Enabling Ontology-Based Search: A Case Study in the Bioinformatics Domain

Kyriakakis, Alexandros; Koumakis, Lefteris; Kanterakis, Alexandros; Iatraki, Galateia; Tsiknakis, Manolis; Potamias, George

doi:10.1109/bibe.2019.00048

Cited by 3 publications

(1 citation statement)

References 47 publications

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“… Function B -Search by enabling semantic proximity. Since the Latent Semantic Indexing proposed by the Information Retrieval field [16], the tendency is rather to exploit knowledge resources of semantic relation between terms supported by ontologies [17] or knowledge graph [18]. Two orientations are then taken, query expansion or enrichment of indexed descriptors.…”

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confidence: 99%

i-DATAQUEST: A Proposal for a Manufacturing Data Query System Based on a Graph

Kim

Yahia

Segonds

et al. 2020

IFIP Advances in Information and Communication Technology

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During the manufacturing product life cycle, an increasing volume of data is generated and stored in distributed resources. These data are heterogeneous, explicitly and implicitly linked and they could be structured and unstructured. The rapid, exhaustive and relevant acquisition of information from this data is a major manufacturing industry issue. The key challenges, in this context, are to transform heterogeneous data into a common searchable data model, to allow semantic search, to detect implicit links between data and to rank results by relevance. To address this issue, the authors propose a query system based on a graph database. This graph is defined based on all the transformed manufacturing data. Besides, the graph is enriched by explicitly and implicitly data links. Finally, the enriched graph is queried thanks to an extended queries system defined by a knowledge graph. The authors depict a proof of concept to validate the proposal. After a partial implementation of this proof of concept, the authors obtain an acceptable result and a needed effort to improve the system response time. Finally, the authors open the topic on the subjects of right management, user profile/customization and data update.

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Section: No No Nomentioning

confidence: 99%

i-DATAQUEST: A Proposal for a Manufacturing Data Query System Based on a Graph

Kim

Yahia

Segonds

et al. 2020

IFIP Advances in Information and Communication Technology

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Converting Biomedical Text Annotated Resources into FAIR Research Objects with an Open Science Platform

et al. 2021

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Today, there are excellent resources for the semantic annotation of biomedical text. These resources span from ontologies, tools for NLP, annotators, and web services. Most of these are available either in the form of open source components (i.e., MetaMap) or as web services that offer free access (i.e., Whatizit). In order to use these resources in automatic text annotation pipelines, researchers face significant technical challenges. For open-source tools, the challenges include the setting up of the computational environment, the resolution of dependencies, as well as the compilation and installation of the software. For web services, the challenge is implementing clients to undertake communication with the respective web APIs. Even resources that are available as Docker containers (i.e., NCBO annotator) require significant technical skills for installation and setup. This work deals with the task of creating ready-to-install and run Research Objects (ROs) for a large collection of components in biomedical text analysis. These components include (a) tools such as cTAKES, NOBLE Coder, MetaMap, NCBO annotator, BeCAS, and Neji; (b) ontologies from BioPortal, NCBI BioSystems, and Open Biomedical Ontologies; and (c) text corpora such as BC4GO, Mantra Gold Standard Corpus, and the COVID-19 Open Research Dataset. We make these resources available in OpenBio.eu, an open-science RO repository and workflow management system. All ROs can be searched, shared, edited, downloaded, commented on, and rated. We also demonstrate how one can easily connect these ROs to form a large variety of text annotation pipelines.

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An automated information extraction system from the knowledge graph based annual financial reports

Mohsin,

Jami,

Wasi

et al. 2024

PeerJ Computer Science

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This article presents a semantic web-based solution for extracting the relevant information automatically from the annual financial reports of the banks/financial institutions and presenting this information in a queryable form through a knowledge graph. The information in these reports is significantly desired by various stakeholders for making key investment decisions. However, this information is available in an unstructured format making it much more complex and challenging to understand and query manually or even through digital systems. Another challenge that makes the understanding of information more complex is the variation of terminologies among financial reports of different banks or financial institutions. The solution presented in this article signifies an ontological approach to solving the standardization problems of the terminologies in this domain. It further addresses the issue of semantic differences to extract relevant data sharing common semantics. Such semantics are then incorporated by implementing their representation as a Knowledge Graph to make the information understandable and queryable. Our results highlight the usage of Knowledge Graph in search engines, recommender systems and question-answering (Q-A) systems. This financial knowledge graph can also be used to serve the task of financial storytelling. The proposed solution is implemented and tested on the datasets of various banks and the results are presented through answers to competency questions evaluated on precision and recall measures.

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Enabling Ontology-Based Search: A Case Study in the Bioinformatics Domain

Cited by 3 publications

References 47 publications

i-DATAQUEST: A Proposal for a Manufacturing Data Query System Based on a Graph

i-DATAQUEST: A Proposal for a Manufacturing Data Query System Based on a Graph

Converting Biomedical Text Annotated Resources into FAIR Research Objects with an Open Science Platform

An automated information extraction system from the knowledge graph based annual financial reports

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