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Building knowledge graph in the transportation domain

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F23%3A00368459" target="_blank" >RIV/68407700:21220/23:00368459 - isvavai.cz</a>

  • Nalezeny alternativní kódy

    RIV/68407700:21260/23:00368459

  • Výsledek na webu

    <a href="http://dx.doi.org/10.1109/SCSP58044.2023.10146125" target="_blank" >http://dx.doi.org/10.1109/SCSP58044.2023.10146125</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/SCSP58044.2023.10146125" target="_blank" >10.1109/SCSP58044.2023.10146125</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Building knowledge graph in the transportation domain

  • Popis výsledku v původním jazyce

    Transportation data are naturally graph-oriented. All our everyday transit decisions are based on oriented paths from A to B in graph topology without us consciously realizing that. So why do we still look at transportation domain data as transaction data, pushing them into relational databases and losing graph analytics opportunities given to us for free? Let's leverage the full potential of transportation data with graph theory, state-of-the-art graph databases, and fast graph algorithms. The paper aims to propose the usage of a modern NoSQL graph-oriented database in the transportation domain. Our goal is to create a transportation knowledge graph: a network of real-world interconnected entities organized in layers. Layers of data from various transportation domains are linked together following the geographical attributes of nodes mapped to the open-street map layer. The ability to see multiple transportation data in one interconnected network is crucial for our next analytical stage. Knowledge graph structure encourages us to analyze the system and solve centrality, pathfinding, or similarity challenges. To demonstrate the application of our approach, we use the PageRank algorithm to explore geographical heat spots based on start and end rides data from student carsharing Uniqway. Uniqway is carsharing made by students of Czech Technical University in Prague (CTU), Czech University of Life Sciences Prague (CZU) and Prague University of Economics and Business (VŠE) for university students and employees. Knowledge graph presents a general structure, and future work can go deeper in various fields such as optimization or machine learning.

  • Název v anglickém jazyce

    Building knowledge graph in the transportation domain

  • Popis výsledku anglicky

    Transportation data are naturally graph-oriented. All our everyday transit decisions are based on oriented paths from A to B in graph topology without us consciously realizing that. So why do we still look at transportation domain data as transaction data, pushing them into relational databases and losing graph analytics opportunities given to us for free? Let's leverage the full potential of transportation data with graph theory, state-of-the-art graph databases, and fast graph algorithms. The paper aims to propose the usage of a modern NoSQL graph-oriented database in the transportation domain. Our goal is to create a transportation knowledge graph: a network of real-world interconnected entities organized in layers. Layers of data from various transportation domains are linked together following the geographical attributes of nodes mapped to the open-street map layer. The ability to see multiple transportation data in one interconnected network is crucial for our next analytical stage. Knowledge graph structure encourages us to analyze the system and solve centrality, pathfinding, or similarity challenges. To demonstrate the application of our approach, we use the PageRank algorithm to explore geographical heat spots based on start and end rides data from student carsharing Uniqway. Uniqway is carsharing made by students of Czech Technical University in Prague (CTU), Czech University of Life Sciences Prague (CZU) and Prague University of Economics and Business (VŠE) for university students and employees. Knowledge graph presents a general structure, and future work can go deeper in various fields such as optimization or machine learning.

Klasifikace

  • Druh

    D - Stať ve sborníku

  • CEP obor

  • OECD FORD obor

    20104 - Transport engineering

Návaznosti výsledku

  • Projekt

  • Návaznosti

    S - Specificky vyzkum na vysokych skolach

Ostatní

  • Rok uplatnění

    2023

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název statě ve sborníku

    2023 Smart City Symposium Prague

  • ISBN

    979-8-3503-2162-3

  • ISSN

    2831-5618

  • e-ISSN

    2691-3666

  • Počet stran výsledku

    4

  • Strana od-do

    1-4

  • Název nakladatele

    IEEE Transactions on Acoustics, Speech, and Signal Processing

  • Místo vydání

    New York

  • Místo konání akce

    Prague

  • Datum konání akce

    25. 5. 2023

  • Typ akce podle státní příslušnosti

    WRD - Celosvětová akce

  • Kód UT WoS článku