Scholarly

Scholarlydata

This is a description

Ontologies

Core ontology

The conference-ontology is a new self-contained ontology for modelleing knowledge about conferences. The conference-ontology adopts best ontology design practices (e.g., Ontology Design Patterns, ontology reuse and interlinking) and guarantees interoperability with SWC ontology and all other pertinent vocabularies.

Alignments to other scholarly vocabularies

This ontology provides alignments between the core ontology of scholarly data and other ontologies or vocabularies that model knowledge within the domain of scholarly publications.

Datasets

Scholarlydata.org

Scholarlydata dataset is a refactoring of the Semantic Web Dog Food (SWDF), in an effort to keep the dataset growing in good health. We use a novel data model, the conference-ontology, which improves the Semantic Web Conference Ontology, adopting best ontology design practices.

All the current data can be accessed in different formats (i.e., HTML, RDF/XML, Turtle, N-TRIPLES, and JSON-LD) via URI dereferencing, queried via SPARQL or downloaded as single RDF dumps for each conference and workshop.

Smart Cities

PRISMA

PRISMA (PiattafoRme cloud Interoperabili per SMArt-government) is an industrial research project with the aim of providing Public Administrations with an innovative open-source cloud computing platform. The designed cloud platform addresses the specific needs and requirements of Public Administrations, in order to support advanced interoperable services, improve data and process management activities in urban environments, and foster the definition and adoption of new business models. PRISMA can be positioned in the broader context of Smart Cities and Smart Communities, and specifically targets e-Government, e-Health and e-Seismic use cases, with the goal of bringing together cities, industries and citizens to improve urban life through more sustainable integrated solutions.

Ontologies

Prisma ontology

The ontology defining the core classes and properties from the LOD system developed in the project PRISMA for the Municipality of Catania.

Datasets

PRISMA Linked Open Data

Linked Open data of the Semantic Technologies Laboratory of the Institute of Cognitive Sciences and Technologies of the Italian National Research Council , developed in the context of the PRISMA project (PlatfoRms Interoperable cloud for SMArt-government).

Food

Food

The FOOD (FOod in Open Data) project defines and makes available standardization models and reference ontologies for representing food quality certification schemes, in accordance with product specifications defined by the Italian Ministry of Agricultural, Food and Forestry Policies. FOOD focuses on the semantic representation of the information and prodution rules set out in the product specifications for agri-food products and their quality designations, including Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI) schemes.

Ontologies

Food ontology

The design of reference ontologies and the production of open data have been the core activities in the FOOD project. Specifications for agri-food products and their quality designations have driven the definition of a set of OWL ontologies. The ontologies model both quality certification schemes (PDO and PGI) and product categories (e.g., wine, cheese, meat, fruit, etc...) belonging to these certification schemes. Automated and manual data extraction techniques were used to produce RDF datasets (and related metadata) for the defined ontologies, in accordance with the Linked Open Data paradigm. The designed ontologies rely on design patterns and reuse existing ontologies (e.g., AGROVOC); the corresponding datasets are linked to and aligned with datasets available in the Web of Data (e.g., DBpedia, SPCData).

Bread ontology

Cereal ontology

Cheese ontology

Essential oil ontology

Fish ontology

Fresh meat ontology

Fruit ontology

Honey ontology

Liquorice ontology

Mollusc ontology

Oil ontology

Pasta ontology

Ricotta ontology

Saffron ontology

Salt ontology

Salumi ontology

Sweet ontology

Vegetable ontology

Vinegar ontology

Wine ontology

Datasets

Food dataset

Research organization

Data.cnr

data.cnr.it is an initiative of the Italian National Research Council (CNR) aimed at providing an Open Data platform to enable public access to the information of the CNR organization. The platform is designed by the Semantic Technology Laboratory (STLab), which includes researchers and engineers from the Information Systems Office (SI), and the Institute of Cognitive Sciences and Technologies (ISTC).

Ontologies

Data.cnr ontology

The produced OWL ontologies and corresponding datasets model and provide access to heterogeneous, detailed information about the Italian National Research Council's structure and activities, such as people, departments/units, research activities, results and publications.

Datasets

Data.cnr dataset

The data available here is an RDF dump of some of the databanks of CNR.

Assistive and Social Robotics

Mario ontology network

The MARIO Ontology Network (MON) aims at providing MARIO robot with the means for creating, organising, querying and reasoning over a background knowledge base (e.g. to store/retrieve user’s personal information).
MARIO background knowledge consists of: lexical knowledge (e.g. natural language lexica and linguistic frames), domain knowledge (e.g. cga, personal sphere), environmental knowledge (e.g. physical locations and maps), sensor knowledge (e.g. RFID, life mesures), and metadata knowledge (e.g. entity tagging).
The MARIO Ontology Network (MON) is composed of different ontologies that cover different knowledge areas that are relevant to MARIO in order to make it a cognitive agent able to support older patient affected by dementia.
The knowledge areas were identified by analysing the use cases that emerged from the MARIO project.
These uses cases mainly describe actions and behaviours featuring the MARIO robots.
Nevertheless, they also provide us with detailed descriptions about the nature of the knowledge that the robot should deal with in order to perform and select actions and behaviours, respectively.
Currently, the MON consists of 12 knowledge areas and 36 modules.

Ontologies

Action ontology

Activity ontology

Affordance ontology

Calling ontology

Capability assessment ontology

Clinical act ontology

Cohabitation status ontology

Comprehensive geriatric assessment ontology

Cumulative illness rating scale ontology

Drinking ontology

Eating ontology

Emotional sphere ontology

Emotional state ontology

Environment ontology

Ess ontology

Event ontology

Generic ontology

Health role ontology

Health sphere ontology

House environment ontology

Language ontology

Life events ontology

Mario ontology

Marioception ontology

Measurement ontology

Medication use ontology

Mini Nutritional Assessment ontology

Multimedia content ontology

Music ontology

Online account ontology

Open knowledge ontology

Person ontology

Personal sphere ontology

Playing Ontology

Postal address ontology

Prov ontology

Provenance ontology

Reconciliation ontology

Regulatory sphere ontology

Service ontology

Social and multimedia ontology

Spatial ontology

Spatiotemporal ontology

Short portable mental status questionaire ontology

Tagging ontology

Time ontology

Time indexed relationship ontology

Time indexed situation ontology

Vital signs ontology

Chatting ontology

Datasets

Cultural heritage

Cultural Heritage

The project aims at identifying and providing technical solutions, based on standards of the Semantic Web, to enable the integration and rationalisation of MiBACT's data sources related to cultural heritage. To improve availability and reusability of the information assets owned by MiBACT, and to promote open government principles as well, the project started from the so-called DB Unico 2.0 registry, currently available as Open Data of level 3, by opening its content according to the design principles and recommendations of the Italian guidelines on "Semantic interoperability through Linked Open Data" and the W3C Linked Open Data best practices.

Ontologies

Cultural heritage ontology

The main activities of the project included

  • Definition of an OWL ontology for representing the information related to cultural institutes or sites (e.g., museums, historical archives, libraries, monuments, archaeological sites, etc.) and cultural events (e.g., exhibitions, seminars, conferences, etc.). The ontology aims at modelling cultural institutes or sites and all the data that can characterise them. Examples of data are: agents that act on a cultural institute or site, sites, contact points, the multimedia material describing the cultural institute or site, the services and any other information useful to the public in order to access the institute or site. Moreover, the ontology represents events that can take place in specific cultural institutes or sites, modelling all the data regarding possible tickets required to access to the event.
  • Automated extraction of the data from DB Unico 2.0 by means of D2R.
  • Production of the RDF dataset (and the related metadata) by following the Linked Open Data principles.

Datasets

Natural language

Framester

Framester is a hub between FrameNetWordNetVerbNetBabelNetDBpediaYagoDOLCE-Zero, as well as other resources. Framester does not simply creates a strongly connected knowledge graph, but also applies a rigorous formal treatment for Fillmore's frame semantics, enabling full-fledged OWL querying and reasoning on the created joint frame-based knowledge graph.

Ontologies

Framester ontology

Datasets

Framester dataset

Fishery

Mare

Conceived by the Directorate-General for Maritime Affairs and Fisheries (DG MARE) of the European Commission, the project focuses on the establishment of a single information system for fishery and aquaculture products marketed in the European Union. The information system, available as a fully operational multilingual prototype, aims at providing different stakeholders (consumers, producers, fishmongers, processors, importers, retailers, control authorities and other interested parties) with heterogeneous information related to commercial designations of fishery and aquaculture products put on the market in the EU Member States. MARE combines a solid taxonomic and nomenclatural backbone for aquatic species and their scientific names with commercial designations published by each Member State's national authorities in accordance with EU regulations. This core information is complemented with heterogeneous data that include, among the others, production methods, fishing gears, fishing areas, marketing standards, quality certification schemes, fishing opportunities and species distribution.

 

In the context of the MARE project, STLab has defined a modular ontology and produced a corresponding dataset for representing heterogeneous information about fishery and aquaculture products. Dealing with the complexity of the fishery domain and the heterogeneity of the available data sources requires the adoption of a well-designed, consolidated methodology, with the aim of obtaining open, interoperable and reusable models and data.

To favor semantic interoperability and reuse, the MARE ontology exploits well-established ontology design patterns and was designed following a modular approach. In particular, the information about aquatic species marketed in the European Union is structured in three interconnected modules, each focusing on a specific informational dimension or perspective. The identified ontology modules include:

  1. Mare Taxa, which focuses on taxonomic and nomenclatural data for aquatic organisms, such as taxonomic names, synonyms, taxonomic ranks, hierarchical classification, etc.
  2. Mare, which focuses on data related to commercial and legal requirements, such as commercial designations in the EU Member States, production methods and fishing gears, marketing standards, fishing quotas, nutrition facts, etc.
  3. Mare Geo, which focuses on geographical data related to aquatic organisms, such as major fishing areas, species distribution maps, etc.

Following the Semantic Web standars and principles, MARE ontology modules and core concepts are aligned to existing external authoritative ontologies available on the Web, so as to enable semantic interoperability and data linking.

Ontologies

Mare ontology

Mare taxonomic classification ontology

Mare geographical ontology

Datasets