Relational Markov Network

See: Markov Network, Conditional Random Field.

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References

2004

• (Bunescu & Mooney, 2004) ⇒ Razvan C. Bunescu, and Raymond Mooney. (2004). “Collective Information Extraction with Relational Markov Networks.” In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004). doi:10.3115/1218955.1219011.
  • We present a new IE method that employs Relational Markov Networks (a generalization of CRFs), which can represent arbitrary dependencies between extractions.

2003

• (Taskar et al., 2003) ⇒ Ben Taskar, Ming-Fai Wong, Pieter Abbeel and Daphne Koller. (2003). “Link Prediction in Relational Data.” In: Neural Information Processing Systems Conference (NIPS 2003)
  • QUOTE: We apply the relational Markov network framework of Taskar et al. to define a joint probabilistic model over the entire link graph — entity attributes and links.

2002

• (Taskar et al., 2002) ⇒ Ben Taskar, Pieter Abbeel, and Daphne Koller. (2002). “Discriminative Probabilistic Models for Relational Data.” In: Proceedings of UAI Conference (UAI 2002).
  • We introduce the framework of relational Markov network (RMNs), which compactly defines a Markov network over a relational data set. The graphical structure of an RMN is based on the relational structure of the domain, and can easily model complex patterns over related entities.

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