A lexico-semantic pattern language for learning ontology instances from text

IJntema, Wouter; Sangers, Jordy; Hogenboom, Frederik; Frăsincar, Flavius

doi:10.1016/j.websem.2012.01.002

Cited by 34 publications

(4 citation statements)

References 29 publications

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“…Its data model seems to allow only a disjunct partitioning of the document. HIEL (IJntema et al 2012) is built upon JAPE and focuses on a compact rule representation for elements of an ontology. Xlog (Shen et al 2007) is a rule language based on Datalog with embedded extraction predicates.…”

Section: Rule Languagesmentioning

confidence: 99%

UIMA Ruta: Rapid development of rule-based information extraction applications

et al. 2014

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Rule-based information extraction is an important approach for processing the increasingly available amount of unstructured data. The manual creation of rule-based applications is a time-consuming and tedious task, which requires qualified knowledge engineers. The costs of this process can be reduced by providing a suitable rule language and extensive tooling support. This paper presents UIMA Ruta, a tool for rule-based information extraction and text processing applications. The system was designed with focus on rapid development. The rule language and its matching paradigm facilitate the quick specification of comprehensible extraction knowledge. They support a compact representation while still providing a high level of expressiveness. These advantages are supplemented by the development environment UIMA Ruta Workbench. It provides, in addition to extensive editing support, essential assistance for explanation of rule execution, introspection, automatic validation, and rule induction. UIMA Ruta is a useful tool for academia and industry due to its open source license. We compare UIMA Ruta to related rule-based systems especially concerning the compactness of the rule representation, the expressiveness, and the provided tooling support. The competitiveness of the runtime performance is shown in relation to a popular and freelyavailable system. A selection of case studies implemented with UIMA Ruta illustrates the usefulness of the system in real-world scenarios.

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Section: Rule Languagesmentioning

confidence: 99%

UIMA Ruta: Rapid development of rule-based information extraction applications

et al. 2014

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“…Similar to the finite automaton approach, CFG is used to build a parser to encode the sequence and pattern in the learnt language. However, this method mainly utilizes the lexicon-syntactics (Hung et al 2010) (vocabulary based and structural pattern) of the learnt text, instead of incorporating the lexicon-semantic pattern (IJntema et al 2012). In fact, the lexical semantic-based pattern can encode the semantic representation of the texts, and this expression can contribute towards a deeper understanding of the text.…”

Section: Text Identificationmentioning

confidence: 99%

A comparative study of evolving fuzzy grammar and machine learning techniques for text categorization

et al. 2014

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Several methods have been studied in text categorization and mostly are inspired by the statistical distribution features in the texts, such as the implementation of Machine Learning (ML) methods. However, there is no work available that investigates the performance of ML-based methods against the text expression-based method, especially for incident and medical case categorization. Meanwhile, these two domains are becoming ever more popular, due to a growing interest of automation in security intelligence and health services. This paper presents a text expression-based method called Evolving Fuzzy Grammar (EFG) and evaluates its performance against the conventional ML methods of Naïve Bayes, support vector machine, k-nearest neighbor, adaptive booting, and decision tree. The incident dataset used is a real dataset that was taken from the World Incidents Tracking System, while ImageCLEF 2009 was used as the source for radiology case reports. The results suggested variations of strength and weakness of each method in both cateCommunicated by V. Loia. gorization tasks, where a standard evaluation technique (i.e., recall, precision, and F-measure) was used. In both domains, the SMO and IBk methods were the best, while AdaBoost was the worst. It was also observed that the medical dataset was easier to categorize than the incident. Although EFG was ranked second lowest, it obtained the highest precision score in the bombing categorization, the highest score in armed attack recall, and was averagely ranked in the top three for the medical case categorization. It was also noted that the text expression-based method used in EFG was the most verbose and expressive, when compared to the ML methods. This indicates that EFG is a viable method in text categorization and may serve as an alternative approach to such a task.

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“…Research that solves the problem of automatic or semi-automatic replenishment of the ontology is based on patterns of language structures located in texts. These are lexico-syntactic patterns used syntactic information and lexical perceptions [6], [7] or lexico-semantic patterns [8], [9], which combine lexical perceptions with semantic and syntactic information in extraction process. A feature of the proposed approach is that the applied patterns will be built automatically basing on other types of ontology patterns [10], prerequisites of which are presented in [11], [12], and for further term extraction, named entity extraction methods based on machine learning will be used [13], trained on those terms that will be extracted using patterns.…”

Section: Introductionmentioning

confidence: 99%

Development of an intelligent information resource model based on modern natural language processing methods

Sadirmekova

Sambetbayeva

Serikbayeva

et al. 2023

IJECE

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<span lang="EN-US">Currently, there is an avalanche-like increase in the need for automatic text processing, respectively, new effective methods and tools for processing texts in natural language are emerging. Although these methods, tools and resources are mostly presented on the internet, many of them remain inaccessible to developers, since they are not systematized, distributed in various directories or on separate sites of both humanitarian and technical orientation. All this greatly complicates their search and practical use in conducting research in computational linguistics and developing applied systems for natural text processing. This paper is aimed at solving the need described above. The paper goal is to develop model of an intelligent information resource based on modern methods of natural language processing (IIR NLP). The main goal of IIR NLP is to render convenient valuable access for specialists in the field of computational linguistics. The originality of our proposed approach is that the developed ontology of the subject area “NLP” will be used to systematize all the above knowledge, data, information resources and organize meaningful access to them, and semantic web standards and technology tools will be used as a software basis.</span>

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A lexico-semantic pattern language for learning ontology instances from text

Cited by 34 publications

References 29 publications

UIMA Ruta: Rapid development of rule-based information extraction applications

UIMA Ruta: Rapid development of rule-based information extraction applications

A comparative study of evolving fuzzy grammar and machine learning techniques for text categorization

Development of an intelligent information resource model based on modern natural language processing methods

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