ArsMeteo

Acotto, Edoardo; Baldoni, Matteo; Baroglio, Cristina; Patti, Viviana; Portis, Flavio; Vaccarino, Giorgio

doi:10.1145/1557914.1557973

Cited by 5 publications

(5 citation statements)

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“…The pet fish phenomenon is a paradigmatic example of the difficulty to address when building formalisms and systems trying to imitate this combinatorial human ability. In this paper, we exploit the recently introduced nonmonotonic extension of Description Logics T CL (typicality-based compositional logic, introduced in [25]), that is able to account for this type of human-like concept combination 1 . More specifically, we show how it can be used as a tool for the generation of novel compound emotions and, as a consequence, for the suggestion of novel emotion-related contents.…”

Section: Commonsense Concept Invention Via Dynamic Knowledge Combinationmentioning

confidence: 99%

“…In this logic, "typical" properties can be directly specified by means of a "typicality" operator T enriching the underlying Description Logic (from now on, DL for short), and a TBox can contain inclusions of the form T(C) D to represent that 1 Other works have already shown how such logic can be used to model complex cognitive phenomena [25], creative problem solving [26,22] and to build intelligent applications in the field of computational creativity [24]. Alternative approaches to the problem of commonsense conceptual combination have been recently discussed in [13], [21], [10].…”

Section: Commonsense Concept Invention Via Dynamic Knowledge Combinationmentioning

confidence: 99%

“…Concerning rigid properties, we have that all fishes live in water (1). The logic T CL combines the concepts Pet and Fish, by using the latter as the HEAD and the former as the MODIFIER.…”

Section: The Description Logic T CL For Concept Combinationmentioning

confidence: 99%

“…In this section we describe DEGARI, the system exploiting the logic T CL on the Ar-sEmotica knowledge base in order to generate and suggest novel emotion related contents and tested on the RaiPlay catalog 6 , as well as on two artwork datasets: WikiArt Emotions 7 and ArsMeteo [1,5]. DEGARI is implemented in Python and it makes use of the library owlready2 8 for relying on the services of efficient DL reasoners (like HermiT).…”

Section: Degari: Generating Novel Emotions From Arsemoticamentioning

confidence: 99%

“…ArsMeteo [1] is an art portal for sharing artworks and their emerging, connective meanings. Its development is leaded by a non-profit cultural organization called Associazione Culturale ArsMeteo (AMA), based in Turin, Italy.…”

Section: Arsmeteo Datasetmentioning

confidence: 99%

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A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classification

Lieto,

Pozzato,

Zoia

et al. 2021

Preprint

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In this work we present an explainable system for emotion attribution and recommendation (called DEGARI and available here: http://di.unito.it/DEGARI) relying on a recently introduced commonsense reasoning framework (the T CL logic) which is based on a human-like procedure for the automatic generation of novel concepts in a Description Logics knowledge base. Starting from an ontological formalization of emotions based on the Plutchik's model (known as ArsEmotica and available at http://130.192.212.225/fuseki/ArsEmotica-core), the system exploits the logic T CL to automatically generate novel commonsense semantic representations of compound emotions (e.g. Love as derived from the combination of Joy and Trust according to Plutchik's model). The generated emotions corresponds to prototypes, i.e. commonsense representations of given concepts, and have been used to reclassify emotion-related contents in a variety of artistic domains, ranging from art datasets to the editorial content available in RaiPlay, the online multimedia platform of RAI Radiotelevisione Italiana (the Italian public broadcasting company). We have tested our system (i) by reclassifying the available contents in the tested dataset with respect to the new generated compound emotions (ii) with an evaluation, in the form of a controlled user study experiment, of the feasibility of using the obtained reclassifications as recommended emotional content. The obtained results are encouraging and pave the way to many possible further improvements and research directions.

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Section: Commonsense Concept Invention Via Dynamic Knowledge Combinationmentioning

confidence: 99%

Section: Commonsense Concept Invention Via Dynamic Knowledge Combinationmentioning

confidence: 99%

“…Concerning rigid properties, we have that all fishes live in water (1). The logic T CL combines the concepts Pet and Fish, by using the latter as the HEAD and the former as the MODIFIER.…”

Section: The Description Logic T CL For Concept Combinationmentioning

confidence: 99%

Section: Degari: Generating Novel Emotions From Arsemoticamentioning

confidence: 99%

Section: Arsmeteo Datasetmentioning

confidence: 99%

See 3 more Smart Citations

A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classification

Lieto,

Pozzato,

Zoia

et al. 2021

Preprint

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Sentiment Analysis in the Planet Art: A Case Study in the Social Semantic Web

Baldoni

Baroglio

Patti

et al. 2013

New Challenges in Distributed Information Filtering and Retrieval

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Affective computing is receiving increasing attention in many sectors, ranging from advertisement to politics. Its application to the Planet Art, however, is quite at its beginning, especially for what concerns the visual arts. This work, set in a Social Semantic Web framework, explores the possibility of extracting rich, emotional semantic information from the tags freely associated to digitalized visual artworks, identifying the prevalent emotions that are captured by the tags. This is done by means of ArsEmotica, an application software that we developed and that combines an ontology of emotional concepts with available computational and sentiment lexicons. Besides having made possible the enrichment of the ontology with over four-hundred Italian terms, ArsEmotica is able to analyse the emotional semantics of a tagged artwork by working at different levels: not only it can compute a semantic value, captured by tags that can be directly associated to emotional concepts, but it can also compute the semantic value of tags that can be ascribed to emotional concepts only indirectly. The results of a user study, aimed at validating the outcomes of ArsEmotica, are reported and commented. They were obtained by involving the users of the same community which tagged the artworks. It is important to observe that the tagging activity was not performed with the aim of later applying some kind of Sentiment Analysis, but in a pure Web 2.0 approach, i.e. as a form of spontaneous annotation produced by the members of the community on one another's artworks.

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