Multi-Ontology Refined Embeddings (MORE): A hybrid multi-ontology and corpus-based semantic representation model for biomedical concepts

Jiang, Steven; Wu, Weiyi; Tomita, Naofumi; Ganoe, Craig H.; Hassanpour, Saeed

doi:10.1016/j.jbi.2020.103581

Cited by 9 publications

(9 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the limitations of NLP for clinical applications is that concepts can be referenced in multiple forms across different texts. To surmount this challenge, a multi-ontology refined embedding (MORE) was proposed by [8]. This facilitated more accurate clustering of concepts across a wide range of applications, such as analysing patient health records to identify subjects with similar pathologies, to improve interoperability between hospitals.…”

Section: Review Of the Most Existing Methodologiesmentioning

confidence: 99%

“…According to [8,78], extracting accurate information in response to a natural language question (NLQ) remains a challenge for QA systems. This is primarily because the existing techniques used by the system cannot address the effectiveness and efficiency of semantic issues in their NLQ analysis.…”

Section: The Challenges Of the Qa Systemmentioning

confidence: 99%

“…Researchers usually utilize measures of similarity to discover identical notions in free text notes and records. A measure of semantic similarity adopts two notions as input, and then provides a numerical score, which calculates how similar they are in terms of senses [8]. A variety of semantic similarity measures have been developed to describe the strength of relationships between concepts.…”

Section: Introductionmentioning

confidence: 99%

“…Ontology-based semantic similarities usually rely on various graph-based characteristics, for instance, the shortest path length between notions, as well as the location of lowest prevalent ancestors when identifying the similarity in meaning. Such ontology-based methods rely on the quality and totality of fundamental ontologies [8]; nevertheless, maintaining and curating domain ontologies is a challenging and labour-intensive task. It is worth noting that corpus-based approaches as an alternative to identifying ontology-based semantic similarities, are grounded on the co-occurrences and distributional semantics of terms in free texts [9].…”

Section: Introductionmentioning

confidence: 99%

“…Although corpus-based approaches can outperform ontology-based approaches, the literature has shown that the performance gap between both kinds of approaches has narrowed [8,10]. Today, some ontology-based approaches reportedly achieve better performance than the corpus-based approach.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review

Arbaaeen

Shah

2021

Information

View full text Add to dashboard Cite

For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain.

show abstract

Section: Review Of the Most Existing Methodologiesmentioning

confidence: 99%

Section: The Challenges Of the Qa Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations