Discovering Relations Using Matrix Factorization Methods

Cergani, Ervina; Miettinen, Pauli

doi:10.1145/2505515.2507841

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Discovering Relations Using Matrix Factorization Methods

MPS-Authors

/persons/resource/persons104374

Cergani, Ervina
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons45046

Miettinen, Pauli
Databases and Information Systems, MPI for Informatics, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Cergani, E., & Miettinen, P. (2013). Discovering Relations Using Matrix Factorization Methods. In W. Nejdl, J. Pei, & R. Rastogi (Eds.), CIKM’13 (pp. 1549-1552). New York, NY: ACM. doi:10.1145/2505515.2507841.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0015-19DB-7

Abstract

Traditional relation extraction methods work on manually defined relations and typically expect manually labelled extraction patterns for each relation. This strongly limits the scalability of these systems. In Open Relation Extraction (ORE), the relations are identified automatically based on co-occurrences of ``surface relations'' (contexts) and entity pairs. The recently-proposed methods for ORE use partition clustering to find the relations. In this work we propose the use of matrix factorization methods instead of clustering. Specifically, we study Non-Negative Matrix Factorization (NMF) and Boolean Matrix Factorization (BMF). These methods overcome many problems inherent in clustering and perform better than the k-means clustering in our evaluation.