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The use of semantics in computer science is a recent trend thatprovides potential benefits. Ontologies are essential semantic Webcomponents that organize knowledge in a structural classificationformat. The use of ontology was embraced in many scientificfields such as pharmacology, biology, and linguistics. Learningnew knowledge from different language textbooks require bothlearning the language and...
Deepening prose comprehension involves understanding the relation among the prose concepts and reading external references. In this paper, we propose an interesting system which mimics the human reading process. Given a prose, the system discovers the relevant parts from relevant references that connect and illuminate a set of learnable concepts from the prose by adding new familiarity meaningful...
Inferring Bloom's Taxonomy among knowledge units is important and challenging. This paper proposes a novel method that can identify the revised Bloom's Taxonomy levels among knowledge units in the semantic cognitive graph (SCG) by using a graph triangularity. The method determines significant relationships among knowledge units by utilizing triangularity of knowledge units in the computer science...
Semantic analysis among knowledge units in the text is a very interesting problem in numerous applications. Beside the semantic relationships expressed in the text, relationships are also encoded in knowledge structures in our brains. However, the relationships among knowledge units are highly sophisticated and require a human judgment. In this paper, we propose a Graph-Tringluarity-based system for...
Most of the current text understanding techniques are based on ontology engine and external knowledge resources to reach to a deep comprehension. In this paper, we propose a computerized text comprehension technique for a given text. This technique can accommodate a deep text comprehension by an iterative reading of reference texts related to the given text using ontology engine. Performance analysis...
In this paper we propose a twitter sentiment analytics that mines for opinion polarity about a given topic. Most of current semantic sentiment analytics depends on polarity lexicons. However, many key tone words are frequently bipolar. In this paper we demonstrate a technique which can accommodate the bipolarity of tone words by context sensitive tone lexicon learning mechanism where the context is...
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