scholarly journals Information retrieval and semantic annotation of scientific corpora

2017 ◽  
Author(s):  
Iana Atanassova
Keyword(s):  
Information Retrieval ◽  
Semantic Annotation

scholarly journals Fish Ontology framework for taxonomy-based fish recognition

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3811 ◽  
Author(s):  
Najib M. Ali ◽  
Haris A. Khan ◽  
Amy Y-Hui Then ◽  
Chong Ving Ching ◽  
Manas Gaur ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Fish Species ◽  
Life Science ◽  
Unique Feature ◽  
Semantic Annotation ◽  
Automated Classification ◽  
Current Version ◽  
Fish Taxonomy ◽  
Fisheries Resources ◽  
Indispensable Tool

Life science ontologies play an important role in Semantic Web. Given the diversity in fish species and the associated wealth of information, it is imperative to develop an ontology capable of linking and integrating this information in an automated fashion. As such, we introduce the Fish Ontology (FO), an automated classification architecture of existing fish taxa which provides taxonomic information on unknown fish based on metadata restrictions. It is designed to support knowledge discovery, provide semantic annotation of fish and fisheries resources, data integration, and information retrieval. Automated classification for unknown specimens is a unique feature that currently does not appear to exist in other known ontologies. Examples of automated classification for major groups of fish are demonstrated, showing the inferred information by introducing several restrictions at the species or specimen level. The current version of FO has 1,830 classes, includes widely used fisheries terminology, and models major aspects of fish taxonomy, grouping, and character. With more than 30,000 known fish species globally, the FO will be an indispensable tool for fish scientists and other interested users.

Exploiting Wikipedia in Integrating Semantic Annotation with Information Retrieval

2006 ◽  
pp. 61-70
Author(s):  
Norberto Fernández-García ◽  
José M. Blázquez-del-Toro ◽  
Luis Sánchez-Fernández ◽  
Vicente Luque
Keyword(s):  
Information Retrieval ◽  
Semantic Annotation

scholarly journals Enabling Fast Annotation Process With Table2Annotation Tool

2020 ◽  
Author(s):  
Pierre Larmande ◽  
Kazim Muhammed Jibril
Keyword(s):  
Information Retrieval ◽  
Natural Language ◽  
Semantic Annotation ◽  
Scientific Text ◽  
Complex Concept ◽  
Retrieval Systems ◽  
Annotation Process ◽  
Speed Error ◽  
Semantic Concepts ◽  
Information Retrieval Systems

AbstractSemantic annotation is the process in which semantic concepts are linked to natural language. It helps in boosting the search and access of resources and can be used in information retrieval systems to increase the queries from the user. In this paper, we are interested in identifying ontological concepts in scientific text contained in spreadsheet. We developed a tool which is able to handle various types of spreadsheet. Furthermore, we used the benefits of NCBO Annotator API provided by BioPortal to enhance the semantic annotation functionalities covering spreadsheet data. Table2Annotation developed strengths in certain criteria like speed, error handling and complex concept matching.AvailabilityGitHub : https://github.com/pierrelarmande/ontology-project

scholarly journals Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 200
Author(s):  
Ammar Arbaaeen ◽  
Asadullah Shah
Keyword(s):  
Information Retrieval ◽  
Literature Review ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Annotation ◽  
Closed Domain ◽  
Ontological Approach ◽  
Semantically Enhanced ◽  
Review Reports

For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain.

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