Explores knowledge inference, embedding techniques, and schema matching in data integration.
Introduces the basics of information retrieval, covering text-based retrieval, document features, similarity functions, and the difference between Boolean and ranked retrieval.
Covers the basic concepts of text-based information retrieval and how documents are indexed and retrieved based on user queries.
Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.
Covers association rules mining, focusing on Apriori and FP-growth algorithms to find frequent itemsets and extract rules efficiently.
Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.
Covers probabilistic retrieval models, evaluation metrics, query likelihood, user relevance feedback, and query expansion.
Explains the evaluation of information retrieval models, including recall, precision, F-Measure, and the precision/recall tradeoff.
