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Creators/Authors contains: "Balasubramani, Booma"

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  1. ; ; ; ; (, SIGSPATIAL '20: 28th International Conference on Advances in Geographic Information Systems)
    null (Ed.)
    Full Text Available 
  2. ; ; ; ; ; ; ; (, Proceedings of the 13th International Workshop on Ontology Matching co-located with the 17th International Semantic Web Conference)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; (, Proceedings of the 2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS@SIGSPATIAL)
    Cities are actively creating open data portals to enable predictive analytics of urban data. However, the large number of observable patterns that can be extracted by techniques such as Association Rule Mining (ARM) makes the task of sifting through patterns a tedious and time-consuming task. In this paper, we explore the use of domain ontologies to: (i) filter and prune rules that are specific variations of a more general concept in the ontology, and (ii) replace specific rules by a single "general" rule, with the intent to downsize the number of general rules while keeping the semantics of the larger generated set. We show how the combination of several methods reduces significantly the number of rules thus effectively allowing city administrators to use open data to understand patterns, use patterns for decision-making, and better direct limited government resources. 
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    Full Text Available 
  4. ; ; ; ; ; ; ; (, 11th International Workshop on Ontology Matching co-located with the 15th International Semantic Web Conference, CEUR Workshop Proceedings)
    AgreementMakerLight (AML) is an automated ontology matching system based primarily on element-level matching and on the use of external resources as background knowledge. This paper describes its configuration for the OAEI 2016 competition and discusses its results. For this OAEI edition, we tackled instance matching for the first time, thus expanding the coverage of AML to all types of ontology matching tasks. We also explored OBO logical definitions to match ontologies for the first time in the OAEI. AML was the top performing system in five tracks (including the Instance and instance-based Process Model tracks) and one of the top performing systems in three others (including the novel Disease and Phenotype track, in which it was one of three prize recipients). 
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    Accepted Manuscript Full Text Available