A knowledge base for Vitis vinifera functional analysis

Vitis vinifera (Grapevine) is the most important fruit species in the modern world. Wine and table grapes sales contribute significantly to the economy of major wine producing countries. The most relevant goals in wine production concern quality and safety. In order to significantly improve the achievement of these objectives and to gain biological knowledge about cultivars, a genomic approach is the most reliable strategy. The recent grapevine genome sequencing offers the opportunity to study the potential roles of genes and microRNAs in fruit maturation and other physiological and pathological processes. Although several systems allowing the analysis of plant genomes have been reported, none of them has been designed specifically for the functional analysis of grapevine genomes of cultivars under environmental stress in connection with microRNA data

Graph-based Retrieval for Claim Verification over Cross-document Evidence

Verifying the veracity of claims requires reasoning over a large knowledge base, often in the form of corpora of trustworthy sources. A common approach consists in retrieving short portions of relevant text from the reference documents and giving them as input to a natural language inference module that determines whether the claim can be inferred or contradicted from them. This approach, however, struggles when multiple pieces of evidence need to be collected and combined from different documents, since the single documents are often barely related to the target claim and hence they are left out by the retrieval module. We conjecture that a graph-based approach can be beneficial to identify fragmented evidence. We tested this hypothesis by building, over the whole corpus, a large graph that interconnects text portions by means of mentioned entities and exploiting such a graph for identifying candidate sets of evidence from multiple sources. Our experiments show that leveraging on a graph structure is beneficial in identifying a reasonably small portion of passages related to a claim

Semantic web machine reading with FRED

A formal machine reader is a tool able to transform natural language text into formal structured knowledge so as the latter can be interpreted by machines, according to a shared semantics. FRED is a formal machine reader for the semantic web: its output is a RDF/OWL graph, whose design is based on frame semantics. FRED's graph are domain- and task-independent, making the tool suitable to be used as a semantic middleware for domain- or task-specific applications. To serve this purpose, it is available both as REST service and as Python library. This paper provides details about FRED's capabilities, design issues, implementation and evaluation

Producing Linked Data for Smart Cities: The Case of Catania

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 \u2013 as a result of transforming heterogeneous sources into Linked Data \u2013 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

Dialogue Systems and Conversational Agents for Patients with Dementia: The Human-Robot Interaction

This study aimed to identify and describe the fundamental characteristics of spoken dialogue systems, and their role in supporting human-robot interaction and enabling the communication between socially assistive robots and patients with dementia. First, this work provides an overview of spoken dialogue systems by considering the underlying technologies, approaches, methods, and general issues. Then, the analysis focuses on studies, systems, and approaches that have investigated the role of dialogue systems and conversational agents in the interaction with elderly people with dementia by presenting the results of a literature review. While the overview of spoken dialogue systems relies on existing surveys and reviews, a research was conducted to identify existing works in the literature that have investigated the role of conversational agents and dialogue systems in the elderly and people with cognitive impairments. Inclusion criteria were as follows: (1) use of conversational agents, dialogue systems, or language processing tools for people with cognitive impairments; (2) age ≥60 years; (3) diagnosis of dementia according to National Institute on Aging-Alzheimer's Association (NIAAA) criteria; (4) presence of tests or experiments with qualitative or quantitative results. Initially 125 studies published between 2000 and 2017 were identified, of which 12 met the inclusion criteria. The review identifies the issues and challenges that are reported when conversational agents and speech-based interfaces have been used for interacting with people with cognitive impairments. In addition, the review led to the identification of studies that have investigated speech processing and natural language processing capabilities to assess the cognitive status of people with dementia

MARIO: Managing active and healthy aging with use of caRing servIce rObots.

The MARIO project addresses the difficult challenges of loneliness, isolation and dementia in older persons through innovative and multi-faceted inventions delivered by service robots.European Union Horizon 2020 the Framework Programme for Research and Innovation (2014-2020) under grant agreement 643808 Project MARI