<div> <p>E-commerce organizations, these days, need to keep striving for constant innovation. Customers have a massive impact on the performance of an organization, so industries need to have solid customer retention strategies. Various big data analytics methodologies are being used by organizations to improve overall online customer experience. While there are multiple techniques available, this research study utilized and tested a framework proposed by Laux et al. (2017), which combines Big Data and Six Sigma methodologies, to the e-commerce domain for identification of issues faced by the customer; this was done by analyzing online product reviews and ratings of customers to provide improvement strategies for enhancing customer experience. </p> <p>Analysis performed on the data showed that approximately 90% of the customer reviews had positive polarity. Among the factors which were identified to have affected the opinions of the customers, the Rating field had the most impact on the sentiments of the users and it was found to be statistically significant. Upon further analysis of reviews with lower rating, the results attained showed that the major issues faced by customers were related to the product itself; most issues were more specifically about the size/fit of the product, followed by the product quality, material used, how the product looked on the online portal versus how it looked in reality, and its price concerning the quality.</p> </div> <br>

Ces dernières années ont témoigné du succès du projet Linked Open Data (LOD) et de la croissance du nombre de sources de données sémantiques disponibles sur le web. Cependant, il y a encore beaucoup de données qui ne sont pas encore mises à disposition dans le LOD telles que les données sur demande, les données de capteurs etc. Elles sont néanmoins fournies par des API des services Web. L'intégration de ces données au LOD ou dans des applications de mashups apporterait une forte valeur ajoutée. Cependant, chercher de tels services avec les outils de découverte de services existants nécessite une connaissance préalable des répertoires de services ainsi que des ontologies utilisées pour les décrire.Dans cette thèse, nous proposons de nouvelles approches et des cadres logiciels pour la recherche de services web sémantiques avec une perspective d'intégration de données. Premièrement, nous introduisons LIDSEARCH, un cadre applicatif piloté par SPARQL pour chercher des données et des services web sémantiques.De plus, nous proposons une approche pour enrichir les descriptions sémantiques de services web en décrivant les relations ontologiques entre leurs entrées et leurs sorties afin de faciliter l'automatisation de la découverte et de la composition de services. Afin d'atteindre ce but, nous utilisons des techniques de traitement automatique de la langue et d'appariement de textes basées sur le deep-learning pour mieux comprendre les descriptions des services.Nous validons notre travail avec des preuves de concept et utilisons les services et les ontologies d'OWLS-TC pour évaluer nos approches proposées de sélection et d'enrichissement. / The last years witnessed the success of the Linked Open Data (LOD) project as well as a significantly growing amount of semantic data sources available on the web. However, there are still a lot of data not being published as fully materialized knowledge bases like as sensor data, dynamic data, data with limited access patterns, etc. Such data is in general available through web APIs or web services. Integrating such data to the LOD or in mashups would have a significant added value. However, discovering such services requires a lot of efforts from developers and a good knowledge of the existing service repositories that the current service discovery systems do not efficiently overcome.In this thesis, we propose novel approaches and frameworks to search for semantic web services from a data integration perspective. Firstly, we introduce LIDSEARCH, a SPARQL-driven framework to search for linked data and semantic web services. Moreover, we propose an approach to enrich semantic service descriptions with Input-Output relations from ontologies to facilitate the automation of service discovery and composition. To achieve such a purpose, we apply natural language processing techniques and deep-learning-based text similarity techniques to leverage I/O relations from text to ontologies.We validate our work with proof-of-concept frameworks and use OWLS-TC as a dataset for conducting our experiments on service search and enrichment.

Information extraction from large corpora can be a useful tool for many applications in industry and academia. For instance, political communication science has just recently begun to use the opportunities that come with the availability of massive amounts of information available through the Internet and the computational tools that natural language processing can provide. We give a linguistically motivated interpretation of topic modeling, a state-of-the-art algorithm for extracting latent semantic sets of words from large text corpora, and extend this interpretation to cover issues and issue-cycles as theoretical constructs coming from political communication science. We build on a dynamic topic model, a model whose semantic sets of words are allowed to evolve over time governed by a Brownian motion stochastic process and apply a new form of analysis to its result. Generally this analysis is based on the notion of volatility as in the rate of change of stocks or derivatives known from econometrics. We claim that the rate of change of sets of semantically related words can be interpreted as issue-cycles, the word sets as describing the underlying issue. Generalizing over the existing work, we introduce dynamic topic models that are driven by general (Brownian motion is a special case of our model) Gaussian processes, a family of stochastic processes defined by the function that determines their covariance structure. We use the above assumption and apply a certain class of covariance functions to allow for an appropriate rate of change in word sets while preserving the semantic relatedness among words. Applying our findings to a large newspaper data set, the New York Times Annotated corpus (all articles between 1987 and 2007), we are able to identify sub-topics in time, \\\\textit{time-localized topics} and find patterns in their behavior over time. However, we have to drop the assumption of semantic relatedness over all available time for any one topic. Time-localized topics are consistent in themselves but do not necessarily share semantic meaning between each other. They can, however, be interpreted to capture the notion of issues and their behavior that of issue-cycles.

<div>Representation learning (RL) for social networks facilitates real-world tasks such as visualization, link prediction and friend recommendation. Many methods have been proposed in this area to learn continuous low-dimensional embedding of nodes, edges or relations in social and information networks. However, most previous network RL methods neglect social signals, such as textual communication between users (nodes). Unlike more typical binary features on edges, such as post likes and retweet actions, social signals are more varied and contain ambiguous information. This makes it more challenging to incorporate them into RL methods, but the ability to quantify social signals should allow RL methods to better capture the implicit relationships among real people in social networks. Second, most previous work in network RL has focused on learning from homogeneous networks (i.e., single type of node, edge, role, and direction) and thus, most existing RL methods cannot capture the heterogeneous nature of relationships in social networks. Based on these identified gaps, this thesis aims to study the feasibility of incorporating heterogeneous information, e.g., texts, attributes, multiple relations and edge types (directions), to learn more accurate, fine-grained network representations. </div><div> </div><div>In this dissertation, we discuss a preliminary study and outline three major works that aim to incorporate textual interactions to improve relational representation learning. The preliminary study learns a joint representation that captures the textual similarity in content between interacting nodes. The promising results motivate us to pursue broader research on using social signals for representation learning. The first major component aims to learn explicit node and relation embeddings in social networks. Traditional knowledge graph (KG) completion models learn latent representations of entities and relations by interpreting them as translations operating on the embedding of the entities. However, existing approaches do not consider textual communications between users, which contain valuable information to provide meaning and context for social relationships. We propose a novel approach that incorporates textual interactions between each pair of users to improve representation learning of both users and relationships. The second major component focuses on analyzing how users interact with each other via natural language content. Although the data is interconnected and dependent, previous research has primarily focused on modeling the social network behavior separately from the textual content. In this work, we model the data in a holistic way, taking into account the connections between the social behavior of users and the content generated when they interact, by learning a joint embedding over user characteristics and user language. In the third major component, we consider the task of learning edge representations in social networks. Edge representations are especially beneficial as we need to describe or explain the relationships, activities, and interactions among users. However, previous work in this area lack well-defined edge representations and ignore the relational signals over multiple views of social networks, which typically contain multi-view contexts (due to multiple edge types) that need to be considered when learning the representation. We propose a new methodology that captures asymmetry in multiple views by learning well-defined edge representations and incorporates textual communications to identify multiple sources of social signals that moderate the impact of different views between users.</div>

We present an algorithm for deciding whether an arbitrary proper probabilistic context-free grammar is consistent, i.e., whether the probability that a derivation terminates is one. Our procedure has time complexity $\\\\mathcal O(n^3)$ in the unit-cost model of computation. Moreover, we develop a novel characterization of consistent probabilistic context-free grammars. A simple corollary of our result is that training methods for probabilistic context-free grammars that are based on maximum-likelihood estimation always yield consistent grammars.

0 VORWORT Bei der maschinellen Übersetzung natürlicher Sprache dominieren mehrere Probleme. Man hat es immer mit sehr großen Datenmengen zu tun. Auch wenn man nur einen kleinen Text übersetzen will, ist diese Aufgabe in umfänglichen Kontext eingebettet, d.h. alles Wissen über Quell- und Zielsprache muß - in möglichst formalisierter Form - zur Verfügung stehen. Handelt es sich um gesprochenes Wort treten Spracherkennungs- und Sprachausgabeaufgaben sowie harte Echtzeitforderungen hinzu. Die Komplexität des Problems ist - auch unter Benutzung moderner Softwareentwicklungskonzepte - für jeden, der eine Implementation versucht, eine nicht zu unterschätzende Herausforderung. Ansätze, die die Arbeitsprinzipien und Methoden der Informatik konsequent nutzen, stellen ihre Ergebnisse meist nur prototyisch für einen sehr kleinen Teil der Sprache -etwa eine Phrase, einen Satz bzw. mehrere Beispielsätze- heraus und folgern mehr oder weniger induktiv, daß die entwickelte Lösung auch auf die ganze Sprache erfolgreich angewendet werden kann, wenn man nur genügend „Lemminge“ hat, die nach allen Seiten ausschwärmend, die „noch notwendigen Routinearbeiten“ schnell und bienenfleißig ausführen könnten.:0 Vorwort S. 2 1 Allgemeiner Ablauf der Generierung S. 3 1.1 AUFGABE DER GENERIERUNG S. 3 1.2 EINORDNUNG DER GENERIERUNG IN DIE MASCHINELLE ÜBERSETZUNG S.4 1.3 REALISIERUNG S. 4 1.4 MORPHOLOGISCHE GENERIERUNG S.6 2 Strukturen und Abbildungen S. 8 2.1 UNIVERSELLE STRUKTUR: DEFINITION VON GRAPHEN S.8 2.2 FORMALISIERUNG SPEZIELLER SEMANTISCHER STRUKTUREN ALS GRAPHEN S.9 2.3 ABBILDUNG VON STRUKTUREN S.11 2.3.1 Strukturtyperhaltende Funktionen S. 12 2.3.2 Strukturtypverändernde Funktionen S. 19 2.3.3 Komplexe Funktionen S. 20 2.3.4 Abbildung eines gesamten Generierungsprozesses S. 21 4 Beispiel: Generierung von Texten aus prädikatenlogischen Ausdrücken (inkrementeller Algorithmus) S. 23 4.1 ABLAUF S.23 4.2 BEISPIELE VON REGELSTRUKTUREN S.27 5 Zusammenfassung S. 28 6 Quellenverzeichnis S. 30

Development and research on Artificial Intelligence have increased during recent years, and the field of medicine is not excluded as a target audience for this top modern technology. Despite new research and tools in favor of medical care, the staff is still under heavy workloads. The goal of this thesis is to analyze and propose the possibility of a chatbot that aims to ease the pressure on the medical staff. To provide a guarantee that patients are being monitored. With Artificial Intelligence, VOIP, Natural Language Processing, and web development, this chatbot can communicate with a patient, which will act as an assistant tool that conducts preparatory work for the medical staff. The system of the chatbot is integrated through a web application where the administrator can initiate call and store clients onto the database. To ascertain that the system operates in real-time, several tests have been carried out to tests concerning the latency between subsystems and the quality of service. / I utvecklingen av intelligenta system har sjukvården etablerat sig som en stor målgrupp. Trots avancerade tekniker så är sjukvården fortfarande under tung belastning. Målet för detta examensarbete är att undersöka möjligheten av en chatbot vars syfte är att lätta på arbetsbelastningen hos sjukvårdspersonalen och samtidigt erbjuda en garanti för att patienter får den tillsyn och återkoppling de behöver. Med hjälp av Artificiell Intelligens, VOIP, Natural Language Processing och webbutveckling kan denna chatbot kommunicera med patienten. Chatboten agerar som ett assisterande verktyg som står för ett förarbete i beslutstagandet för sjukvårdspersonal. Ett systemsom inte bara ger praktisk nytta utan också ett främjande av den utveckling som Artificiell Intelligens gör inom sjukvården. Systemet administreras genom en hemsida som kopplar samman de flera olika komponenterna. Här kan en administratör initiera samtal och spara klienter som ska ringas till databasen. För att kunna fastställa att systemet opererar i realtid har görs flertalet prestandatester avseende både tidsfördröjningar och samtalskvalité.

Developing an Automatic Speech Recognition (ASR) system for the Somali language, though not novel, is not actively explored; hence there has been no success in a model for conversational speech. Neither are related works accessible as open-source. The unavailability of digital data is what labels Somali as a low resource language and poses the greatest impediment to the development of an ASR for Somali. The incentive to develop an ASR system for the Somali language is to contribute to reducing the Maternal Mortality Rate (MMR) in Somalia. Researchers acquire interview audio data regarding maternal health and behaviour in the Somali language; to be able to engage the relevant stakeholders to bring about the needed change, these audios must be transcribed into text, which is an important step towards translation into any language. This work investigates available ASR for Somali and attempts to develop a prototype ASR system to convert Somali audios into Somali text. To achieve this target, we first identified the available open-source systems for speech recognition and selected the DeepSpeech engine for the implementation of the prototype. With three hours of audio data, the accuracy of transcription is not as required and cannot be deployed for use. This we attribute to insufficient training data and estimate that the effort towards an ASR for Somali will be more significant by acquiring about 1200 hours of audio to train the DeepSpeech engine

In today’s society there is a large number of social media users that are free to express their opinion on shared platforms. The socio-cultural differences between the people behind those accounts (in terms of ethnicity, gender, sexual orientation, religion, politics, . . . ) give rise to an important percentage of online discussions that make use of offensive language, which often affects in a negative way the psychological well-being of the victims. In order to address the problem, the endless stream of user-generated content engenders a need to find an accurate and scalable solution to detect offensive language using automated methods. This thesis explores different approaches to the offensiveness detection task focusing on five different languages: Arabic, Danish, English, Greek and Turkish. The results obtained using Support Vector Machines (SVM), Convolutional Neural Networks (CNN) and the Bidirectional Encoder Representations from Transformers (BERT) are compared, achieving state-of-the-art results with some of the methods tested. The effect of the embeddings used, the dataset size, the class imbalance percentage and the addition of sentiment features are studied and analysed, as well as the cross-lingual capabilities of pre-trained multilingual models.

<p>Underground utilities must comply with the requirements stipulated in utility regulations to ensure their structural integrity and avoid interferences and disruptions of utility services. Noncompliance with the regulations could cause disastrous consequences such as pipeline explosion and pipeline contamination that can lead to hundreds of deaths and huge financial loss. However, the current practice of utility compliance checking relies on manual efforts to examine lengthy textual regulations, interpret them subjectively, and check against massive and heterogeneous utility data. It is time-consuming, costly, and error prone. There remains a critical need for an effective mechanism to help identify the regulatory non-compliances in new utility designs or existing pipelines to limit possible negative impacts. Motivated by this critical need, this research aims to create an intelligent, knowledge-based method to automate the compliance checking for underground utilities. </p> <p>The overarching goal is to build semantic intelligence to enable knowledge-based, automated compliance checking of underground utilities by integrating semantic web technologies, natural language processing (NLP), and domain ontologies. Three specific objectives are: (1) designing an ontology-based framework for integrating massive and heterogeneous utility data for automated compliance checking, (2) creating a semi-automated method for utility ontology development, and (3) devising a semantic NLP approach for interpreting textual utility regulations. Objective 1 establishes the knowledge-based skeleton for utility compliance checking. Objectives 2 and 3 build semantic intelligence into the framework resulted from Objective 1 for improved performance in utility compliance checking. </p> <p>Utility compliance checking is the action that examines geospatial data of utilities and their surroundings against textual utility regulations. The integration of heterogeneous geospatial data of utilities as well as textual data remains a big challenge. Objective 1 is dedicated to addressing this challenge. An ontology-based framework has been designed to integrate heterogeneous data and automate compliance checking through semantic, logic, and spatial reasoning. The framework consists of three key components: (1) four interlinked ontologies that provide the semantic schema to represent heterogeneous data, (2) two data convertors to transform data from proprietary formats into a common and interoperable format, and (3) a reasoning mechanism with spatial extensions for detecting non-compliances. The ontology-based framework was tested on a sample utility database, and the results proved its effectiveness.</p> <p>Two supplementary methods were devised to build the semantic intelligence in the ontology-based framework. The first one is a novel method that integrates the top-down strategy and NLP to address two semantic limitations in existing ontologies for utilities: lack of compatibility with existing utility modeling initiatives and relatively small vocabulary sizes. Specifically, a base ontology is first developed by abstracting the modeling information in CityGML Utility Network ADE through a series of semantic mappings. Then, a novel integrated NLP approach is devised to automatically learn the semantics from domain glossaries. Finally, the semantics learned from the glossaries are incorporated into the base ontology to result in a domain ontology for utility infrastructure. For case demonstration, a glossary of water terms was learned to enrich the base ontology (formalized from the ADE) and the resulting ontology was evaluated to be an accurate, sufficient, and shared conceptualization of the domain. </p> <p>The second one is an ontology- and rule-based NLP approach for automated interpretation of textual regulations on utilities. The approach integrates ontologies to capture both domain and spatial semantics from utility regulations that contain a variety of technical jargons/terms and spatial constraints regarding the location and clearance of utility infrastructure. The semantics are then encoded into pattern-matching rules for extracting the requirements from the regulations. An ontology- and deontic logic-based mechanism have also been integrated to facilitate the semantic and logic-based formalization of utility-specific regulatory knowledge. The proposed approach was tested in interpreting the spatial configuration-related requirements in utility accommodation policies, and results proved it to be an effective means for interpreting utility regulations to ensure the compliance of underground utilities. </p> <p>The main outcome of this research is a novel knowledge-based computational platform with semantic intelligence for regulatory compliance checking of underground utilities, which is also the primary contribution of this research. The knowledge-based computational platform provides a declarative way rather than the otherwise procedural/hard-coding implementation approach to automate the overall process of utility compliance checking, which is expected to replace the conventional costly and time-consuming skill-based practice. Utilizing this computational platform for utility compliance checking will help eliminate non-compliant utility designs at the very early stage and identify non-compliances in existing utility records for timely correction, thus leading to enhanced safety and sustainability of the massive utility infrastructure in the U.S.</p>