This project builds a knowledge graph (KG) that provides insights into the relationship between tourism and waste management in the Province of Trento, Italy. The KG supports stakeholders such as tourists, facility owners, policymakers, and researchers by offering:
Locations of waste bins and recycling facilities.
Waste types and disposal methods.
Analysis of waste production trends, particularly in tourist areas.
Evaluation of waste-related policies and practices
The project is developed using iTelos Methodology
iTelos is a methodology designed to facilitate the reuse of existing knowledgeβontologies, schemas, and datasetsβby guiding the development of Knowledge Graphs (KGs) through a purpose-driven, middle-out approach.Β
Its aim is to make both the developed applications and their knowledge components reusable and shareable.
It consists of 5 phases: Purpose definition, Information Gathering, Language Definition, Knowledge Definition and Entity Definition.Β
Purpose Definition
Defined the projectβs informal goal: exploring the relationship between tourism and waste management in Trentino.
Identified user scenarios and personas (e.g., tourists, policymakers, facility owners).
Six personas were selected representing stakeholders (tourist, facility owner, researcher, analyst, etc.)
Realistic use cases listed (e.g., ski tourists needing recycling info, policy-makers analyzing pandemic waste trends).
Formulated Competency Questions (CQs) to drive the design of the Knowledge Graph.
Information Gathering
Collected, cleaned, and standardized data from various open sources:
OpenStreetMap (waste bins, tourist sites)
Dolomiti Ambiente (waste categories)
ISTAT (population data)
ISPRA (waste production statistics)
Created intermediate datasets and merged them based on shared identifiers like municipality codes.
Language Definition
Defined formal concepts, properties, and data values using:
UKC (Universal Knowledge Core) for generic terms.
OpenStreetMap tags for domain-specific terminology.
Introduced custom definitions where existing vocabularies were insufficient (e.g., geometry, waste disposal types).
Knowledge Definition
Modeled the teleology (bottom-up) and teleontology (top-down) of the domain.
Combined both into a final teleontology using a middle-out approach.
Used ProtΓ©gΓ© for ontology modeling and created schema alignment with reference ontologies like schema.org.
Entity Definition
Linked real-world data to the ontology using the Karma tool.
Ensured each entity had unique identifiers and consistent relationships.
Exported the final Knowledge Graph in RDF Turtle (TTL) format.
Tourists needing to locate recycling bins (e.g., Maximilian at Monte Bondone).
Facility owners complying with waste regulations (e.g., Luciana).
Policymakers analyzing tourismβs impact on waste (e.g., Diego).
Researchers studying COVID-19 effects on waste generation (e.g., Chiara).
Where are the nearest recycling bins for glass/plastic?
What types of waste are most common in ski resorts?
How did COVID-19 impact waste generation?
What special waste disposal facilities exist in Trentino?
OpenStreetMap (OSM): For geospatial data on bins, facilities, and tourist attractions.
Dolomiti Ambiente: For waste type and disposal method guidelines.
ISPRA: For annual waste production statistics.
ISTAT: For municipality population data.
Overpass Turbo: For querying OSM and extracting detailed points of interest.
Entities: Person, Municipality, Location, TouristAttraction, Waste, WasteBasket, WasteProduction, WasteDisposalType.
Properties: Names, coordinates, categories, population size, disposal methods, etc.
Teleontology: Modeled with ProtΓ©gΓ©, extended schema.org and domain-specific classes.
SPARQL Queries: Executed to verify the KG's ability to answer competency questions.
Successfully integrated and cleaned multiple open datasets.
Designed and implemented a rich knowledge graph schema.
Evaluated for purpose coverage (~70%) and full ontology reusability (100% for referenced ontology classes).
Good connectivity (Entity Connectivity Score = 9.5).
OSM data sparsity for some bin types.
Lack of standardized waste policy data across municipalities.
Potential improvements include:
Integrating real-time data (e.g., smart bin sensors).
Extending coverage to additional regions.
Developing an API or web tool for live querying.
ProtΓ©gΓ© β Ontology modeling
Karma β Entity mapping
Overpass Turbo β OSM querying
GraphDB - To Import the KG and run SPARQL Queries
SPARQL β Querying the KG
Python (Pandas, GeoPandas) β Data cleaning
GitHub β Project and code repository
Professor at University of Trento, Dept. of Information Engineering and Computer Science
Research Fellow at University of Trento