Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis

Cimiano, Philipp; Hotho, Andreas; Staab, Steffen

doi:10.1613/jair.1648

Cited by 381 publications

(302 citation statements)

References 44 publications

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“…It is commonly assumed that the desired information is local to a sentence [7-9,14]. We thus constrain the event extraction at the sentence resolution.…”

Section: Text Preprocessingmentioning

confidence: 99%

Extracting rate changes in transcriptional regulation from MEDLINE abstracts

Liu

Miao

et al. 2014

BMC Bioinformatics

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BackgroundTime delays are important factors that are often neglected in gene regulatory network (GRN) inference models. Validating time delays from knowledge bases is a challenge since the vast majority of biological databases do not record temporal information of gene regulations. Biological knowledge and facts on gene regulations are typically extracted from bio-literature with specialized methods that depend on the regulation task. In this paper, we mine evidences for time delays related to the transcriptional regulation of yeast from the PubMed abstracts.ResultsSince the vast majority of abstracts lack quantitative time information, we can only collect qualitative evidences of time delays. Specifically, the speed-up or delay in transcriptional regulation rate can provide evidences for time delays (shorter or longer) in GRN. Thus, we focus on deriving events related to rate changes in transcriptional regulation. A corpus of yeast regulation related abstracts was manually labeled with such events. In order to capture these events automatically, we create an ontology of sub-processes that are likely to result in transcription rate changes by combining textual patterns and biological knowledge. We also propose effective feature extraction methods based on the created ontology to identify the direct evidences with specific details of these events. Our ontologies outperform existing state-of-the-art gene regulation ontologies in the automatic rule learning method applied to our corpus. The proposed deterministic ontology rule-based method can achieve comparable performance to the automatic rule learning method based on decision trees. This demonstrates the effectiveness of our ontology in identifying rate-changing events. We also tested the effectiveness of the proposed feature mining methods on detecting direct evidence of events. Experimental results show that the machine learning method on these features achieves an F1-score of 71.43%.ConclusionsThe manually labeled corpus of events relating to rate changes in transcriptional regulation for yeast is available in https://sites.google.com/site/wentingntu/data. The created ontologies summarized both biological causes of rate changes in transcriptional regulation and corresponding positive and negative textual patterns from the corpus. They are demonstrated to be effective in identifying rate-changing events, which shows the benefits of combining textual patterns and biological knowledge on extracting complex biological events.

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“…It is commonly assumed that the desired information is local to a sentence [7-9,14]. We thus constrain the event extraction at the sentence resolution.…”

Section: Text Preprocessingmentioning

confidence: 99%

Extracting rate changes in transcriptional regulation from MEDLINE abstracts

Liu

Miao

et al. 2014

BMC Bioinformatics

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“…theory NELL [3] 24 × 7 learning fixed dynamic ML techniques DART [7] world knowledge × × semi-automated RTE [2], and [13] entailment × × ATP NLU [20] commonsense rules × × semi-supervised Text2Onto [6] ontology learning √ √ semi-supervised LexO [24] complex classes √ × semi-supervised FCA [5] taxonomy √ × FCA OP [4], and [23] ontology population available available semi-/supervised a corpus many be large, it might not contain all the necessary evidence of an event of interest. A corpus contains ambiguous statements about an event that leads to a non-determinism of the state of the event.…”

Section: Workmentioning

confidence: 99%

PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation Using Probabilistic Methods

Abeyruwan

Visser

Lemmon

et al. 2013

Uncertainty Reasoning for the Semantic Web II

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Abstract.Formalizing an ontology for a domain manually is well-known as a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck. Therefore, researchers developed algorithms and systems that can help to automatize the process. Among them are systems that include text corpora for the acquisition. Our idea is also based on vast amount of text corpora. Here, we provide a novel unsupervised bottom-up ontology generation method. It is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. We provide a quantitative and two qualitative results illustrating our approach using a high throughput screening assay corpus and two custom text corpora. This process could also provide evidence for domain experts to build ontologies based on top-down approaches.

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“…Moreover, it may evidence inconsistent concept chains as instances are extracted in pairs and gathered to form taxonomy hierarchies. Set-theoretic approaches [3] use formal concept analysis that naturally structures terms with intensional inclusion relations within a concept lattice. Such term organization differs from usual lexical taxonomies that provide semantic relations between terms rather than inclusion relations between formal concepts.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Learning Pretopological Spaces for Lexical Taxonomy Acquisition

Cleuziou

Dias

2015

Machine Learning and Knowledge Discovery in Databases

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Abstract. In this paper, we propose a new methodology for semisupervised acquisition of lexical taxonomies. Our approach is based on the theory of pretopology that offers a powerful formalism to model semantic relations and transforms a list of terms into a structured term space by combining different discriminant criteria. In order to learn a parameterized pretopological space, we define the Learning Pretopological Spaces strategy based on genetic algorithms. In particular, rare but accurate pieces of knowledge are used to parameterize the different criteria defining the pretopological term space. Then, a structuring algorithm is used to transform the pretopological space into a lexical taxonomy. Results over three standard datasets evidence improved performances against state-of-the-art associative and pattern-based approaches.

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Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis

Cited by 381 publications

References 44 publications

Extracting rate changes in transcriptional regulation from MEDLINE abstracts

Extracting rate changes in transcriptional regulation from MEDLINE abstracts

PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation Using Probabilistic Methods

Learning Pretopological Spaces for Lexical Taxonomy Acquisition

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