Semedico: A Comprehensive Semantic Search Engine for the Life Sciences

Faessler, Erik; Hahn, Udo

doi:10.18653/v1/p17-4016

Cited by 8 publications

(3 citation statements)

References 15 publications

(15 reference statements)

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“…Medline ranker [8], MScanner [11], eTBlast [12], PubFocus [13] and BabelMeSH [17] tools application is on medline database while RefMed [7], MiSearch [9], Semantic Medline [10], McSyBi [14], GoPubMed [15], PubNet [16], HubMed [18], Semedico [19] application is on PubMed Journal.…”

Section: Related Workmentioning

confidence: 99%

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Latent Drug Recognition Using Fuzzy Matching in Sequential Analysis of Full-Text Health Related Publications: Case of a Health Dissemination tool in a developing country

Mulunda

Wagacha

Muchemi

2023

Preprint

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Health related discoveries are mainly published as journal publications and the rate at which they are generated increases as new information and discoveries emerge. Discovery of latent medically-related terms in a document corpus is a challenging task where the researcher is not an expert in that domain and a viable database of medicine related words is not readily available. The study focused on investigating methodologies and best practises that will enable discovery of latent drug terms found in health publications corpus for effective dissemination at county and national levels. Fuzzy matching methodology was considered for its near and exact matching algorithms. DrugBank dataset was chosen as reference for drug terms because of its comprehensive list of drugs, that are frequently updated and freely accessible. Semi-supervised learning was applied in modelling of multi-search medical terms on an hourly basis. drug name Recognition, Sentence Categorization and Information Retrieval are among the features described in the presented model.

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Section: Related Workmentioning

confidence: 99%

“…MiSearch [9], Semantic Medline [10], MScanner [11], eTBlast [12], PubFocus [13], McSyBi [14], GoPubMed [15], PubNet [16], Babelmesh [17], HubMed [18], Semedico [19]. Google scholar [20] and Scopus [21] web crawler is applicable to various domain area.…”

Section: Introductionmentioning

confidence: 99%

Latent Drug Recognition Using Fuzzy Matching in Sequential Analysis of Full-Text Health Related Publications: Case of a Health Dissemination tool in a developing country

Mulunda

Wagacha

Muchemi

2023

Preprint

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“…Thus, metadata files are gathered from two data sources with very different characteristics ( BEFChina and data.world ). The Semedico search engine ( Faessler and Hahn 2017 ) retrieves relevant abstracts from PubMed , a source with more than 32M abstracts. To ensure the relevance of the crawled data from Semedico, we have followed an iterative way of revision.…”

Section: Introductionmentioning

confidence: 99%

BiodivNERE: Gold standard corpora for named entity recognition and relation extraction in the biodiversity domain

Abdelmageed¹,

Löffler²,

Feddoul³

et al. 2022

BDJ

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Biodiversity is the assortment of life on earth covering evolutionary, ecological, biological, and social forms. To preserve life in all its variety and richness, it is imperative to monitor the current state of biodiversity and its change over time and to understand the forces driving it. This need has resulted in numerous works being published in this field. With this, a large amount of textual data (publications) and metadata (e.g. dataset description) has been generated. To support the management and analysis of these data, two techniques from computer science are of interest, namely Named Entity Recognition (NER) and Relation Extraction (RE). While the former enables better content discovery and understanding, the latter fosters the analysis by detecting connections between entities and, thus, allows us to draw conclusions and answer relevant domain-specific questions. To automatically predict entities and their relations, machine/deep learning techniques could be used. The training and evaluation of those techniques require labelled corpora. In this paper, we present two gold-standard corpora for Named Entity Recognition (NER) and Relation Extraction (RE) generated from biodiversity datasets metadata and abstracts that can be used as evaluation benchmarks for the development of new computer-supported tools that require machine learning or deep learning techniques. These corpora are manually labelled and verified by biodiversity experts. In addition, we explain the detailed steps of constructing these datasets. Moreover, we demonstrate the underlying ontology for the classes and relations used to annotate such corpora.

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