No. 3: Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches
All Papers
Commonsense Reasoning Based on Betweenness and Direction in Distributional Models
PDFTowards High-Level Probabilistic Reasoning with Lifted Inference
PDFCORPP: Commonsense Reasoning and Probabilistic Planning, as Applied to Dialog with a Mobile Robot
PDFDistributional Semantic Features as Semantic Primitives — Or Not
PDFOn Approximate Reasoning Capabilities of Low-Rank Vector Spaces
PDFLearning Distributed Word Representations for Natural Logic Reasoning
PDFProbability Distributions over Structured Spaces
PDFNeural-Symbolic Learning and Reasoning: Contributions and Challenges
PDFTowards A Model Theory for Distributed Representations
PDFTowards Ontologies in Variation
PDFCompositional Vector Space Models for Knowledge Base Inference
PDFTowards Extracting Faithful and Descriptive Representations of Latent Variable Models
PDFEnriching Word Embeddings Using Knowledge Graph for Semantic Tagging in Conversational Dialog Systems
PDFLatent Predicate Networks: Concept Learning with Probabilistic Context-Sensitive Grammars
PDFDimensionality Reduction via Program Induction
PDFNeural Relational Learning Through Semi-Propositionalization of Bottom Clauses
PDFDistributional-Relational Models: Scalable Semantics for Databases
PDFCombining Vector Space Embeddings with Symbolic Logical Inference over Open-Domain Text
PDFProbabilistic Region Connection Calculus
PDFA Unified Semantic Embedding: Relating Taxonomies and Attributes
PDFTowards Learning a Knowledge Base of Actions from Experiential Microblogs
PDFFoxPSL: An Extended and Scalable PSL Implementation
PDFLearning Probabilistic Logic Models with Human Advice
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