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Lecture
Knowledge Modeling: Introduction
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Related lectures (33)
Entity & Information Extraction
Explores knowledge extraction from text, covering key concepts like keyphrase extraction and named entity recognition.
Text Understanding
Explores Text Understanding, focusing on Named Entities, Information Extraction, and Machine Reading methods.
Entity & Information Extraction
Explores information extraction using classifiers, features, and syntactic analysis.
Semantic Web: Modeling and Ontologies
Explores the Semantic Web, database schemas, XML data model, and ontologies.
Knowledge Inference for Graphs
Explores knowledge inference for graphs, discussing label propagation, optimization objectives, and probabilistic behavior.
Entity Disambiguation and Link Prediction
Explores entity disambiguation, linking text to knowledge bases, and link prediction in knowledge graphs with examples from Wikipedia.
Taxonomy Induction: Learning Concepts and Relationships
Explores taxonomy induction, learning relationships and concepts from documents.
Knowledge Representation: Introduction
Covers knowledge representation in AI, logical inference, and applications in various domains.
Digital Humanities Lab: Practices
Introduces students to digital humanities through practical exercises, such as working on a large ongoing project.
Expert Systems: Backward Chaining
Explores expert systems, backward chaining, and uncertainty through fuzzy logic in practical applications.
Information Extraction & Knowledge Inference
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Explores information extraction, knowledge inference, taxonomy induction, and entity disambiguation.
Knowledge Inference: Entity Disambiguation and Graph Embeddings
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Explores entity disambiguation, graph embeddings, scoring functions, and learning methods.
Information Extraction: Methods and Applications
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Explores methods for information extraction, including traditional and embedding-based approaches, supervised learning, distant supervision, and taxonomy induction.
Semantic Web & Information Extraction
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Explores Semantic Web, ontologies, information extraction, key phrases, named entities, and knowledge bases.
Information Extraction: Algorithms and Techniques
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Explores algorithms and techniques for information extraction, including Viterbi algorithm, named entities recognition, and distant supervision.
Knowledge Inference
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Explores knowledge inference, embedding techniques, and schema matching in data integration.
Matrix Factorization: Linking Text to Knowledge Bases
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Explores distant supervision for linking text to knowledge bases using entity extraction and classifiers.
Entity Disambiguation
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Explores Entity Disambiguation, linking text mentions to a knowledge base, coherence in entity graphs, and Personalized PageRank.
Named Entity Recognition: Applications and Techniques
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Explores Named Entity Recognition, its uses, techniques, and applications in information extraction.
Projects Presentation & Logistics
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Covers the presentation of 4 projects in the course and related logistics.
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