Semantic tagging with linked open data

John Cuzzola; Zoran Jeremic; Ebrahim Bagheri; Dragan Gasevic; Jelena Jovanovic; Reza Bashash

Semantic tagging with linked open data

John Cuzzola, Zoran Jeremic, Ebrahim Bagheri, Dragan Gasevic, Jelena Jovanovic, Reza Bashash

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review

Abstract

Making sense of text is a challenge for computers particularly with the ambiguity associated with language. Various annotators continue to be developed using a variety of techniques in order to provide context to text. In this paper, we describe Denote-our annotator that uses a structured ontology, machine learning, and statistical analysis to perform tagging and topic discovery. A short screencast for the curious is also available at http://youtu.be/espItTRQVzY as well as demonstration links provided in the conclusion.

Original language	English
Title of host publication	4th Canadian Semantic Web Symposium
Publisher	Rheinisch-Westfaelische Technische Hochschule Aachen
Pages	52-53
Number of pages	2
Volume	1054
Publication status	Published - 2013
Externally published	Yes

Keywords

Annotators
Disambiguation
Entity recognition
Linked-data
LOD
Semantic web
Tagging
Wikifying

Cite this

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title = "Semantic tagging with linked open data",

abstract = "Making sense of text is a challenge for computers particularly with the ambiguity associated with language. Various annotators continue to be developed using a variety of techniques in order to provide context to text. In this paper, we describe Denote-our annotator that uses a structured ontology, machine learning, and statistical analysis to perform tagging and topic discovery. A short screencast for the curious is also available at http://youtu.be/espItTRQVzY as well as demonstration links provided in the conclusion.",

keywords = "Annotators, Disambiguation, Entity recognition, Linked-data, LOD, Semantic web, Tagging, Wikifying",

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T1 - Semantic tagging with linked open data

AU - Cuzzola, John

AU - Jeremic, Zoran

AU - Bagheri, Ebrahim

AU - Gasevic, Dragan

AU - Jovanovic, Jelena

AU - Bashash, Reza

PY - 2013

Y1 - 2013

N2 - Making sense of text is a challenge for computers particularly with the ambiguity associated with language. Various annotators continue to be developed using a variety of techniques in order to provide context to text. In this paper, we describe Denote-our annotator that uses a structured ontology, machine learning, and statistical analysis to perform tagging and topic discovery. A short screencast for the curious is also available at http://youtu.be/espItTRQVzY as well as demonstration links provided in the conclusion.

AB - Making sense of text is a challenge for computers particularly with the ambiguity associated with language. Various annotators continue to be developed using a variety of techniques in order to provide context to text. In this paper, we describe Denote-our annotator that uses a structured ontology, machine learning, and statistical analysis to perform tagging and topic discovery. A short screencast for the curious is also available at http://youtu.be/espItTRQVzY as well as demonstration links provided in the conclusion.

KW - Annotators

KW - Disambiguation

KW - Entity recognition

KW - Linked-data

KW - LOD

KW - Semantic web

KW - Tagging

KW - Wikifying

UR - http://www.scopus.com/inward/record.url?scp=84924354528&partnerID=8YFLogxK

M3 - Conference Paper

AN - SCOPUS:84924354528

VL - 1054

SP - 52

EP - 53

BT - 4th Canadian Semantic Web Symposium

PB - Rheinisch-Westfaelische Technische Hochschule Aachen

ER -

Semantic tagging with linked open data

Abstract

Keywords

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Cite this