Mining for Lexons: Applying Unsupervised Learning Methods to Create Ontology Bases

Reinberger, Marie-Laure; Spyns, Peter; Daelemans, Walter; Meersman, Robert

doi:10.1007/978-3-540-39964-3_51

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…In the existing work, researchers mainly focus on mining the ontologies from text documents (e.g., web content) [25] or other web data (web usage, web structure and web user profiles) [23]. In [28], clustering is used to discover the concepts in the ontology. Association rule mining has been adopted to discover the relationships between different concepts [26].…”

Section: Related Workmentioning

confidence: 99%

Development of NeuroElectroMagnetic ontologies(NEMO)

Dou

Frishkoff

Rong

et al. 2007

Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Event-related potentials (ERP) are brain electrophysiological patterns created by averaging electroencephalographic (EEG) data, time-locking to events of interest (e.g., stimulus or response onset). In this paper, we propose a generic framework for mining and developing domain ontologies and apply it to mine brainwave (ERP) ontologies. The concepts and relationships in ERP ontologies can be mined according to the following steps: pattern decomposition, extraction of summary metrics for concept candidates, hierarchical clustering of patterns for classes and class taxonomies, and clustering-based classification and association rules mining for relationships (axioms) of concepts. We have applied this process to several dense-array (128-channel) ERP datasets. Results suggest good correspondence between mined concepts and rules, on the one hand, and patterns and rules that were independently formulated by domain experts, on the other. Data mining results also suggest ways in which expert-defined rules might be refined to improve ontology representation and classification results. The next goal of our ERP ontology mining framework is to address some long-standing challenges in conducting large-scale comparison and integration of results across ERP paradigms and laboratories. In a more general context, this work illustrates the promise of an interdisciplinary research program, which combines data mining, neuroinformatics and ontology engineering to address real-world problems.

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Section: Related Workmentioning

confidence: 99%

Development of NeuroElectroMagnetic ontologies(NEMO)

Dou

Frishkoff

Rong

et al. 2007

Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…An interesting framework for hybrid approaches, combining the above techniques is presented in [36]. The Thematic Mapping System [8] developed at Verity, Inc. and the lexon mining approach [28] most closely reflects our perspective. A complementary approach that uses the structure and content of HTML-based pages on the Web to generate ontologies is presented in [9].…”

Section: Related Workmentioning

confidence: 99%

TaxaMiner: an experimentation framework for automated taxonomy bootstrapping

Kashyap¹,

Ramakrishnan²,

Thomas³

et al. 2005

IJWGS

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Ontologies are a central component of the Semantic Web (SW) infrastructure. The design and construction of domain ontologies and taxonomies is a human intensive process which requires allocation of huge resources in terms of cost and time. For the SW to scale and become feasible, approaches that reduce human effort and resource commitments need to be investigated urgently. Towards this end, we present a framework for automated taxonomy construction based on a large corpus of documents, a first step towards large scale, automated ontology construction. Our approach involves: (a) generation of a document cluster hierarchy; (b) extraction of a topic hierarchy from this cluster hierarchy; and (c) assignment of labels to nodes in the topic hierarchy. We draw upon a suite of clustering and NLP techniques and identify parameters which form the basis of an experimentation framework. We also propose metrics to measure quality of the resulting topic hierarchy and evaluate the impact of various parameters on these quality metrics. The MEDLINE® database is used as the document corpus and the MeSH thesaurus as the gold standard. Insights from these experiments are presented and discussed.

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“…false combinations generated by chance) from genuine results. More details on the linguistic processing can be found in [20,21,22].…”

Section: Unsupervised Text Miningmentioning

confidence: 99%

“…These decisions require the involvement of human evaluators, and/or an established gold standard. An earlier experiment on evaluating the precision of unsupervised text mining for ontologies is reported in [20] using UMLS [13] as gold standard.…”

Section: Precision and Recallmentioning

confidence: 99%

Lexically Evaluating Ontology Triples Generated Automatically from Texts

Spyns

Reinberger

2005

Lecture Notes in Computer Science

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Abstract. Our purpose is to present a method to lexically evaluate the results of extracting in an unsupervised way material from text corpora to build ontologies. We have worked on a legal corpus (EU VAT directive) consisting of 43K words. The unsupervised text miner has produced a set of triples. These are to be used as preprocessed material for the construction of ontologies from scratch. A quantitative scoring method (coverage, accuracy, recall and precision metrics resulting in a 38.68%, 52.1%, 9.84% and 75.81% scores respectively) has been defined and applied.

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Mining for Lexons: Applying Unsupervised Learning Methods to Create Ontology Bases

Cited by 13 publications

References 16 publications

Development of NeuroElectroMagnetic ontologies(NEMO)

Development of NeuroElectroMagnetic ontologies(NEMO)

TaxaMiner: an experimentation framework for automated taxonomy bootstrapping

Lexically Evaluating Ontology Triples Generated Automatically from Texts

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