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ICARUS @WIMS 2020 (June 2020, virtual)

The ICARUS aviation ontology was presented at the 10th international Conference on Web Intelligence, Mining and Semantics (WIMS'20) that was held virtually during June 30th – 3rd July in 2020.

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The ACM 10th International Conference on Web Intelligence, Mining and Semantics
(WIMS ’20), June 30 – July 3, 2020, Biarritz, France
The ICARUS ONTOLOGY:
A general aviation ontology developed using
a multi-layer approach
Dimosthenis Stefanidis, Chrysovalantis Christodoulou, Moysis Symeonidis, George Pallis, Marios D.
Dikaiakos, Loukas Pouis, Kalia Orphanou, Fenareti Lampathaki, Dimitrios Alexandrou
dstefa02@cs.ucy.ac.cy
1
Aviation Industry
2
Airlines
Aviation Data
Airports
More than 98
million terabytes
of data by 20261
4.1 billion passengers
and 56.1 million
tones of freight were
carried in 20172
1 www.flightglobal.com/news/articles/insight-from-flightglobal-the-big-data-landscape-446681
2 https://www.icao.int/annual-report-2017/Pages/the-world-of-air-transport-in-2017.aspx
Aviation Data
 Complex
 Derive from heterogeneous data sources
 Lack of standardization
 Data integration and linking challenge
3
“One of the biggest challenges is to integrate the different data silos, for example weather data, live airspace usage or data from the
airports. There is really no standard and that complicates things. Even internally we are still merging different data sets from different areas in
the company. Insights emerge when you put different departmental data sets together, but at the moment, it is not a smooth process.”
By Rey-Villaverde (Head of Data Science, EasyJet)
Varying data formats
A significant bottleneck with huge cost
Data Integration Problem
 With no common standard, aviation data models can vary along
various dimensions!
 Data providers can use different formats to encode aviation data
e.g. an airline carrier ID field could be stored as a three (IATA) or four-letter code (ICAO)
 Field names assigned to values can be misleading
e.g. a provider may use the name "AT" while another may use "arrTime" for the field "aircraft arrival time"
 Even if two data fields are identical, that doesn't ensure that the data represents the same information
e.g. the "aircraft arrival time“ may correspond to a scheduled or an actual arrival time.
 Data values may be recorded at different temporal frequencies (e.g. once per hour) or spatial regions (e.g.
airspace sectors, geographic regions)
 Measurement units are often omitted in the data storage schemes and can lead to problems when different
units are employed across different systems (e.g. metric vs imperial, feet vs flight level)
4
ICARUS Platform
5
 A novel Big Data platform to deal with
the data integration challenges in the
aviation
 Allows exploration, sharing, trading,
curation, integration and deep analysis
in a trusted manner
 Original and derivative data, characterized
by different volume, velocity and variety
www.icarus2020.aero
The ICARUS Ontology
6

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ICARUS @WIMS 2020 (June 2020, virtual)

  • 1. The ACM 10th International Conference on Web Intelligence, Mining and Semantics (WIMS ’20), June 30 – July 3, 2020, Biarritz, France The ICARUS ONTOLOGY: A general aviation ontology developed using a multi-layer approach Dimosthenis Stefanidis, Chrysovalantis Christodoulou, Moysis Symeonidis, George Pallis, Marios D. Dikaiakos, Loukas Pouis, Kalia Orphanou, Fenareti Lampathaki, Dimitrios Alexandrou dstefa02@cs.ucy.ac.cy 1
  • 2. Aviation Industry 2 Airlines Aviation Data Airports More than 98 million terabytes of data by 20261 4.1 billion passengers and 56.1 million tones of freight were carried in 20172 1 www.flightglobal.com/news/articles/insight-from-flightglobal-the-big-data-landscape-446681 2 https://www.icao.int/annual-report-2017/Pages/the-world-of-air-transport-in-2017.aspx
  • 3. Aviation Data  Complex  Derive from heterogeneous data sources  Lack of standardization  Data integration and linking challenge 3 “One of the biggest challenges is to integrate the different data silos, for example weather data, live airspace usage or data from the airports. There is really no standard and that complicates things. Even internally we are still merging different data sets from different areas in the company. Insights emerge when you put different departmental data sets together, but at the moment, it is not a smooth process.” By Rey-Villaverde (Head of Data Science, EasyJet) Varying data formats A significant bottleneck with huge cost
  • 4. Data Integration Problem  With no common standard, aviation data models can vary along various dimensions!  Data providers can use different formats to encode aviation data e.g. an airline carrier ID field could be stored as a three (IATA) or four-letter code (ICAO)  Field names assigned to values can be misleading e.g. a provider may use the name "AT" while another may use "arrTime" for the field "aircraft arrival time"  Even if two data fields are identical, that doesn't ensure that the data represents the same information e.g. the "aircraft arrival time“ may correspond to a scheduled or an actual arrival time.  Data values may be recorded at different temporal frequencies (e.g. once per hour) or spatial regions (e.g. airspace sectors, geographic regions)  Measurement units are often omitted in the data storage schemes and can lead to problems when different units are employed across different systems (e.g. metric vs imperial, feet vs flight level) 4
  • 5. ICARUS Platform 5  A novel Big Data platform to deal with the data integration challenges in the aviation  Allows exploration, sharing, trading, curation, integration and deep analysis in a trusted manner  Original and derivative data, characterized by different volume, velocity and variety www.icarus2020.aero
  • 7. The ICARUS Ontology  Represents meaningfully entities of the ICARUS Platform e.g. datasets, algorithms, services (a combination of data and ML algorithms), usage statistics, registered experts etc.  Captures structural and semantic characteristics of entities by using semantic annotation of datasets  Extracts metadata from ICARUS Platform operations to construct the ICARUS knowledge-base  Supports continuous integration of new datasets, services, and users into the platform  Supports search, query and linking over multiple data sources and information assets  Feeds the ICARUS recommendation engine with useful information 7
  • 8. ICARUS Ontology - A Multi-layer Approach 8 Meta Contexts and attributes (top-level ontology related to metadata of entities) Domain-specific context and attributes (domain ontologies related to aviation) C C1 C2 C3 C4 Weather Airport Aircraft FlightPassenger Health Top-level ontology for the metadata of entities …
  • 9. ICARUS Ontology - Design Process 9 Expand the domain-level ontologies Capture important concepts, relationships and data fields from aviation stakeholders’ data Integrate existing aviation domain ontology (e.g. NASA ontology1) Create a top-level ontology for describing platform’s concepts Ontology Coding Based on a formal language (OWL) using Protégé. 1 Keller, R. M. (2016, September). Ontologies for aviation data management. In 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC) (pp. 1-9). IEEE.
  • 15. Possible competencies questions that ICARUS Ontology can answer 15  ‘‘Which datasets contain columns about flight delay time?’’  ‘‘Which is the airport departure terminal for a specific flight?’’  ‘‘How many were the occupied seats on a specific flight?’’
  • 17. Scenario: Twitter  Based on Twitter data (e.g. travelers' tweets) that are related to the aviation, the popularity (e.g. sentiment score) of airlines and airports can be found and extracted.  Providing such kind of statistics (e.g. popularity) could help airlines to find:  the most common problems in case of bad flight e.g. late flight, long lines, lost luggage, customer service, etc.  popularity of one airline versus competitors 17
  • 18. Scenario: Twitter 1. Ontology Extension: expand the ICARUS ontology based on the concepts and entities related to Twitter e.g. twitter user account, the number of followers, tweets, etc. 2. Data Collection: retrieve tweets and airlines accounts via the Twitter Streaming API 3. Data Pre-processing: apply data cleaning and natural language processing (NLP) techniques to the retrieved tweets 18
  • 19. Scenario: Twitter 4. Emotions Extraction: perform sentiment analysis (e.g. VADER) on a set of retrieved tweets, by including emotion categories in the ontology 5. Storage: store to the ICARUS ontology (knowledge base) 6. Query KB: Use SPARQL queries to answer possible questions of ICARUS users e.g. "Which airline has the lowest popularity?" (searching for the entity airline with the most negative sentiment based on the stored aggregated statistics) 19
  • 20. Scenario: Recommendations 20  Provide high-quality recommendations of datasets and services to the ICARUS users by utilizing the ICARUS ontology.  We can recommend assets that are connected indirectly to the user’s preferences and needs by:  capturing structural and semantic characteristics of the various ICARUS entities  inferring relationships (e.g. inheritance) between users and assets that were hidden
  • 21. Scenario: Recommendations 21 1. Data Collection: retrieve data related to users and assets of the ICARUS platform (e.g. preferences, interactions like purchases with datasets and services, metadata of datasets, etc.). 2. Storage: store to the ICARUS ontology (knowledge base) 3. Reasoning: apply a reasoning algorithm (Pellet) to reveal hidden relationships (e.g. inheritance)
  • 22. Scenario: Recommendations 22 4. Recommender: Use a weighted-based hybrid recommendation system approach to provide recommendation of datasets and services to each user  Content-Based: Use SPARQL queries to retrieve users’ preferences, geolocation and organization types with the respective information of the given datasets and services  Collaborative Filtering: Use SPARQL queries to retrieve the interplay between users and assets to construct the interaction matrix
  • 23. Scenario: COVID-19  Current challenges of health organizations: locate, collect, explore and integrate reliable data about airline and human mobility, with a sufficient geographical coverage and resolution.  Improving such level of detail would result:  in more accurate epidemic predictions and  a possible estimation of relative revenue losses to be expected in different pandemic scenarios 23
  • 24. Scenario: COVID-19  The ICARUS ontology and the relationships between each entity can be utilized to combine epidemics data with other aviation- related data for data analytics and epidemic forecasts. 1. Ontology Extension: expand the ICARUS ontology based on the concepts and entities related to COVID e.g. mortality rate, cases per city/country, etc. 2. Data Collection: retrieve open data related to COVID-19 and aviation data 24
  • 25. Scenario: COVID-19 3. Storage: store to the ICARUS ontology (knowledge base) 4. Query KB: Use SPARQL queries, aviation-related data and COVID- 19 data to answer possible questions of ICARUS users e.g. "Which datasets can help me predict a virus transmission from incoming flights?" (Find datasets that are related to incoming flights and virus and they are utilized by forecasting services for virus transmission) 25
  • 26. Conclusion  We presented the ICARUS ontology, an aviation domain ontology designed using a multi-layer approach for enabling:  the integration and reasoning over multiple sources of heterogeneous aviation- related data  the semantic description of metadata produced by the ICARUS platform  Main strengths of the proposed ontology:  extendibility and interoperability due to the multi-layer design  ease of use on multiple aviation data sources of different format and structure 26
  • 27. Thank You! The ontology is available here (open source): https://github.com/UCY-LINC-LAB/icarus-ontology Co-funded by the European Commission Horizon 2020 - Grant # 780792 27