The Biomaterials Annotator: a system for ontology-based concept annotation of biomaterials text (2021)

Abstract: Biomaterials are synthetic or natural materials used for constructing artificial organs, fabricating prostheses, or replacing tissues. The last century saw the development of thousands of novel biomaterials and, as a result, an exponential increase in scientific publications in the field. Large-scale analysis of biomaterials and their performance could enable data-driven material selection and implant design. However, such analysis requires identification and organization of concepts, such as materials and structures, from published texts. To facilitate future information extraction and the application of machine-learning techniques, we developed a semantic annotator specifically tailored for the biomaterials literature. The Biomaterials Annotator has been implemented following a modular organization using software containers for the different components and orchestrated using Nextflow as workflow manager. Natural language processing (NLP) components are mainly developed in Java. This set-up has allowed named entity recognition of seventeen classes relevant to the biomaterials domain. Here we detail the development, evaluation and performance of the system, as well as the release of the first collection of annotated biomaterials abstracts. We make both the corpus and system available to the community to promote future efforts in the field and contribute towards its sustainability.

## introduction the last two decades saw the field of biomaterials and tissue engineering grow from a small niche of biomedical research to an extensive domain, covering topics such as functional materials, cell-material interaction, nanomaterials and medical devices. the expanding scientific data generated by the field is primarily available in text documents, such as peer-reviewed research papers, patents and conference abstracts. this evergrowing knowledge is increasingly harder for researchers to efficiently discover, organize and use. for example, systematically reviewing the applications and scaffolds made of a commonly used polymer such as poly-lactic-glycolic-acid (plga), requires skimming @xmath0,000+ abstracts (medline search on october 2020). among the different alternatives for the automated processing of available texts, natural language processing (nlp) workflows for information retrieval and indexing offer a much needed automated solution. such computational workflows facilitate information discovery, information extraction and organization, saving researchers time and minimizing manual tasks. central to information retrieval and indexing is the extraction of concepts of interest, also known as named entity recognition (ner). ner is an integral part of nlp workflows as it allows the automated identification of concepts in unstructured text and its assignment to a pre-defined category or class. for example, in the field of biomaterials, categories may include 'biomaterials' ('plga'), 'structures' (such as 'fibre' or 'sponge') and 'tissues' (such as 'tendon' or 'bone'). the use of ner to automatically recognize entities enables several downstream applications, including machine translation, information retrieval and indexing as well as automated question-answering mechanisms. the recognition of concepts in the biomaterials domain is complicated by language and terminology originating from multiple scientific disciplines (chemistry, engineering, biology, medicine). a significant challenge lies in identifying and combining lexical and semantic resources across domains, and thus to date there are no automatic biomaterialsspecific ner systems to detect relevant entities of interest. here, we report the development of the first biomaterials-specific annotation system, designed to recognize named entities from seventeen different categories, reflecting the complexity and diversity of contemporary biomaterials research. when considering approaches for the design of the biomaterials annotator, i.e. lexical versus machine learning-based ner, such as crf or rnn, it was essential to consider the number of desired annotation categories in the system (17) and the absence of an annotated corpora for text mining efforts for the majority of them. based on these premises, it was concluded that training a model for each category was impractical. thus, the system relies on manually curated and validated lexical resources. to cover entities from different domains, multiple nomenclatures, vocabularies, and especially ontologies were identified and combined. to combine these resources into a single instrument, the devices, experimental scaffolds and biomaterials ontology (deb) was used providing the logical schema and the definition of key categories @xcite . the resulting open source-system, the bio-materials annotator, along with an annotated collection of biomaterials literature, are publicly available for use and further development at https://github.com/projectdebbie/ biomaterials_annotator . ## previous relevant work unlike general purpose nlp systems, biomedical domain-specific tools require advanced approaches to detect classes of interest such as diseases and gene names. in this area, there are several wellknown and widely used systems and tools, such as metamap @xcite and @xcite , which were developed using different ner methodologies and approaches, e.g. gazetteers and hand-made rule-based ner; machine learning-based ner that includes hidden markov model, conditional random fields (crf) and recurrent neural network (rnn); and hybrid ner @xcite @xcite @xcite @xcite @xcite @xcite . in the context of this work, generic text mining tools previously developed for the etransafe project @xcite have been adapted and further developed for the biomaterials domain. whilst there are a handful of ontologies in the biomaterials domain (such the nanoparticle ontology npo @xcite and the bone and cartilage tissue engineering ontology bcteo @xcite ), to the best of the our knowledge, the deb ontology @xcite ) is the only one that is tailored to link and curate concepts for biomaterials ner. therefore, it specifically covers different categories related to the biomaterials domain. ## methodology overview to develop the annotation tool, the workflow in figure 1 was followed. various corpora of abstracts were used during the development, covering the general biomaterials literature. these corpora included a collection of manually curated abstracts of the biomedical polymer polydioxanone (fuenteslópez et al 2021, manuscript in preparation, github repository: https://github.com/projectdebbie/ polydioxanone_project ) and a previously published biomaterials gold standard collection @xcite , comprising a total of 1222 abstracts. corpora were passed through four steps, each described in detail below. the first step was a text preprocessing component (section 3.1). this was followed by concept recognition (section 3.2), initially using the mesh controlled vocabulary and the deb ontology. then, the annotations were evaluated by two domain experts, errors were flagged up and additional lexical resources were added through keyword searches. concept recognition, manual evaluation and curation of lexical resources were performed in an iterative manner during the development phase (section 3.3) over 1000 abstracts. once the development phase was completed, validation by domain experts was performed on 199 independent abstracts which were not used during the development process (section 4.1). the resulting annotated collection of biomaterials abstracts was published as open source. ## conclusions and future directions in this work we present the biomaterials annotator, an ontology-based ner system that identifies 17 domain specific types of concepts and delivers an annotated biomaterials corpus of 1222 med-line articles available for future text mining and machine learning efforts. we have carried out a validation activity to measure the performance of the ner system, with the participation of nine biomaterials experts, obtaining a global average f-score of 0.77. future work in the development of the system could involve annotating relations and linking identified concepts to manufactured biomaterials objects. it may also include incorporating additional categories using controlled resources. improvements to the system will continue in an iterative manner aiming to enhanced performance in key categories such as biomaterials and cells. in addition, future versions of the biomaterials annotator will be closely related to the debbie system and include additional functionalities and features developed to achieve its main objectives. the biomaterials annotator and the annotated corpus are open source and available to the community to promote future efforts in the field and contribute towards its sustainability. table: continued annotation category definition resource and imported classes 14 adverse effects an unfavourable or unintended disease, sign, or symptom (including an abnormal laboratory finding) that is temporally associated with the use of a medical device or biomaterial. deb: adverse effects mesh: pathologic function 15 research technique the reported laboratory technique or instrument used in an experimental study. mesh: laboratory procedure, molecular biology research technique deb: research technique ncit: research technique npo: instrument iobc: microscope obi: assay edam: imaging, sample preparation ontotoxnuc: outil 16 effect on biological system the effect associated with manufactured object in a specific test system (cells, tissue or organism). deb: effect on biological system 17 study type the study set up, such as in vitro, in vivo, or clinical. deb: study type