Semantic Web technologies have in recent years started to also find their way into the world of commercial enterprises. Enterprise ontologies can be used as a basis for determining the relevance of information with respect to the enterprise. The interests of individuals can be expressed by means of the enterprise ontology. The main contribution of our approach is the integration of point set di...

Ontology matching is a problem that is rapidly reaching maturity especially in the context of the emergent semantic web. Semantic matching is the detection of specific correspondences between concepts in two ontologies including but not limited to ≡, v and w. Many semantic matching approaches today use some form of background knowledge to aid them in discovering correspondences. This paper pres...

Introduction The discovery of semantic relationships such as subsumption and dis-jointness is still a challenge in ontology matching [6]. Existing methods use logical reasoning over computed equivalence relationships or machine learning based on lexical and structural features of ontology elements [7, 3]. While these methods deliver good results for some cases, they are limited to the informati...

We think of Match as an operator which takes two graph-like structures and produces a mapping between semantically related nodes. We concentrate on classifications with tree structures. In semantic matching, correspondences are discovered by translating the natural language labels of nodes into propositional formulas, and by codifying matching into a propositional unsatisfiability problem. We d...

We view match as an operator that takes two graph-like structures (e.g., XML schemas) and produces a mapping between the nodes of these graphs that correspond semantically to each other. Semantic schema matching is based on the two ideas: (i) we discover mappings by computing semantic relations (e.g., equivalence, more general); (ii) we determine semantic relations by analyzing the meaning (con...

We think of Match as an operator which takes two graph-like structures and produces a mapping between semantically related nodes. The matching process is essentially divided into two steps: element level and structure level. Element level matchers consider only labels of nodes, while structure level matchers start from this information to consider the full graph. In this paper we present variou...

Many of automated computer programming feedback is generated based on static template matching that need to be provided by the experts. This research focusing developing an online semantic error using dynamic models students’ correct answers submission. Currently, there a lack model due their complexity and varies in terms structure. To solve formulation templates, new front rear n-gram sequenc...

Lexical and semantic matching capture different successful approaches to text retrieval the fusion of their results has proven be more effective robust than either alone. Prior work performs hybrid by conducting lexical using systems (e.g., Lucene Faiss, respectively) then fusing model outputs. In contrast, our integrates representations with dense densifying high-dimensional into what we call ...