Knowledge Graph Identification
Jay Pujara,Hui Miao,Lise Getoor,William W. Cohen +3 more
- 21 Oct 2013
- Vol. 8218, pp 542-557
TL;DR: This paper shows how uncertain extractions about entities and their relations can be transformed into a knowledge graph and shows that compared to existing methods, the proposed approach is able to achieve improved AUC and F1 with significantly lower running time.
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Abstract: Large-scale information processing systems are able to extract massive collections of interrelated facts, but unfortunately transforming these candidate facts into useful knowledge is a formidable challenge. In this paper, we show how uncertain extractions about entities and their relations can be transformed into a knowledge graph. The extractions form an extraction graph and we refer to the task of removing noise, inferring missing information, and determining which candidate facts should be included into a knowledge graph as knowledge graph identification. In order to perform this task, we must reason jointly about candidate facts and their associated extraction confidences, identify co-referent entities, and incorporate ontological constraints. Our proposed approach uses probabilistic soft logic (PSL), a recently introduced probabilistic modeling framework which easily scales to millions of facts. We demonstrate the power of our method on a synthetic Linked Data corpus derived from the MusicBrainz music community and a real-world set of extractions from the NELL project containing over 1M extractions and 70K ontological relations. We show that compared to existing methods, our approach is able to achieve improved AUC and F1 with significantly lower running time.
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Citations
Knowledge vault: a web-scale approach to probabilistic knowledge fusion
Xin Dong,Evgeniy Gabrilovich,Geremy Heitz,Wilko Horn,Ni Lao,Kevin Murphy,Thomas Strohmann,Shaohua Sun,Wei Zhang +8 more
- 24 Aug 2014
TL;DR: The Knowledge Vault is a Web-scale probabilistic knowledge base that combines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories that computes calibrated probabilities of fact correctness.
A Review of Relational Machine Learning for Knowledge Graphs
Maximilian Nickel,Kevin Murphy,Volker Tresp,Evgeniy Gabrilovich +3 more
- 01 Jan 2016
TL;DR: This paper provides a review of how statistical models can be “trained” on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph) and how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web.
Knowledge Graphs
Aidan Hogan,Eva Blomqvist,Michael Cochez,Claudia d'Amato,Gerard de Melo,Claudio Gutierrez,José Emilio Labra Gayo,Sabrina Kirrane,Sebastian Neumaier,Axel Polleres,Roberto Navigli,Axel-Cyrille Ngonga Ngomo,Sabbir M. Rashid,Anisa Rula,Lukas Schmelzeisen,Juan F. Sequeda,Steffen Staab,Antoine Zimmermann +17 more
TL;DR: The historical events that lead to the interweaving of data and knowledge are tracked to help improve knowledge and understanding of the world around us.
1.3K
A review: Knowledge reasoning over knowledge graph
TL;DR: The basic concept and definitions of knowledge reasoning and the methods for reasoning over knowledge graphs are reviewed, and the reasoning methods are dissected into three categories: rule- based reasoning, distributed representation-based reasoning and neural network-based Reasoning.
912
Never-ending learning
Tom M. Mitchell,William W. Cohen,Estevam R. Hruschka,Partha Pratim Talukdar,Bishan Yang,Justin Betteridge,Andrew Carlson,Bhavana Dalvi,Matt Gardner,Bryan Kisiel,Jayant Krishnamurthy,Ni Lao,Kathryn Mazaitis,T. Mohamed,Ndapandula Nakashole,Emmanouil Antonios Platanios,Alan Ritter,Mehdi Samadi,Burr Settles,Richard Wang,Derry Tanti Wijaya,Abhinav Gupta,Xinlei Chen,Abulhair Saparov,M. Greaves,J. Welling +25 more
TL;DR: The Never-Ending Language Learner (NELL) as discussed by the authors is a case study of a machine learning system that learns to read the Web 24hrs/day since January 2010, and so far has acquired a knowledge base with 120mn diverse, confidence-weighted beliefs (e.g., servedWith(tea,biscuits), while learning thousands of interrelated functions that continually improve its reading competence over time.
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