Semantic annotation in biomedicine: the current landscape

Abstract

The abundance and unstructured nature of biomedical texts, be it clinical or research content, impose significant challenges for the effective and efficient use of information and knowledge stored in such texts. Annotation of biomedical documents with machine intelligible semantics facilitates advanced, semantics-based text management, curation, indexing, and search. This paper focuses on annotation of biomedical entity mentions with concepts from relevant biomedical knowledge bases such as UMLS. As a result, the meaning of those mentions is unambiguously and explicitly defined, and thus made readily available for automated processing. This process is widely known as semantic annotation, and the tools that perform it are known as semantic annotators.Over the last dozen years, the biomedical research community has invested significant efforts in the development of biomedical semantic annotation technology. Aiming to establish grounds for further developments in this area, we review a selected set of state of the art biomedical semantic annotators, focusing particularly on general purpose annotators, that is, semantic annotation tools that can be customized to work with texts from any area of biomedicine. We also examine potential directions for further improvements of today’s annotators which could make them even more capable of meeting the needs of real-world applications. To motivate and encourage further developments in this area, along the suggested and/or related directions, we review existing and potential practical applications and benefits of semantic annotators.

DES-ROD: Exploring Literature to Develop New Links between RNA Oxidation and Human Diseases

“…It has been acknowledged that automated systems are needed to search for and retrieve useful information from such voluminous data. Thus, several automated systems have been developed using text mining (TM) and/or natural language processing (NLP) for over 30 years [16][17][18][19][20][21][22][23]. Moreover, TM and NLP methods have been combined with different approaches for knowledge extraction from free text.…”

Enabling Fast Annotation Process With Table2Annotation Tool

“…The challenges faced in annotation can be tackled by two methods which are term-to-concept matching method which has to do with matching some parts of provided texts to structured knowledge databases, dictionaries or vocabularies and machine learning (ML) method which has to do with creating annotators for specific purposes and usage instead of general usage [4].…”

“…• Text Variation. According to [4], challenges are faced also due to the fact that there are different kinds of biomedical texts and variations among variations of text for example in biomedical and clinical text. • Disambiguation.…”

“…Semantic annotation helps in boosting the search and access of resources. From [4], Semantic annotation can be used in information retrieval systems to increase the queries from the user with some ontology terms and also provide grouping of documents gotten based on specific contents. In Biomedical resources like there are a lot of abbreviations in the texts which makes it hard for researches to comprehend.…”

Beyond MeSH: Fine-Grained Semantic Indexing of Biomedical Literature Based on Weak Supervision

“…Specific challenges and sources of noise in this process include identification of multi-word terms, term inflection, and term disambiguation in case of homonymous concepts. Significant efforts by the community of biomedical natural language processing have led to a range of proposed methods and tools for automatically identifying the occurrence of biomedical concepts [23]. In the context of this work, MetaMap, one of the most popular and comprehensive tools in the field, is employed for concept-occurrence extraction.…”