Semantics-based information extraction for detecting economic events

As today's financial markets are sensitive to breaking news on economic events, accurate and timely automatic identification of events in news items is crucial. Unstructured news items originating from many heterogeneous sources have to be mined in order to extract knowledge useful for guiding decision making processes. Hence, we propose the Semantics-Based Pipeline for Economic Event Detection (SPEED), focusing on extracting financial events from news articles and annotating these with meta-data at a speed that enables real-time use. In our implementation, we use some components of an existing framework as well as new components, e.g., a high-performance Ontology Gazetteer, a Word Group Look-Up component, a Word Sense Disambiguator, and components for detecting economic events. Through their interaction with a domain-specific ontology, our novel, semantically enabled components constitute a feedback loop which fosters future reuse of acquired knowledge in the event detection process

Semantic Knowledge Management and Blockchain-based Privacy for Internet of Things Applications, Journal of Telecommunications and Information Technology, 2022, nr 3

Design of distributed complex systems raises several important challenges, such as: confidentiality, data authentication and integrity, semantic contextual knowledge sharing, as well as common and intelligible understanding of the environment. Among the many challenges are semantic heterogeneity that occurs during dynamic knowledge extraction and authorization decisions which need to be taken when a resource is Accessem in an open, dynamic environment. Blockchain offers the tools to protect sensitive personal data and solve reliability issues by providing a secure communication architecture. However, setting-up blockchain-based applications comes with many challenges, including processing and fusing heterogeneous information from various sources. The ontology model explored in this paper relies on a unified knowledge representation method and thus is the backbone of a distributed system aiming to tackle semantic heterogeneity and to model decentralized management of Access control authorizations.We intertwine the blockchain technology with an ontological model to enhance knowledge management processes for distributed systems. Therefore, rather than reling on the mediation of a third party, the approach enhances autonomous decision-making. The proposed approach collects data generated by sensors into higher-level abstraction using n-ary hierarchical structures to describe entities and actions. Moreover, the proposed semantic architecture relies on hyperledger fabric to ensure the checking and authentication of knowledge integrity while preserving privacy

Modèles sémantiques, raisonnements réactifs et narratifs, pour la gestiondu contexte. : intelligence ambiante et robotique ubiquitaire

With the appearance of the paradigms of ubiquitous systems and ambient intelligence, a new domain of research is emerging with the aim of creating intelligent environments and ecosystems, that can provide multiple services that can improve quality of life, the physical and mental status and the social wellness of the users. In this thesis, weaddress the problem of semantic knowledge representation and reasoning, in the context of ambient intelligent systems and ubiquitous robots. We propose two semantic models that improve the cognitive functions of these systems, in terms of context recognition, and context adaptation. The first one is an ontology-based model, which is associatedwith a rule language to model reactive reasoning process on contextual knowledge. To take into account the dynamicity of context and insure coherent decision-making, this process guarantees two essential properties : decidability and non-monotonic reasoning. The second model is also an ontology-based model that completes the previous model interms of expressiveness for semantic representation of non-trivial contexts with temporal dimension It is based on n-ary relations and a narrative representation of events for inferring causalities between events, and therefore to build the chronological context of a situation as from past and current events. The proposed models have been implemented onthe ubiquitous experimental platform of LISSI, and validated through three scenarios for cognitive assistance and context recognition.Avec l’apparition des paradigmes des systèmes ubiquitaires ou omniprésents, et de l’intelligence ambiante, on assiste à l’émergence d’un nouveau domaine de recherche visant à créer des environnements ou écosystèmes intelligents pouvant offrir une multitude de services permettant d’améliorer la qualité de vie, l’état physique et mental, et lebien-être social des usagers. Dans cette thèse, nous nous focalisons sur la problématique de la représentation sémantique des connaissances et du raisonnement dans le cadre des systèmes à intelligence ambiante et des robots ubiquitaires. Nous proposons deux modèles sémantiques permettant d’améliorer les fonctions cognitives de ces systèmes en termes de gestion du contexte. Au premier modèle, de type ontologique, sont associés un langagede règles et un raisonnement réactif pour la sensibilité au contexte. Pour prendre en compte le caractère dynamique du contexte et assurer une prise de décision cohérente, le mode de raisonnement retenu garantit deux propriétés essentielles : la décidabilité et la non-monotonie. Le deuxième modèle, également de type ontologique, complète le modèle précédent en termes d’expressivité pour la représentation de contextes non-triviaux et/ou liés au temps. Il s’appuie sur des relations n-aires et une représentation narrative des événements pour inférer des causalités entre événements et reconnaître des contextes complexes non-observables à partir d’événements passés et courants. Les modèles proposés ont été mis en œuvre et validés sur la plateforme ubiquitaire d’expérimentation du LISSI àpartir de trois scenarii d’assistance cognitive et de reconnaissance de contexte

Modèles sémantiques et raisonnements réactif et narratif, pour la gestion du contexte en intelligence ambiante et en robotique ubiquitaire

With the appearance of the paradigms of ubiquitous systems and ambient intelligence, a new domain of research is emerging with the aim of creating intelligent environments and ecosystems, that can provide multiple services that can improve quality of life, the physical and mental status and the social wellness of the users. In this thesis, we address the problem of semantic knowledge representation and reasoning, in the context of ambient intelligent systems and ubiquitous robots. We propose two semantic models that improve the cognitive functions of these systems, in terms of context recognition, and context adaptation. The first one is an ontology-based model, which is associated with a rule language to model reactive reasoning process on contextual knowledge. To take into account the dynamicity of context and insure coherent decision-making, this process guarantees two essential properties: decidability and non-monotonic reasoning. The second model is also an ontology-based model that completes the previous model in terms of expressiveness for semantic representation of non-trivial contexts with temporal dimension It is based on n-ary relations and a narrative representation of events for inferring causalities between events, and therefore to build the chronological context of a situation as from past and current events. The proposed models have been implemented on the ubiquitous experimental platform of LISSI, and validated through three scenarios for cognitive assistance and context recognitionAvec l'apparition des paradigmes des systèmes ubiquitaires ou omniprésents et de l'intelligence ambiante, on assiste à l'émergence d'un nouveau domaine de recherche visant à créer des environnements ou écosystèmes intelligents pouvant offrir une multitude de services permettant d'améliorer la qualité de vie, l'état physique et mental, et le bien-être social des usagers. Dans cette thèse, nous nous focalisons sur la problématique de la représentation sémantique des connaissances et du raisonnement dans le cadre des systèmes à intelligence ambiante et des robots ubiquitaires. Nous proposons deux modèles sémantiques permettant d'améliorer les fonctions cognitives de ces systèmes en termes de gestion du contexte. Au premier modèle, de type ontologique, sont associés un langage de règles et un raisonnement réactif pour la sensibilité au contexte. Pour prendre en compte le caractère dynamique du contexte et assurer une prise de décision cohérente, le mode de raisonnement retenu garantit deux propriétés essentielles : la décidabilité et la non-monotonie. Le deuxième modèle, également de type ontologique, complète le modèle précédent en termes d'expressivité pour la représentation de contextes non-triviaux et/ou liés au temps. Il s'appuie sur des relations n-aires et une représentation narrative des événements pour inférer des causalités entre événements et reconnaitre des contextes complexes non-observables à partir d'événements passés et courants. Les modèles proposés ont été mis en oeuvre et validés sur la plateforme ubiquitaire d'expérimentation du LISSI à partir de trois scenarii d'assistance cognitive et de reconnaissance de context

Automated Detection of Financial Events in News Text

Today’s financial markets are inextricably linked with financial events like acquisitions, profit announcements, or product launches. Information extracted from news messages that report on such events could hence be beneficial for financial decision making. The ubiquity of news, however, makes manual analysis impossible, and due to the unstructured nature of text, the (semi-)automatic extraction and application of financial events remains a non-trivial task. Therefore, the studies composing this dissertation investigate 1) how to accurately identify financial events in news text, and 2) how to effectively use such extracted events in financial applications. Based on a detailed evaluation of current event extraction systems, this thesis presents a competitive, knowledge-driven, semi-automatic system for financial event extraction from text. A novel pattern language, which makes clever use of the system’s underlying knowledge base, allows for the definition of simple, yet expressive event extraction rules that can be applied to natural language texts. The system’s knowledge-driven internals remain synchronized with the latest market developments through the accompanying event-triggered update language for knowledge bases, enabling the definition of update rules. Additional research covered by this dissertation investigates the practical applicability of extracted events. In automated stock trading experiments, the best performing trading rules do not only make use of traditional numerical signals, but also employ news-based event signals. Moreover, when cleaning stock data from disruptions caused by financial events, financial risk analyses yield more accurate results. These results suggest that events detected in news can be used advantageously as supplementary parameters in financial applications

Using punctuation as an iconic system for describing and augmenting video structure

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2001.Includes bibliographical references (leaves 98-101).Affordable digital cameras, high bandwidth connectivity and large-scale video hosting websites are combining to offer an alternative mode of production and channel of distribution for independent filmmakers and home moviemakers. There is a growing need to develop systems that meaningfully support the desires of these filmmakers to communicate and collaborate effectively with others and to propel cinematic storytelling into new and dynamic realms. This document proposes the development of a networked software application, called PlusShorts, that will allow a distributed group of users to contribute to and collaborate upon the creation of shared movie sequences. This system introduces an iconic language, consisting of punctuation symbols, for annotating, sharing and interpreting conceptual ideas about cinematic structure. The PlusShorts application presents individual movie sequences as elements within an evolving cinematic storyspace, where participants can explore, collaborate and share ideas.Aisling Geraldine Mary Kelliher.S.M

Grounding the Interaction : Knowledge Management for Interactive Robots

Avec le développement de la robotique cognitive, le besoin d’outils avancés pour représenter, manipuler, raisonner sur les connaissances acquises par un robot a clairement été mis en avant. Mais stocker et manipuler des connaissances requiert tout d’abord d’éclaircir ce que l’on nomme connaissance pour un robot, et comment celle-ci peut-elle être représentée de manière intelligible pour une machine. \ud \ud Ce travail s’efforce dans un premier temps d’identifier de manière systématique les besoins en terme de représentation de connaissance des applications robotiques modernes, dans le contexte spécifique de la robotique de service et des interactions homme-robot. Nous proposons une typologie originale des caractéristiques souhaitables des systèmes de représentation des connaissances, appuyée sur un état de l’art détaillé des outils existants dans notre communauté. \ud \ud Dans un second temps, nous présentons en profondeur ORO, une instanciation particulière d’un système de représentation et manipulation des connaissances, conçu et implémenté durant la préparation de cette thèse. Nous détaillons le fonctionnement interne du système, ainsi que son intégration dans plusieurs architectures robotiques complètes. Un éclairage particulier est donné sur la modélisation de la prise de perspective dans le contexte de l’interaction, et de son interprétation en terme de théorie de l’esprit. \ud \ud La troisième partie de l’étude porte sur une application importante des systèmes de représentation des connaissances dans ce contexte de l’interaction homme-robot : le traitement du dialogue situé. Notre approche et les algorithmes qui amènent à l’ancrage interactif de la communication verbale non contrainte sont présentés, suivis de plusieurs expériences menées au Laboratoire d’Analyse et d’Architecture des Systèmes au CNRS à Toulouse, et au groupe Intelligent Autonomous System de l’université technique de Munich. Nous concluons cette thèse sur un certain nombre de considérations sur la viabilité et l’importance d’une gestion explicite des connaissances des agents, ainsi que par une réflexion sur les éléments encore manquant pour réaliser le programme d’une robotique “de niveau humain”.-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------With the rise of the so-called cognitive robotics, the need of advanced tools to store, manipulate, reason about the knowledge acquired by the robot has been made clear. But storing and manipulating knowledge requires first to understand what the knowledge itself means to the robot and how to represent it in a machine-processable way. \ud \ud This work strives first at providing a systematic study of the knowledge requirements of modern robotic applications in the context of service robotics and human-robot interaction. What are the expressiveness requirement for a robot? what are its needs in term of reasoning techniques? what are the requirement on the robot's knowledge processing structure induced by other cognitive functions like perception or decision making? We propose a novel typology of desirable features for knowledge representation systems supported by an extensive review of existing tools in our community. \ud \ud In a second part, the thesis presents in depth a particular instantiation of a knowledge representation and manipulation system called ORO, that has been designed and implemented during the preparation of the thesis. We elaborate on the inner working of this system, as well as its integration into several complete robot control stacks. A particular focus is given to the modelling of agent-dependent symbolic perspectives and their relations to theories of mind. \ud \ud The third part of the study is focused on the presentation of one important application of knowledge representation systems in the human-robot interaction context: situated dialogue. Our approach and associated algorithms leading to the interactive grounding of unconstrained verbal communication are presented, followed by several experiments that have taken place both at the Laboratoire d'Analyse et d'Architecture des Systèmes at CNRS, Toulouse and at the Intelligent Autonomous System group at Munich Technical University. \ud \ud The thesis concludes on considerations regarding the viability and importance of an explicit management of the agent's knowledge, along with a reflection on the missing bricks in our research community on the way towards "human level robots". \ud \u