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 IEEE.Incoherent alignment has been a major issue on the ontology matching field. Since 2012, OAEI has its own assessment to evaluate incoherent mapping produced by ontology matching systems. Mapping repair process could automatically resolve the incoherent to coherent situation by removing some unwanted mappings from alignment. Removing unwanted mappings is called diagnosis. Diagnosis process should be done as little as possible to minimize the impact of changes in input alignment. Implementing global minimal technique minimizes the number of removed mappings and total confidence value of removed mappings as well. These both minimal focus will be the evaluator view in this research. Search algorithm has been widely used in solving various path search issues. This research implemented additional procedures into Uniform Cost Search to support minimal diagnosis optimally. The experiments used small-scale ontology with understandable concepts, that was conference track. The results of experiments show that adding reordering priority queue and interaction group procedures meets the objective of two minimal focus. This research improved Uniform Cost Search algorithm to minimally remove unwanted mappings and produce total confidence value of removed mappings.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Confidence values,Impact of changes,Minimal diagnosis,Ontology alignment,Ontology matching,Search Algorithms,Uniform cost search algorithms,Uniform cost searches[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]incoherent alignment,minimal diagnosis,ontology matching,removed mappings,two minimal focus[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/SNPD.2018.8441046[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]