Overview of Biomedical Relations Extraction using Hybrid Rule-based Approaches

Kadir, Rabiah Abdul; Bokharaeian, Behrouz

doi:10.12720/jiii.1.3.169-173

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…It may be because cause-effect is a common relation that researchers scale up to RE literature reviews. Examples include the generalized survey [96], detailed analyses of RE in the biomedical domain [41,101], and a survey about the application of distant supervision on RE [82]. From our point of view, however, CE is different from RE, as the former task is a binary classification while the later is multiple classification problem.…”

Section: Previous Surveysmentioning

confidence: 99%

A survey on extraction of causal relations from natural language text

Han

Poon

2022

Knowl Inf Syst

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As an essential component of human cognition, cause–effect relations appear frequently in text, and curating cause–effect relations from text helps in building causal networks for predictive tasks. Existing causality extraction techniques include knowledge-based, statistical machine learning (ML)-based, and deep learning-based approaches. Each method has its advantages and weaknesses. For example, knowledge-based methods are understandable but require extensive manual domain knowledge and have poor cross-domain applicability. Statistical machine learning methods are more automated because of natural language processing (NLP) toolkits. However, feature engineering is labor-intensive, and toolkits may lead to error propagation. In the past few years, deep learning techniques attract substantial attention from NLP researchers because of its powerful representation learning ability and the rapid increase in computational resources. Their limitations include high computational costs and a lack of adequate annotated training data. In this paper, we conduct a comprehensive survey of causality extraction. We initially introduce primary forms existing in the causality extraction: explicit intra-sentential causality, implicit causality, and inter-sentential causality. Next, we list benchmark datasets and modeling assessment methods for causal relation extraction. Then, we present a structured overview of the three techniques with their representative systems. Lastly, we highlight existing open challenges with their potential directions.

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Section: Previous Surveysmentioning

confidence: 99%

A survey on extraction of causal relations from natural language text

Han

Poon

2022

Knowl Inf Syst

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“…On the other side, the researchers have successfully addressed some of the previously discussed challenges such as the long sentences of Arabic and the non-fixed location of semantic relations in sentences. Therefore, they have integrated the rule-based method with machine learning to get hybrid approaches [4,23,65]. These hybrid methods have demonstrated enhanced performance in comparison to the single rule-based or the machine learning-based approaches.…”

Section: Work Year Of Publication Contributionmentioning

confidence: 99%

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

2019

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Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language’s nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept’s path distance, (2) the concept’s distribution layer, and (3) the mutual parent concept’s distribution layer. Then, feature mapping is performed by assigning the concepts’ similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA’s mutation, crossover, and selection processes with the WOA’s processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures.

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“…Kadir and Bokharaeian [ 26 ] proposed a new technique to extract relations between biological and medical entities from biomedical documents. This technique is hybrid in nature, combines different relation extraction approaches, and extracts simple as well as complex relations between the pair of entities.…”

Section: Literature Reviewmentioning

confidence: 99%

A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set

Muzaffar

Azam

Qamar

2015

Computational and Mathematical Methods in Medicine

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The information extraction from unstructured text segments is a complex task. Although manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually because of the exponential increase in data size. Thus, there is a need for automatic tools and techniques for information extraction in biomedical text mining. Relation extraction is a significant area under biomedical information extraction that has gained much importance in the last two decades. A lot of work has been done on biomedical relation extraction focusing on rule-based and machine learning techniques. In the last decade, the focus has changed to hybrid approaches showing better results. This research presents a hybrid feature set for classification of relations between biomedical entities. The main contribution of this research is done in the semantic feature set where verb phrases are ranked using Unified Medical Language System (UMLS) and a ranking algorithm. Support Vector Machine and Naïve Bayes, the two effective machine learning techniques, are used to classify these relations. Our approach has been validated on the standard biomedical text corpus obtained from MEDLINE 2001. Conclusively, it can be articulated that our framework outperforms all state-of-the-art approaches used for relation extraction on the same corpus.

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Overview of Biomedical Relations Extraction using Hybrid Rule-based Approaches

Cited by 10 publications

References 8 publications

A survey on extraction of causal relations from natural language text

A survey on extraction of causal relations from natural language text

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set

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