Information Extraction & Knowledge Inference

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Related lectures (32)

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Knowledge Inference: Entity Disambiguation and Graph Embeddings

Explores entity disambiguation, graph embeddings, scoring functions, and learning methods.

Information Extraction: Methods and Applications

Explores methods for information extraction, including traditional and embedding-based approaches, supervised learning, distant supervision, and taxonomy induction.

Entity Disambiguation

Explores Entity Disambiguation, linking text mentions to a knowledge base, coherence in entity graphs, and Personalized PageRank.

Matrix Factorization: Information Extraction

Explores matrix factorization for information extraction, Bayesian ranking, and relation embeddings.

Semantic Web & Information Extraction

Explores Semantic Web, ontologies, information extraction, key phrases, named entities, and knowledge bases.

Knowledge Inference

Explores knowledge inference, embedding techniques, and schema matching in data integration.

Entity Disambiguation and Link Prediction

Explores entity disambiguation, linking text to knowledge bases, and link prediction in knowledge graphs with examples from Wikipedia.

Knowledge Modeling: Introduction

Explores manual creation of knowledge bases and challenges in knowledge extraction from text.

Knowledge Representation: Introduction

Covers knowledge representation in AI, logical inference, and applications in various domains.

Information Extraction: Algorithms and Techniques

Explores algorithms and techniques for information extraction, including Viterbi algorithm, named entities recognition, and distant supervision.