Predicting Knowledge in an Ontology Stream

Freddy Lécué; Jeff Z. Pan

Predicting Knowledge in an Ontology Stream

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Recently, ontology stream reasoning has been introduced as a multidisciplinary approach, merging synergies from Artificial Intelligence, Database, World-Wide-Web to reason on semantic augmented data streams. Although knowledge evolution and real-time reasoning have been largely addressed in ontology streams, the challenge of predicting its future (or missing) knowledge remains open and yet unexplored. We tackle predictive reasoning as a correlation and interpretation of past semantics-augmented data over exogenous ontology streams. Consistent predictions are constructed as Description Logics entailments by selecting and applying relevant cross-streams association rules. The experiments have shown accurate prediction with real and live stream data from Dublin City in Ireland.

Original language	English
Title of host publication	Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence
Editors	Francesca Rossi
Publisher	AAAI Press
Pages	2662–2669
Number of pages	8
ISBN (Print)	9781577356332
Publication status	Published - 3 Aug 2013
Event	Twenty-Third International Joint Conference on Artificial Intelligence - Beijing, China Duration: 3 Aug 2013 → 9 Aug 2013 Conference number: 23 https://ijcai13.org/

Publication series

Name	IJCAI '13
Publisher	AAAI Press

Conference

Conference	Twenty-Third International Joint Conference on Artificial Intelligence
Abbreviated title	IJCAI 2013
Country/Territory	China
City	Beijing
Period	3/08/13 → 9/08/13
Internet address	https://ijcai13.org/

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https://www.ijcai.org/Abstract/13/392Licence: All Rights Reserved

Cite this

@inproceedings{743fb41cfcc44a5590cdd030c3c672d0,

title = "Predicting Knowledge in an Ontology Stream",

abstract = "Recently, ontology stream reasoning has been introduced as a multidisciplinary approach, merging synergies from Artificial Intelligence, Database, World-Wide-Web to reason on semantic augmented data streams. Although knowledge evolution and real-time reasoning have been largely addressed in ontology streams, the challenge of predicting its future (or missing) knowledge remains open and yet unexplored. We tackle predictive reasoning as a correlation and interpretation of past semantics-augmented data over exogenous ontology streams. Consistent predictions are constructed as Description Logics entailments by selecting and applying relevant cross-streams association rules. The experiments have shown accurate prediction with real and live stream data from Dublin City in Ireland.",

author = "Freddy L{\'e}cu{\'e} and Pan, \{Jeff Z.\}",

year = "2013",

month = aug,

day = "3",

language = "English",

isbn = "9781577356332",

series = "IJCAI '13",

publisher = "AAAI Press",

pages = "2662–2669",

editor = "Francesca Rossi",

booktitle = "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence",

note = "Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI 2013 ; Conference date: 03-08-2013 Through 09-08-2013",

url = "https://ijcai13.org/",

}

TY - GEN

T1 - Predicting Knowledge in an Ontology Stream

AU - Lécué, Freddy

AU - Pan, Jeff Z.

N1 - Conference code: 23

PY - 2013/8/3

Y1 - 2013/8/3

N2 - Recently, ontology stream reasoning has been introduced as a multidisciplinary approach, merging synergies from Artificial Intelligence, Database, World-Wide-Web to reason on semantic augmented data streams. Although knowledge evolution and real-time reasoning have been largely addressed in ontology streams, the challenge of predicting its future (or missing) knowledge remains open and yet unexplored. We tackle predictive reasoning as a correlation and interpretation of past semantics-augmented data over exogenous ontology streams. Consistent predictions are constructed as Description Logics entailments by selecting and applying relevant cross-streams association rules. The experiments have shown accurate prediction with real and live stream data from Dublin City in Ireland.

AB - Recently, ontology stream reasoning has been introduced as a multidisciplinary approach, merging synergies from Artificial Intelligence, Database, World-Wide-Web to reason on semantic augmented data streams. Although knowledge evolution and real-time reasoning have been largely addressed in ontology streams, the challenge of predicting its future (or missing) knowledge remains open and yet unexplored. We tackle predictive reasoning as a correlation and interpretation of past semantics-augmented data over exogenous ontology streams. Consistent predictions are constructed as Description Logics entailments by selecting and applying relevant cross-streams association rules. The experiments have shown accurate prediction with real and live stream data from Dublin City in Ireland.

M3 - Conference contribution

SN - 9781577356332

T3 - IJCAI '13

SP - 2662

EP - 2669

BT - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence

A2 - Rossi, Francesca

PB - AAAI Press

T2 - Twenty-Third International Joint Conference on Artificial Intelligence

Y2 - 3 August 2013 through 9 August 2013

ER -

Predicting Knowledge in an Ontology Stream

Abstract

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