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Found 9 papers, 4 papers with code
Computationally Assisted Quality Control for Public Health Data Streams
However, existing outlier detection frameworks perform poorly on this task because they do not account for the data volume or for the statistical properties of public health streams.
Conversational Neuro-Symbolic Commonsense Reasoning
More precisely, we consider the problem of identifying the unstated presumptions of the speaker that allow the requested action to achieve the desired goal from the given state (perhaps elaborated by making the implicit presumptions explicit).
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
In this paper we look at a more practical setting, namely QA over the combination of a KB and entity-linked text, which is appropriate when an incomplete KB is available with a large text corpus.
Graph Representation Learning
Open-Domain Question Answering
Quasar: Datasets for Question Answering by Search and Reading
ClueWeb09 serves as the background corpus for extracting these answers.
Explainable Entity-based Recommendations with Knowledge Graphs
Explainable recommendation is an important task.
Bootstrapping Distantly Supervised IE using Joint Learning and Small Well-structured Corpora
We propose a framework to improve performance of distantly-supervised relation extraction, by jointly learning to solve two related tasks: concept-instance extraction and relation extraction.
Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic
We show that the problem of constructing proofs for this logic is related to computation of personalized PageRank (PPR) on a linearized version of the proof space, and using on this connection, we develop a proveably-correct approximate grounding scheme, based on the PageRank-Nibble algorithm.
Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic
In many probabilistic first-order representation systems, inference is performed by "grounding"---i. e., mapping it to a propositional representation, and then performing propositional inference.
