Neli Zlatareva

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– This paper presents our approach of introducing Machine Learning from an AI perspective. We present an AI course with a Machine Learning component. We also discuss some of the examples and projects we used to introduce various search algorithms and show how they can be extended into projects that incorporate ML techniques. The N-puzzle problem is used as(More)
This paper presents a methodology for testing general non-monotonic knowledge bases for logical and semantic inconsistencies. It extends the CTMS-based verification framework introduced in our previous work with an additional integrity test. This test aims to ensure that a logically consistent non-monotonic knowledge base is also free of semantic(More)
In this paper we present work on a project funded by the National Science Foundation with a goal of unifying the Artificial Intelligence (AI) course around the theme of machine learning. Our work involves the development and testing of an adaptable framework for the presentation of core AI topics that emphasizes the relationship between AI and computer(More)
Ensuring the consistency and completeness of Semantic Web ontologies is practically impossible, because of their scale and highly dynamic nature. Many web applications, therefore, must deal with vague, incomplete and even inconsistent knowledge. Rules were shown to be very effective in processing such knowledge, and future web services are expected to(More)
Ontology alignment is regarded as one of the core tasks in many Web services. It is concerned with finding the correspondences between separate ontologies by identifying concepts with the same or similar semantics in order to resolve semantic heterogeneity between them. Existing ontology alignment techniques are tailored towards today's ontology languages,(More)