Complete Semantics to empower Touristic Service Providers

The tourism industry has a significant impact on the world's economy, contributes 10.2% of the world's gross domestic product in 2016. It becomes a very competitive industry, where having a strong online presence is an essential aspect for business success. To achieve this goal, the proper usage of latest Web technologies, particularly schema.org annotations is crucial. In this paper, we present our effort to improve the online visibility of touristic service providers in the region of Tyrol, Austria, by creating and deploying a substantial amount of semantic annotations according to schema.org, a widely used vocabulary for structured data on the Web. We started our work from Tourismusverband (TVB) Mayrhofen-Hippach and all touristic service providers in the Mayrhofen-Hippach region and applied the same approach to other TVBs and regions, as well as other use cases. The rationale for doing this is straightforward. Having schema.org annotations enables search engines to understand the content better, and provide better results for end users, as well as enables various intelligent applications to utilize them. As a direct consequence, the region of Tyrol and its touristic service increase their online visibility and decrease the dependency on intermediaries, i.e. Online Travel Agency (OTA).Comment: 18 pages, 6 figure

Prototype of a Conversational Assistant for Satellite Mission Operations

The very first artificial satellite, Sputnik, was launched in 1957 marking a new era. Concurrently, satellite mission operations emerged. These start at launch and finish at the end of mission, when the spacecraft is decommissioned. Running a satellite mission requires the monitoring and control of telemetry data, to verify and maintain satellite health, reconfigure and command the spacecraft, detect, identify and resolve anomalies and perform launch and early orbit operations. The very first chatbot, ELIZA was created in 1966, and also marked a new era of Artificial Intelligence Systems. Said systems answer users’ questions in the most diverse domains, interpreting the human language input and responding in the same manner. Nowadays, these systems are everywhere, and the list of possible applications seems endless. The goal of the present master’s dissertation is to develop a prototype of a chatbot for mission operations. For this purpose implementing a Natural Language Processing (NLP) model for satellite missions allied to a dialogue flow model. The performance of the conversational assistant is evaluated with its implementation on a mission operated by the European Space Agency (ESA), implying the generation of the spacecraft’s Database Knowledge Graph (KG). Throughout the years, many tools have been developed and added to the systems used to monitor and control spacecrafts helping Flight Control Teams (FCT) either by maintaining a comprehensive overview of the spacecraft’s status and health, speeding up failure investigation, or allowing to easily correlate time series of telemetry data. However, despite all the advances made which facilitate the daily tasks, the teams still need to navigate through thousands of parameters and events spanning years of data, using purposely built user interfaces and relying on filters and time series plots. The solution presented in this dissertation and proposed by VisionSpace Technologies focuses on improving operational efficiency whilst dealing with the mission’s complex and extensive databases.O primeiro satélite artificial, Sputnik, foi lançado em 1957 e marcou o início de uma nova era. Simultaneamente, surgiram as operações de missão de satélites. Estas iniciam com o lançamento e terminam com desmantelamento do veículo espacial, que marca o fim da missão. A operação de satélites exige o acompanhamento e controlo de dados de telemetria, com o intuito de verificar e manter a saúde do satélite, reconfigurar e comandar o veículo, detetar, identificar e resolver anomalias e realizar o lançamento e as operações iniciais do satélite. Em 1966, o primeiro Chatbot foi criado, ELIZA, e também marcou uma nova era, de sistemas dotados de Inteligência Artificial. Tais sistemas respondem a perguntas nos mais diversos domínios, para tal interpretando linguagem humana e repondendo de forma similar. Hoje em dia, é muito comum encontrar estes sistemas e a lista de aplicações possíveis parece infindável. O objetivo da presente dissertação de mestrado consiste em desenvolver o protótipo de um Chatbot para operação de satélites. Para este proposito, criando um modelo de Processamento de Linguagem Natural (NLP) aplicado a missoões de satélites aliado a um modelo de fluxo de diálogo. O desempenho do assistente conversacional será avaliado com a sua implementação numa missão operada pela Agência Espacial Europeia (ESA), o que implica a elaboração do grafico de conhecimentos associado à base de dados da missão. Ao longo dos anos, várias ferramentas foram desenvolvidas e adicionadas aos sistemas que acompanham e controlam veículos espaciais, que colaboram com as equipas de controlo de missão, mantendo uma visão abrangente sobre a condição do satélite, acelerando a investigação de falhas, ou permitindo correlacionar séries temporais de dados de telemetria. No entanto, apesar de todos os progressos que facilitam as tarefas diárias, as equipas ainda necessitam de navegar por milhares de parametros e eventos que abrangem vários anos de recolha de dados, usando interfaces para esse fim e dependendo da utilização de filtros e gráficos de series temporais. A solução apresentada nesta dissertação e proposta pela VisionSpace Technologies tem como foco melhorar a eficiência operacional lidando simultaneamente com as suas complexas e extensas bases de dados

Chatbots for Modelling, Modelling of Chatbots

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-03-202

Choosing a Chatbot Development Tool

© 2021 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksChatbots are programs that supply services to users via conversation in natural language, acting as virtual assistants within social networks or web applications. Here, we review the most representative chatbot development tools with a focus on technical and managerial aspectsThis work was partially funded by the R&D program of the Madrid Region (project FORTE, S2018/TCS4314), and the Spanish Ministry of Science (project MASSIVE, RTI2018-095255-B-I00

Scalable and Quality-Aware Training Data Acquisition for Conversational Cognitive Services

Dialog Systems (or simply bots) have recently become a popular human-computer interface for performing user's tasks, by invoking the appropriate back-end APIs (Application Programming Interfaces) based on the user's request in natural language. Building task-oriented bots, which aim at performing real-world tasks (e.g., booking flights), has become feasible with the continuous advances in Natural Language Processing (NLP), Artificial Intelligence (AI), and the countless number of devices which allow third-party software systems to invoke their back-end APIs. Nonetheless, bot development technologies are still in their preliminary stages, with several unsolved theoretical and technical challenges stemming from the ambiguous nature of human languages. Given the richness of natural language, supervised models require a large number of user utterances paired with their corresponding tasks -- called intents. To build a bot, developers need to manually translate APIs to utterances (called canonical utterances) and paraphrase them to obtain a diverse set of utterances. Crowdsourcing has been widely used to obtain such datasets, by paraphrasing the initial utterances generated by the bot developers for each task. However, there are several unsolved issues. First, generating canonical utterances requires manual efforts, making bot development both expensive and hard to scale. Second, since crowd workers may be anonymous and are asked to provide open-ended text (paraphrases), crowdsourced paraphrases may be noisy and incorrect (not conveying the same intent as the given task). This thesis first surveys the state-of-the-art approaches for collecting large training utterances for task-oriented bots. Next, we conduct an empirical study to identify quality issues of crowdsourced utterances (e.g., grammatical errors, semantic completeness). Moreover, we propose novel approaches for identifying unqualified crowd workers and eliminating malicious workers from crowdsourcing tasks. Particularly, we propose a novel technique to promote the diversity of crowdsourced paraphrases by dynamically generating word suggestions while crowd workers are paraphrasing a particular utterance. Moreover, we propose a novel technique to automatically translate APIs to canonical utterances. Finally, we present our platform to automatically generate bots out of API specifications. We also conduct thorough experiments to validate the proposed techniques and models