Introdução a ontologias e suas aplicações

Different vocabularies and contexts are barriers to the communication between people or software systems. It is necessary a common understanding in the domain that is talked about, so it can be obtained a correct interpretation of the information. An ontology formally models the structure of a domain and turn explicit the shared understanding in the form of concepts and relations that emerge from its observation. Constitutes a sort of framework used in the mapping to the meaning of the information that is talked about. The formal accuracy in which they are defined, by means of axioms, allow machine processing, implicating in systems interoperability. Structured this way, the knowledge is easily transferred between people or systems from different contexts. Ontologies present several applications nowadays. They are considered the infra-structure to the Semantic Web, which is composed by Web resources with embedded meaning. Thereby, the automatic execution of complex tasks is allowed, with the benefit from the effective communication between Web software agents. Among other applications, they also have been used to structure the knowledge generated from several areas, like Biology and Software Engineering.

Inteligência cibernética e uso de recursos semânticos na detecção de perfis falsos no contexto do Big Data

Pós-graduação em Ciência da Informação - FFC

SNIIC: a plataforma digital de cultura brasileira sob a perspectiva das convergências entre o Design e a Ciência da Informação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Inteligência cibernética e uso de recursos semânticos na detecção de perfis falsos no contexto do Big Data

Pós-graduação em Ciência da Informação - FFC

SNIIC: a plataforma digital de cultura brasileira sob a perspectiva das convergências entre o Design e a Ciência da Informação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Unveiling the relationship between complex networks metrics and word senses

The automatic disambiguation of word senses (i.e., the identification of which of the meanings is used in a given context for a word that has multiple meanings) is essential for such applications as machine translation and information retrieval, and represents a key step for developing the so-called Semantic Web. Humans disambiguate words in a straightforward fashion, but this does not apply to computers. In this paper we address the problem of Word Sense Disambiguation (WSD) by treating texts as complex networks, and show that word senses can be distinguished upon characterizing the local structure around ambiguous words. Our goal was not to obtain the best possible disambiguation system, but we nevertheless found that in half of the cases our approach outperforms traditional shallow methods. We show that the hierarchical connectivity and clustering of words are usually the most relevant features for WSD. The results reported here shed light on the relationship between semantic and structural parameters of complex networks. They also indicate that when combined with traditional techniques the complex network approach may be useful to enhance the discrimination of senses in large texts. Copyright (C) EPLA, 2012

Frame-driven Extraction of Linked Data and Ontologies from Text

Ontology design and population -core aspects of semantic technologies- re- cently have become fields of great interest due to the increasing need of domain-specific knowledge bases that can boost the use of Semantic Web. For building such knowledge resources, the state of the art tools for ontology design require a lot of human work. Producing meaningful schemas and populating them with domain-specific data is in fact a very difficult and time-consuming task. Even more if the task consists in modelling knowledge at a web scale. The primary aim of this work is to investigate a novel and flexible method- ology for automatically learning ontology from textual data, lightening the human workload required for conceptualizing domain-specific knowledge and populating an extracted schema with real data, speeding up the whole ontology production process. Here computational linguistics plays a fundamental role, from automati- cally identifying facts from natural language and extracting frame of relations among recognized entities, to producing linked data with which extending existing knowledge bases or creating new ones. In the state of the art, automatic ontology learning systems are mainly based on plain-pipelined linguistics classifiers performing tasks such as Named Entity recognition, Entity resolution, Taxonomy and Relation extraction [11]. These approaches present some weaknesses, specially in capturing struc- tures through which the meaning of complex concepts is expressed [24]. Humans, in fact, tend to organize knowledge in well-defined patterns, which include participant entities and meaningful relations linking entities with each other. In literature, these structures have been called Semantic Frames by Fill- 6 Introduction more [20], or more recently as Knowledge Patterns [23]. Some NLP studies has recently shown the possibility of performing more accurate deep parsing with the ability of logically understanding the structure of discourse [7]. In this work, some of these technologies have been investigated and em- ployed to produce accurate ontology schemas. The long-term goal is to collect large amounts of semantically structured information from the web of crowds, through an automated process, in order to identify and investigate the cognitive patterns used by human to organize their knowledge.

Interfaccia web per un sistema di condivisione semantica dell'informazione: studio e implementazione

Questa tesi progettuale nasce per integrare gli sforzi attuali sullo sviluppo del web semantico. La piattaforma di riferimento sulla quale è stato svolto il presente lavoro è SMART-M3. Questa piattaforma mette a disposizione uno spazio condiviso di informazioni, rappresentate e accessibili secondo le tecnologie del web semantico. In questo scenario, nasce la necessità di disporre di un'interfaccia web capace di interagire con la piattaforma - in grado di risolvere la complessità intrinseca dei dati semantici - allo scopo di averne un completo controllo; ricerche precedenti a questo proposito hanno dato come frutto una libreria PHP che mi è stata consegnata come strumento per lo sviluppo dell'interfaccia. La tesi si è articolata in 3 fasi principali: una fase iniziale di documentazione sull'argomento, eseguita principalmente sul libro “A developer's guide to the semantic web” di Liyang Yu e sulla tesi “Ontologie per il web semantico: un'analisi comparativa.” di Indrit Beqiri; una seconda fase, quella principale, di sviluppo del progetto informatico; una terza fase, infine, di sviluppo di questo elaborato di tesi, da considerarsi come la trattazione di tutto il percorso soprascritto, dall'inizio alla fine, secondo l'ordine cronologico in cui si svolto l'intero processo della tesi.

Un approccio per la concettualizzazione di insiemi di documenti

Introduzione a tecniche di web semantico e realizzazione di un approccio in grado di ricreare un ambiente familiare di un qualsiasi motore di ricerca con funzionalità semantico-lessicali e possibilità di estrazione, in base ai risultati di ricerca, dei concetti e termini chiave che costituiranno i relativi gruppi di raccolta per i vari documenti con argomenti in comune.

Machine Learning applicato al Web Semantico: Statistical Relational Learning vs Tensor Factorization

Obiettivo della tesi è analizzare e testare i principali approcci di Machine Learning applicabili in contesti semantici, partendo da algoritmi di Statistical Relational Learning, quali Relational Probability Trees, Relational Bayesian Classifiers e Relational Dependency Networks, per poi passare ad approcci basati su fattorizzazione tensori, in particolare CANDECOMP/PARAFAC, Tucker e RESCAL.

Searching and retrieving in content-based repositories of formal mathematical knowledge

In this thesis, the author presents a query language for an RDF (Resource Description Framework) database and discusses its applications in the context of the HELM project (the Hypertextual Electronic Library of Mathematics). This language aims at meeting the main requirements coming from the RDF community. in particular it includes: a human readable textual syntax and a machine-processable XML (Extensible Markup Language) syntax both for queries and for query results, a rigorously exposed formal semantics, a graph-oriented RDF data access model capable of exploring an entire RDF graph (including both RDF Models and RDF Schemata), a full set of Boolean operators to compose the query constraints, fully customizable and highly structured query results having a 4-dimensional geometry, some constructions taken from ordinary programming languages that simplify the formulation of complex queries. The HELM project aims at integrating the modern tools for the automation of formal reasoning with the most recent electronic publishing technologies, in order create and maintain a hypertextual, distributed virtual library of formal mathematical knowledge. In the spirit of the Semantic Web, the documents of this library include RDF metadata describing their structure and content in a machine-understandable form. Using the author's query engine, HELM exploits this information to implement some functionalities allowing the interactive and automatic retrieval of documents on the basis of content-aware requests that take into account the mathematical nature of these documents.

BIRD-A: una piattaforma per la creazione di interfacce Web basate su ontologie per l'authoring di dati RDF

Lo scopo del progetto Bird-A è di mettere a disposizione uno strumento basato su ontologie per progettare un'interfaccia web collaborativa di creazione, visualizzazione, modifica e cancellazione di dati RDF e di fornirne una prima implementazione funzionante. La visione che sta muovendo la comunità del web semantico negli ultimi anni è quella di creare un Web basato su dati strutturati tra loro collegati, più che su documenti. Questo modello di architettura prende il nome di Linked Data ed è basata sulla possibilità di considerare cose, concetti, persone come risorse identificabili tramite URI e di poter fornire informazioni e descrivere collegamenti tra queste risorse attraverso l'uso di formati standard come RDF. Ciò che ha però frenato la diffusione di questi dati strutturati ed interconnessi sono stati gli alti requisiti di competenze tecniche necessarie sia alla loro creazione che alla loro fruizione. Il progetto Bird-A si prefigge di semplificare la creazione e la fruizione di dati RDF, favorendone la condivisione e la diffusione anche fra persone non dotate di conoscenze tecniche specifiche.

Linked services for M2M communication with Enterprise IT systems

Linking the physical world to the Internet, also known as the Internet of Things, has increased available information and services in everyday life and in the Enterprise world. In Enterprise IT an increasing number of communication is done between IT backend systems and small IoT devices, for example sensor networks or RFID readers. This introduces some challenges in terms of complexity and integration. We are working on the integration of IoT devices into Enterprise IT by leveraging SOA techniques and Semantic Web technologies. We present a SOA based integration platform for connecting WSNs and large enterprise business processes. For ensuring interoperability our platform is based on Linked Services. These are thoroughly described, machine-readable, machine-reasonable service descriptions.

Fuzzy Web Knowledge Aggregation, Representation, and Reasoning for Online Privacy and Reputation Management

A social Semantic Web empowers its users to have access to collective Web knowledge in a simple manner, and for that reason, controlling online privacy and reputation becomes increasingly important, and must be taken seriously. This chapter presents Fuzzy Cognitive Maps (FCM) as a vehicle for Web knowledge aggregation, representation, and reasoning. With this in mind, a conceptual framework for Web knowledge aggregation, representation, and reasoning is introduced along with a use case, in which the importance of investigative searching for online privacy and reputation is highlighted. Thereby it is demonstrated how a user can establish a positive online presence.

The FORA Framework - A Fuzzy Grassroots Ontology for Online Reputation Management

Online reputation management deals with monitoring and influencing the online record of a person, an organization or a product. The Social Web offers increasingly simple ways to publish and disseminate personal or opinionated information, which can rapidly have a disastrous influence on the online reputation of some of the entities. The author focuses on the Social Web and possibilities of its integration with the Semantic Web as resource for a semi-automated tracking of online reputations using imprecise natural language terms. The inherent structure of natural language supports humans not only in communication but also in the perception of the world. Thereby fuzziness is a promising tool for transforming those human perceptions into computer artifacts. Through fuzzy grassroots ontologies, the Social Semantic Web becomes more naturally and thus can streamline online reputation management. For readers interested in the cross-over field of computer science, information systems, and social sciences, this book is an ideal source for becoming acquainted with the evolving field of fuzzy online reputation management in the Social Semantic Web area. ​