CORE: Context-Aware Open Relation Extraction with Factorization Machines

By Fabio Petroni, Luciano Del Corro, Rainer Gemulla. 2015

32 citations recorded by the source corpus.

We propose CORE, a novel matrix factorization model that leverages contextual information for open relation extraction. Our model is based on factorization machines and integrates facts from various sources, such as knowledge bases or open information extractors, as well as the context in which these facts have been observed. We argue that integrating contextual information—such as metadata about extraction sources, lexical context, or type information—significantly improves prediction performance. Open information extractors, for example, may produce extractions that are unspecific or ambiguous when taken out of context. Our experimental study on a large real-world dataset indicates that CORE has significantly better prediction performance than state-ofthe-art approaches when contextual information is available.

Subfields: Artificial Intelligence, Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

KILT: a Benchmark for Knowledge Intensive Language Tasks (2020, background): “KILT is an in-KB resource (Petroni et al., 2015), i.e., the evidence required to answer each of the ~3.2M instances in KILT is present somewhere in the knowledge source.”
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, background): “…including pattern extraction (Banko et al., 2007; Fader et al., 2011; Mausam et al., 2012; Del Corro and Gemulla, 2013; Angeli et al., 2015; Petroni et al., 2015; Stanovsky and Da-gan, 2016; Mausam, 2016; Cui et al., 2018), relation discovery (Yao et al., 2011; Marcheggiani and Titov,…”
Neural Factorization Machines for Sparse Predictive Analytics (2017, background): “It has been successfully applied to many predictive tasks, ranging from online advertising [21], microblog retrieval [26], to open relation extraction [25].”
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks (2017, methodology): “, 2016] to natural language processing [Petroni et al., 2015].”