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      <h1><a href="https://RED-PAND4.github.io/">KGE-2024-KnowDive-Research-Group-Project</a></h1>


      <p><strong>Authors: </strong> Aloisi Deborah & Giaretta Leonardo</p>

<p>This project resulted from the 2024/2025 Knowledge Graph Engineering class of Prof. Fausto Giunchiglia at UniTn.</p>


<p>This page describes the process and outcome of the engineering of a knowledge graph about “KnowDive Research Group”.
    The graph consists of a knowledge layer and a data layer.
    Entities are instances of the E-type classes, which have their own data properties, as well as object properties which connect them.

    The project has been implemented following the <a href="https://doi.org/10.48550/arxiv.2105.09418">iTelos</a> methodology
    which provides a schema to follow implementing phase after phase and achieving intermediate and eventually final results.
    This is done with the aim to not only create a graph for the specific case in hand, but to also allow for potential reuse and expansion.
</p>
<h3 id="purpose-and-domain-of-interest">Purpose and Domain of Interest</h3>
<p>The main purpose of this project as expressed by the final user is the following:</p>

<p>"A service which helps the users to query and know about all the products and the people that are working or were working in the Knowdive research group at the University of Trento"</p>

<h3 id="Layers">Layers</h3>
This project evaluate the following layers of the iTelos methodology:
<p>• <strong>Language Layer:</strong> any linguistic resource - property or dataset about languages and terms
used - should be explicitly referenced to increase re-usability.</p>

<p>• <strong>Knowledge Layer:</strong> any Knowledge Base encoding information about KG schemas (etypes
and properties). If detailed enough, this layer could be decomposed in its core elements:
ontology and teleology, thus giving the user an unprecedented insight into the conceptual
and relational data shaping behind the actual data values representation.</p>

<p>• <strong>Data layer:</strong> any dataset which consists of data in some format, instantiating the KG’s structure (entities and attributes).</p>

<h3 id="Language-Resources">Language Resources</h3>
<p>In this project, we have used the following language resources:</p>
    <p>• <strong>LiveDataUNITN: </strong><a href="https://datascientiafoundation.github.io/LiveDataUNITN/datasets/DU-UNITN-language/">DU-UNITN-Language</a></p>
    <p>• <strong>KRG-UNITN Language: </strong>the dataset is created for collecting and describing the term used into the  data of the Knowdive Research Group.</p>

<h3 id="Knowledge-Resources">Knowledge Resources</h3>
<p>In this project, we have used the following knowledge resources:</p>
    <p>• <strong>Digital University Ontology: </strong><a href="https://datascientiafoundation.github.io/LiveDataUNITN/datasets/DU-UNITN-ontology/">DU-UNITN-ontology</a></p>
    <p>• <strong>VIVO Core Ontology: </strong><a href="http://liveschema.eu/dataset/lov_vivo">VIVO</a></p>
    <p>• <strong>Academic Institution Internal Structure Ontology: </strong><a href="http://liveschema.eu/dataset/lov_aiiso">AIISO</a></p>
    <p>• <strong>Semantic Web for Research Communities: </strong><a href="http://liveschema.eu/dataset/lov_swrc">SWRC</a></p>


<h3 id="Data-Resources">Data Resources</h3>
<p>In this project, we have used the following data resources:</p>
<p>• <strong>KnowDive Research Group: </strong><a href="https://knowdive.disi.unitn.it/">KnowDive</a></p>
<p>• <strong>Courses: </strong> <a href="https://datascientiafoundation.github.io/LiveDataUNITN/datasets/UNITN-courses/">LiveDataUNITN-courses</a></p>
<p>• <strong>People: </strong> <a href="https://datascientiafoundation.github.io/LiveDataUNITN/datasets/UNITN-people/">LiveDataUNITN-people</a></p>
<p>• <strong>Research paper: </strong> <a href="https://datascientiafoundation.github.io/LiveDataUNITN/datasets/UNITN-papers/">LiveDataUNITN-papers</a></p>


<h3 id="Knowledge-Graph-Engineering"> Knowledge Graph </h3>

<p>The following image shows our final EG obtained using Karma Linker and GraphDB.
<img src="https://github.com/RED-PAND4/RED-PAND4.github.io/blob/main/KG.png?raw=true" alt="alt text" /></p>


<p>You can explore deeper our work in our <a href="https://github.com/RED-PAND4/KGE-2024-KnowDive-Research-Group-Project">public repository</a>.</p>

<p>In our <a href="https://github.com/RED-PAND4/KGE-2024-KnowDive-Research-Group-Project/blob/main/Documentation/KGE-2024-KnowDive-Research-Group-Project_Report.pdf">Project Report</a>, stored in the git repository as well, you will find a detailed step-by-step description in which we tried to
    explain how we managed to produce our KG using the iTelos metodology.</p>


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