Diego Reforgiato Recupero

Frame-Based Detection of Opinion Holders and Topics: A Model and a Tool

Sentilo is a model and a tool to detect holders and topics of opinion sentences. Sentilo implements an approach based on the neo-Davidsonian assumption that events and situations are the primary entities for contextualizing opinions, which makes it able to distinguish holders, main topics, and sub-topics of an opinion. It uses a heuristic graph mining approach that relies on FRED, a machine reader for the Semantic Web that leverages Natural Language Processing (NLP) and Knowledge Representation (KR) components jointly with cognitively-inspired frames. The evaluation results are excellent for holder detection (F1: 95%), very good for subtopic detection (F1: 78%), and good for topic detection (F1: 68%).

Sentilo: Frame-Based Sentiment Analysis

Abstract Sentilo is an unsupervised, domain-independent system that performs sentiment analysis by hybridizing natural language processing techniques and semantic Web technologies. Given a sentence expressing an opinion, Sentilo recognizes its holder, detects the topics and subtopics that it targets, links them to relevant situations and events referred to by it and evaluates the sentiment expressed on each topic/subtopic. Sentilo relies on a novel lexical resource, which enables a proper propagation of sentiment scores from topics to subtopics, and on a formal model expressing the semantics of opinion sentences. Sentilo provides its output as a RDF graph, and whenever possible it resolves holders’ and topics’ identity on Linked Data.

The Green Abstraction Layer: A Standard Power-Management Interface for Next-Generation Network Devices

In telecommunications networks, distributed power management across heterogeneous hardware requires a standardized representation of each systems capabilities to decouple distributed high-level algorithms from hardware specifics. The Green Abstraction Layer (GAL) provides this interface between high-level algorithms and a lower level representing the hardware and physical resources that directly exert energy management and actions in a network.

Semantic Web Machine Reading with FRED

A machine reader is a tool able to transform natural language text to formal structured knowledge so as the latter can be interpreted by machines, according to a shared semantics. FRED is a machine reader for the semantic web: its output is a RDF/OWL graph, whose design is based on frame semantics. Nevertheless, FRED’s graph are domain and task independent making the tool suitable to be used as a semantic middleware for domainor 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.

Framester: A Wide Coverage Linguistic Linked Data Hub

Semantic web applications leveraging NLP can benefit from easy access to expressive lexical resources such as FrameNet. However, the usefulness of FrameNet is affected by its limited coverage and non-standard semantics. The access to existing linguistic resources is also limited because of poor connectivity among them. We present some strategies based on Linguistic Linked Data to broaden FrameNet coverage and formal linkage of lexical and factual resources. We created a novel resource, Framester, which acts as a hub between FrameNet, WordNet, VerbNet, BabelNet, DBpedia, Yago, DOLCE-Zero, as well as other resources. Framester is not only a strongly connected knowledge graph, but also applies a rigorous formal treatment for Fillmores frame semantics, enabling full-fledged OWL querying and reasoning on a large frame-based knowledge graph. We also describe Word Frame Disambiguation, an application that reuses Framester data as a base in order to perform frame detection from text, with results comparable in precision to the state of the art, but with a much higher coverage.

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.

Uncovering the Semantics of Wikipedia Pagelinks

Wikipedia pagelinks, i.e. links between Wikipages, carry an intended semantics: they indicate the existence of a factual relation between the DBpedia entity referenced by the source Wikipage, and the DBpedia entity referenced by the target Wikipage of the link. These relations are represented in DBpedia as occurrences of the generic ”wikiPageWikilink” property. We designed and implemented a novel method to uncover the intended semantics of pagelinks, and to represent them as semantic relations. In this paper, we test our method on a subset of Wikipedia, showing its potential impact for DBpedia enrichment.

Using FRED for named entity resolution, linking and typing for knowledge base population

FRED is a machine reader for extracting RDF graphs that are linked to LOD and compliant to Semantic Web and Linked Data patterns. We describe the capabilities of FRED as a semantic middleware for semantic web applications. In particular, we will show (i) how FRED recognizes and resolves named entities, (ii) how it links them to existing knowledge base, and (iii) how it gives them a type. Given a sentence in any language, it provides different semantic functionalities (frame detection, topic extraction, named entity recognition, resolution and coreference, terminology extraction, sense tagging and disambiguation, taxonomy induction, semantic role labeling, type induction) by means of a versatile user-interface, which can be recalled as REST Web service. The system can be freely used at http://wit.istc.cnr.it/stlab-tools/fred.

A Semantic Web Based Core Engine to Efficiently Perform Sentiment Analysis

In this paper we present a domain-independent framework that creates a sentiment analysis model by mixing Semantic Web technologies with natural language processing approaches (This work is supported by the project PRISMA SMART CITIES, funded by the Italian Ministry of Research and Education under the program PON.). Our system, called Sentilo, provides a core sentiment analysis engine which fully exploits semantics. It identifies the holder of an opinion, topics and sub-topics the opinion is referred to, and assesses the opinion trigger. Sentilo uses an OWL opinion ontology to represent all this information with an RDF graph where holders and topics are resolved on Linked Data. Anyone can plug its own opinion scoring algorithm to compute scores of opinion expressing words and come up with a combined scoring algorithm for each identified entities and the overall sentence.

Toward a Green Internet

Methods for energy efficiency savings will be needed to meet the growing demands of increasing Internet usage. Information and communication technology (ICT) has been extensively used to monitor energy use in a variety of applications. However, the use of ICT itself has led to huge increases in energy consumption. Today, we are witnessing a rise of energy costs, customer increase, more on-demand services using cloud architectures, mobile Internet, a diffusion of broadband access, and a growing number of services offered by internet service providers (ISP). Consequently, energy efficiency is quickly becoming a high-priority issue for the Internet.