Learning from Ontology Streams with Semantic Concept Drift

Jiaoyan Chen; Freddy Lécué; Jeff Z. Pan; Huajun Chen

doi:10.24963/ijcai.2017/133

Learning from Ontology Streams with Semantic Concept Drift

Jiaoyan Chen, Freddy Lécué, Jeff Z. Pan, Huajun Chen^*

^*Corresponding author for this work

Computing Science

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

18 Citations (Scopus)

Abstract

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.

Original language	English
Title of host publication	26th International Joint Conference on Artificial Intelligence, IJCAI 2017
Editors	Carles Sierra
Publisher	AAAI Press / International Joint Conferences on Artificial Intelligence
Pages	957-963
Number of pages	7
ISBN (Electronic)	9780999241103
DOIs	https://doi.org/10.24963/ijcai.2017/133
Publication status	Published - 2018
Event	26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia Duration: 19 Aug 2017 → 25 Aug 2017

Conference

Conference	26th International Joint Conference on Artificial Intelligence, IJCAI 2017
Country/Territory	Australia
City	Melbourne
Period	19/08/17 → 25/08/17

Bibliographical note

This work is funded by NSFC 61473260/61673338/61672393, the Alibaba-ZJU joint project on e-Business Knowledge Graph, and the EU Marie Curie IAPP K-Drive project (286348).

Keywords

knowledge representation
reasoning and logic
decription logics and ontologies
machine learning
time series/data streams

Access to Document

10.24963/ijcai.2017/133Licence: Unspecified

Cite this

Learning from Ontology Streams with Semantic Concept Drift. / Chen, Jiaoyan; Lécué, Freddy; Pan, Jeff Z. et al.
26th International Joint Conference on Artificial Intelligence, IJCAI 2017. ed. / Carles Sierra. AAAI Press / International Joint Conferences on Artificial Intelligence, 2018. p. 957-963.

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

Chen, J, Lécué, F, Pan, JZ & Chen, H 2018, Learning from Ontology Streams with Semantic Concept Drift. in C Sierra (ed.), 26th International Joint Conference on Artificial Intelligence, IJCAI 2017. AAAI Press / International Joint Conferences on Artificial Intelligence, pp. 957-963, 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 19/08/17. https://doi.org/10.24963/ijcai.2017/133

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title = "Learning from Ontology Streams with Semantic Concept Drift",

abstract = "Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.",

keywords = "knowledge representation, reasoning and logic, decription logics and ontologies, machine learning, time series/data streams",

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

note = "This work is funded by NSFC 61473260/61673338/61672393, the Alibaba-ZJU joint project on e-Business Knowledge Graph, and the EU Marie Curie IAPP K-Drive project (286348).; 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 ; Conference date: 19-08-2017 Through 25-08-2017",

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doi = "10.24963/ijcai.2017/133",

language = "English",

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AU - Lécué, Freddy

AU - Pan, Jeff Z.

AU - Chen, Huajun

N1 - This work is funded by NSFC 61473260/61673338/61672393, the Alibaba-ZJU joint project on e-Business Knowledge Graph, and the EU Marie Curie IAPP K-Drive project (286348).

PY - 2018

Y1 - 2018

N2 - Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.

AB - Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.

KW - knowledge representation

KW - reasoning and logic

KW - decription logics and ontologies

KW - machine learning

KW - time series/data streams

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DO - 10.24963/ijcai.2017/133

M3 - Published conference contribution

AN - SCOPUS:85031931222

SP - 957

EP - 963

BT - 26th International Joint Conference on Artificial Intelligence, IJCAI 2017

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PB - AAAI Press / International Joint Conferences on Artificial Intelligence

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Y2 - 19 August 2017 through 25 August 2017

ER -

Learning from Ontology Streams with Semantic Concept Drift

Abstract

Conference

Bibliographical note

Keywords

Access to Document

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Cite this