35 citations · 36 across the 4 of their papers we have counts for
4 papers
Learning Relational Dependency Networks for Relation Extraction
Dileep Viswanathan, Ameet Soni, Jude Shavlik +1
We consider the task of KBP slot filling -- extracting relation information from newswire documents for knowledge base construction. We present our pipeline, which employs Relation…
Scaling Inference for Markov Logic with a Task-Decomposition Approach
Feng Niu, Ce Zhang, Christopher Ré +1
Motivated by applications in large-scale knowledge base construction, we study the problem of scaling up a sophisticated statistical inference framework called Markov Logic Network…
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS
Feng Niu, Christopher Ré, AnHai Doan +1
Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including…
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies
D. W. Opitz, J. W. Shavlik
An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to impro…