Andrea Giovanni Nuzzolese

A Smart City Data Model based on Semantics Best Practice and Principles

Data management is crucial in modern smart cities. A good data model for smart cities has to be able to describe and integrate data from multiple domains, such as geographic information, public transportation, road maintenance, waste collection, and urban faults management. We describe our approach for creating a semantic platform for the Municipality of Catania, one of the main cities in Southern Italy. The ultimate goal is to boost the metropolis towards the route of a modern smart city and improve urban life. Our platform exhibits a consistent, minimal and comprehensive semantic data model for the city based on the Linked Open Data paradigm. Both the model and the data are publically accessible thorough dedicated user-friendly services, which allow citizens to observe and interact with the work of the public administration. Our platform also enables interested businesses and programmers to develop front-end services on the top of it. We describe the methodology used to extract data from sources, enrich them, building an ontology that describes them and publish them under the Linked Open Data paradigm. We include in our description employed tools and technologies. Our methodology is based on the standards of the W3C, on good practices of ontology design, on the guidelines issued by the Agency for Digital Italy and the Italian Index of Public Administration, as well as on the in-depth experience of the researchers in this field.

From hyperlinks to Semantic Web properties using Open Knowledge Extraction

Open information extraction approaches are useful but insufficient alone for populating the Web with machine read- able information as their results are not directly linkable to, and immediately reusable from, other Linked Data sources. This work proposes a novel paradigm, named Open Knowledge Extraction, and its implementation (Legalo) that performs unsuper- vised, open domain, and abstractive knowledge extraction from text for producing machine readable information. The imple- mented method is based on the hypothesis that hyperlinks (either created by humans or knowledge extraction tools) provide a pragmatic trace of semantic relations between two entities, and that such semantic relations, their subjects and objects, can be revealed by processing their linguistic traces (i.e. the sentences that embed the hyperlinks) and formalised as Semantic Web triples and ontology axioms. Experimental evaluations conducted on validated text extracted from Wikipedia pages, with the help of crowdsourcing, confirm this hypothesis showing high performances. A demo is available at http://wit.istc.cnr.it/stlab-tools/ legalo.

Aemoo: Linked Data exploration based on Knowledge Patterns

This paper presents a novel approach to Linked Data exploration that uses Encyclopedic Knowledge Patterns (EKPs) as relevance criteria for selecting, organising, and visualising knowledge. EKP are discovered by mining the linking structure of Wikipedia and evaluated by means of a user-based study, which shows that they are cognitively sound as models for building entity summarisations. We implemented a tool named Aemoo that supports EKP-driven knowledge exploration and integrates data coming from heterogeneous resources, namely static and dynamic knowledge as well as text and Linked Data. Aemoo is evaluated by means of controlled, task-driven user experiments in order to assess its usability, and ability to provide relevant and serendipitous information as compared to two existing tools: Google and RelFinder.

The Role of Ontology Design Patterns in Linked Data Projects

The contribution of this paper is twofold: (i) a UML stereotype for component diagrams that allows for representing ontologies as a set of interconnected Ontology Design Patterns, aimed at supporting the communication between domain experts and ontology engineers; (ii) an analysis of possible approaches to ontology reuse and the definition of four methods according to their impact on the sustainability and stability of the resulting ontologies and knowledge bases. To conceptually prove the effectiveness of our proposals, we present two real LOD projects.

Producing Linked Data for Smart Cities: the case of Catania

Abstract Semantic Web technologies and in particular Linked Open Data provide a means for sharing knowledge about cities as physical, social, and technical systems, so enabling the development of smart city applications. This paper presents a prototype based on the case of Catania with the aim of sharing the lessons learnt, which can be reused as reference practices in other cases with similar requirements. The importance of achieving syntactic as well as semantic interoperability – as a result of transforming heterogeneous sources into Linked Data – is discussed: semantic interoperability is solved at data level in order to ease further development on top. We present a comprehensive data model for smart cities that integrates several data sources, including, geo-referenced data, public transportation, urban fault reporting, road maintenance and municipal waste collection. We show some novel ontology design patterns for modeling public transportation, urban fault reporting and road maintenance. Domain practitioners and general members of the public have been asked to play with the prototype, and fill out a survey with questions and feedbacks. A computational experiment has been also conducted to evaluate the performance of our data model in terms of practical scalability over increasing data and efficiency under complex queries. All produced data, models, prototype and questionnaire results are publicly accessible online.

Detecting Sentiment Polarities with Sentilo

We present the tool used for the Concept-Level Sentiment Analysis Challenge ESWC-CLSA 2015 Task #1, concerning binary polarity detection of the sentiment of a sentence. Our tool is a little modification of Sentilo [7], an unsupervised, domain-independent system, previously developed by our group, that performs sentiment analysis by hybridizing natural language processing techniques with semantic web technologies. Sentilo is able to recognize the opinion holder and measure the sentiment expressed on topics and sub-topics. The knowledge extracted from the text is represented by means of an RDF graph. Holders and topics are linked to external knowledge. Sentilo is available as a REST service as well as a user-friendly demo.

Analysing and Discovering Semantic Relations in Scholarly Data

Scholarly publishing has seen an ever increasing interest in Linked Open Data (LOD). However, most of the existing datasets are designed as flat translation of legacy data sources into RDF. Although that is a crucial step to address, a lot of useful information is not expressed in RDF, and humans are still required to infer relevant knowledge by reading and making sense of texts. Examples are the reasons why authors cite other papers, the rhetorical structure of scientific discourse, bibliometric measures, provenance information, and so on. In this paper we introduce the Semantic Lancet Project, whose goal is to make available a LOD which includes the formalisation of some useful knowledge hidden within the textual content of papers. We have developed a toolchain for reengineering and enhancing data extracted from some publisher’s legacy repositories. Finally, we show how these data are immediately useful to help humans to address relevant tasks, such as data browsing, expert finding, related works finding, and identification of data inconsistencies.

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This paper presents the Article Content Miner (a.k.a. ACM), i.e., a method for processing the research papers in PDF format available for the 2016 edition of the Semantic Publishing Challenge in order to extract relevant semantic data and publish them in a RDF triplestore according to the Semantic Publishing And Referencing (SPAR) Ontologies (http://www.sparontologies.net). In particular, the extraction of all the information needed for addressing the queries of the second task of the challenge (https://github.com/ceurws/lod/wiki/SemPub16_Task2) is guaranteed by ACM by using techniques based on Natural Language Processing (i.e., Combinatory Categorial Grammar, Discourse Representation Theory, Linguistic Frames), Semantic Web technologies and good Ontology Design practices (i.e., Content Analysis, Ontology Design Patterns, Discourse Referent Extraction and Linking, Topic Extraction).

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This paper presents the Article Content Miner (a.k.a. ACM), i.e., a method for processing the research papers in PDF format available for the 2016 edition of the Semantic Publishing Challenge in order to extract relevant semantic data and publish them in a RDF triplestore according to the Semantic Publishing And Referencing (SPAR) Ontologies (http://www.sparontologies.net). In particular, the extraction of all the information needed for addressing the queries of the second task of the challenge (https://github.com/ceurws/lod/wiki/SemPub16_Task2) is guaranteed by ACM by using techniques based on Natural Language Processing (i.e., Combinatory Categorial Grammar, Discourse Representation Theory, Linguistic Frames), Semantic Web technologies and good Ontology Design practices (i.e., Content Analysis, Ontology Design Patterns, Discourse Referent Extraction and Linking, Topic Extraction).

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This paper presents the Article Content Miner (a.k.a. ACM), i.e., a method for processing the research papers in PDF format available for the 2016 edition of the Semantic Publishing Challenge in order to extract relevant semantic data and publish them in a RDF triplestore according to the Semantic Publishing And Referencing (SPAR) Ontologies (http://www.sparontologies.net). In particular, the extraction of all the information needed for addressing the queries of the second task of the challenge (https://github.com/ceurws/lod/wiki/SemPub16_Task2) is guaranteed by ACM by using techniques based on Natural Language Processing (i.e., Combinatory Categorial Grammar, Discourse Representation Theory, Linguistic Frames), Semantic Web technologies and good Ontology Design practices (i.e., Content Analysis, Ontology Design Patterns, Discourse Referent Extraction and Linking, Topic Extraction).