Towards Interoperable BioNLP Semantic Web Services Using the SADI Framework

Bukhari, Syed Ahmad Chan; Klein, Artjom; Baker, Christopher J. O.

doi:10.1007/978-3-642-39437-9_6

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…The ontology specifies an explicit relationship (semantic predicate) describing the functionality of a service between the I/O, for example “getDrugNamebyDocument”. 65 The service descriptions are collected in a SADI registry. From there, SADI Services can be readily discovered and composed into workflows, as all services consume and generate RDF (syntactic interoperability) and thus the output of one SADI service can be directly consumed by any other SADI service.…”

Section: Semantic Domain Modellingmentioning

confidence: 99%

Perspectives on automated composition of workflows in the life sciences

et al. 2021

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Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.

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Section: Semantic Domain Modellingmentioning

confidence: 99%

Perspectives on automated composition of workflows in the life sciences

et al. 2021

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“…By example the applications developed by the Dbpedia community (http://wiki.dbpedia.org/Applications). Thereby, different platforms offer semantic annotation (Bukhari et al, 2013;Golbeck et al, 2002;Hu and Du, 2013), although in most cases they require complex infrastructure because they are part of a framework. In addition, there are a range of tools that offer similar solutions related to keyword research (Vállez, 2011), but most of them are based exclusively on statistical techniques to provide the proposed keywords, without taking into account the content structure and specific domain.…”

Section: Digidoc Metaeditmentioning

confidence: 99%

Section: Digidoc Metaeditmentioning

confidence: 99%

A semi-automatic indexing system based on embedded information in HTML documents

et al. 2015

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Purpose -This paper describes and evaluates the tool DigiDoc MetaEdit which allows the semi-automatic indexing of HTML documents. The tool works by identifying and suggesting keywords from a thesaurus according to the embedded information in HTML documents. This enables the parameterization of keyword assignment based on how frequently the terms appear in the document, the relevance of their position, and the combination of both.Design/methodology/approach -In order to evaluate the efficiency of the indexing tool, the descriptors/keywords suggested by the indexing tool are compared to the keywords which have been indexed manually by human experts. To make this comparison a corpus of HTML documents are randomly selected from a journal devoted to Library and Information Science.Findings -The results of the evaluation show that there: (1) is close to a 50% match or overlap between the two indexing systems, however if you take into consideration the related terms and the narrow terms the matches can reach 73%; and (2) the first terms identified by the tool are the most relevant.Originality/value -The tool presented identifies the most important keywords in an HTML document based on the embedded information in HTML documents. Nowadays, representing the contents of documents with keywords is an essential practice in areas such as information retrieval and e-commerce.

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“…Likewise, searching for a particular inventor will show further inventors with similar inventions and awards. Although several applications using semantic technologies and natural language processing are in amalgamation to answer questions in natural language have been developed, however, a majority of them operate with smaller datasets [20], [21]. Facebook introduced a semantic search engine with a natural language querying interface [22].…”

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confidence: 99%