Paper Info
Title
Open domain knowledge extraction: inference on a web scale
Abstract
Though ontologies are considered central to foster the Semantic Web effort, their practical application on a Web scale is limited by the difficulty of building and maintaining high-coverage ontologies, for any of the almost unbounded number of social and technical domains mirrored on the Web. In this paper we present Open Knowledge Extraction (Open KE), a novel paradigm that creates a bridge between information retrieval, taxonomy learning and automated reasoning. Open KE builds on recently published algorithms for Open Information Extraction (Open IE) and automated taxonomy learning, which were shown able to extract information on a Web scale basis in an unsupervised manner. The key idea of Open KE is to generalize Open IE's lexicalized extractions with an automatically learned taxonomy. Lexicalized extractions are transformed in logic predicates, and used to populate a Semantic Model. An inference engine is then used to perform inductions and deductions. In this paper we describe the knowledge extraction workflow in its entirety, and apply it to a large-scale experiment in the domains of Artificial Intelligence and Virology.
Year
DOI
Venue
2013
10.1145/2479787.2479788
WIMS
Keywords
Field
DocType
semantic web effort,open ie,open domain knowledge extraction,web scale basis,automated taxonomy learning,lexicalized extraction,web scale,open ke,open information extraction,open knowledge extraction,taxonomy learning,automated reasoning,semantic web,ontology
Automated reasoning,Data mining,Semantic Web Stack,Computer science,Semantic Web,Artificial intelligence,Natural language processing,Social Semantic Web,Ontology (information science),Information retrieval,Data Web,Information extraction,Knowledge extraction
Conference
Citations 
PageRank 
References 
0
0.34
20
Authors
3
Name
Order
Citations
PageRank
paola velardi11553163.66
Fulvio D'antonio27812.08
Alessandro Cucchiarelli322636.38