Including Spatial Relations and Scales within Sequential Pattern Extraction

Fabrègue, Mickaël; Braud, Agnès; Bringay, Sandra; Ber, Florence Le; Teisseire, Maguelonne

doi:10.1007/978-3-642-33492-4_18

Cited by 3 publications

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References 19 publications

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“…The following definitions are detailed in [2] and presented here according to an example taken from [11]. The database DB in table 1 represents a set of transactions.…”

Section: Preliminariesmentioning

confidence: 99%

Xart system

Berrahou

Buche

Dibie-Barthelemy

et al. 2016

Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

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In this paper, we present Xart system based on a hybrid method using datamining approaches and syntactic analysis to automatically discover and extract relevant data modeled as n-ary relations in plain text. A n-ary relation links a studied object with its features considered as several arguments. Our work focuses on extracting those arguments in text in order to populate a domain Ontological and Terminological Resource (OTR) with new instances. Our approach relies on a new data representation in order to increase data expressiveness in the knowledge discovery process, using the concepts defined in the OTR. Using sequential rules and pattern mining allows the discovery of implicit forms of expression that are used to describe arguments of n-ary relations in text. Once the implicit rules are discovered in specific patterns, we define as Ontological Sequential Patterns (OSP), we use syntactic relations to enrich the patterns in order to obtain Ontological Linguistic Sequential Patterns (OLSP) where the arguments of n-ary relations are expressed according to different levels of word abstraction (word, grammatical category and concept). We have made concluding experiments on a corpus from food packaging domain where relevant data to be extracted are experimental results on packagings. We have been able to extract up to 4 correlated arguments with a F-measure from 0.6 to 0.8.

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“…The following definitions are detailed in [2] and presented here according to an example taken from [11]. The database DB in table 1 represents a set of transactions.…”

Section: Preliminariesmentioning

confidence: 99%