Knowledge Inference: Entity Disambiguation and Graph Embeddings

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Information Extraction & Knowledge Inference

Explores information extraction, knowledge inference, taxonomy induction, and entity disambiguation.

Knowledge Inference for Graphs

Explores knowledge inference for graphs, discussing label propagation, optimization objectives, and probabilistic behavior.

Entity Disambiguation

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

Knowledge Modeling: Introduction

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

Knowledge Inference

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

Entity & Information Extraction

Explores knowledge extraction from text, covering key concepts like keyphrase extraction and named entity recognition.

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 and Link Prediction

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

Semantic Web & Information Extraction

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

Knowledge Representation: Introduction

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