Neli Zlatareva

Learn More
Ž. Non-monotonic Knowledge-Based Systems KBSs must undergo quality assurance procedures for the following two Ž. Ž. reasons: i belief revision if such is provided cannot always guarantee the structural correctness of the knowledge base, Ž. and in certain cases may introduce new semantic errors in the revised theory; ii non-monotonic theories may have(More)
– 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)
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)
Ontologies are the backbone of the emerging Semantic Web, which is envisioned to dramatically improve current web services by extending them with intelligent capabilities such as reasoning and context-awareness. They define a shared vocabulary of common domains accessible to both, humans and computers, and support various types of information management(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)