Methods and tools for temporal knowledge harvesting

To extend the traditional knowledge base with temporal dimension, this thesis offers methods and tools for harvesting temporal facts from both semi-structured and textual sources. Our contributions are briefly summarized as follows. 1. Timely YAGO: A temporal knowledge base called Timely YAGO (T-YAGO) which extends YAGO with temporal attributes is built. We define a simple RDF-style data model to support temporal knowledge. 2. PRAVDA: To be able to harvest as many temporal facts from free-text as possible, we develop a system PRAVDA. It utilizes a graph-based semi-supervised learning algorithm to extract fact observations, which are further cleaned up by an Integer Linear Program based constraint solver. We also attempt to harvest spatio-temporal facts to track a person’s trajectory. 3. PRAVDA-live: A user-centric interactive knowledge harvesting system, called PRAVDA-live, is developed for extracting facts from natural language free-text. It is built on the framework of PRAVDA. It supports fact extraction of user-defined relations from ad-hoc selected text documents and ready-to-use RDF exports. 4. T-URDF: We present a simple and efficient representation model for time- dependent uncertainty in combination with first-order inference rules and recursive queries over RDF-like knowledge bases. We adopt the common possible-worlds semantics known from probabilistic databases and extend it towards histogram-like confidence distributions that capture the validity of facts across time. All of these components are fully implemented systems, which together form an integrative architecture. PRAVDA and PRAVDA-live aim at gathering new facts (particularly temporal facts), and then T-URDF reconciles them. Finally these facts are stored in a (temporal) knowledge base, called T-YAGO. A SPARQL-like time-aware querying language, together with a visualization tool, are designed for T-YAGO. Temporal knowledge can also be applied for document summarization.Diese Dissertation zeigt Methoden und Werkzeuge auf, um traditionelle Wissensbasen um zeitliche Fakten aus semi-strukturierten Quellen und Textquellen zu erweitern. Unsere Arbeit lässt sich wie folgt zusammenfassen. 1. Timely YAGO: Wir konstruieren eine Wissensbasis, genannt ’Timely YAGO’ (T-YAGO), die YAGO um temporale Attribute erweitert. Zusätzlich definieren wir ein einfaches RDF-ähnliches Datenmodell, das temporales Wissen unterstützt. 2. PRAVDA: Um eine möglichst große Anzahl von temporalen Fakten aus Freitext extrahieren zu können, haben wir das PRAVDA-System entwickelt. Es verwendet einen auf Graphen basierenden halbüberwachten Lernalgorithmus, um Feststellungen über Fakten zu extrahieren, die von einem Constraint-Solver, der auf einem ganzzahligen linearen Programm beruht, bereinigt werden. Wir versuchen zudem räumlich-temporale Fakten zu extrahieren, um die Bewegungen einer Person zu verfolgen. 3. PRAVDA-live: Wir entwickeln ein benutzerorientiertes, interaktives Wissensextrahiersystem namens PRAVDA-live, das Fakten aus freier, natürlicher Sprache extrahiert. Es baut auf dem PRAVDA-Framework auf. PRAVDA-live unterstützt die Erkennung von benutzerdefinierten Relationen aus ad-hoc ausgewählten Textdokumenten und den Export der Daten im RDF-Format. 4. T-URDF: Wir stellen ein einfaches und effizientes Repräsentationsmodell für zeitabhängige Ungewissheit in Verbindung mit Deduktionsregeln in Prädikatenlogik erster Stufe und rekursive Anfragen über RDF-ähnliche Wissensbasen vor. Wir übernehmen die gebräuchliche Mögliche-Welten-Semantik, bekannt durch probabilistische Datenbanken und erweitern sie in Richtung histogrammähnlicher Konfidenzverteilungen, die die Gültigkeit von Fakten über die Zeit betrachtet darstellen. Alle Komponenten sind vollständig implementierte Systeme, die zusammen eine integrative Architektur bilden. PRAVDA und PRAVDA-live zielen darauf ab, neue Fakten (insbesondere zeitliche Fakten) zu sammeln, und T-URDF gleicht sie ab. Abschließend speichern wir diese Fakten in einer (zeitlichen) Wissensbasis namens T-YAGO ab. Eine SPARQL-ähnliche zeitunterstützende Anfragesprache wird zusammen mit einem Visualisierungswerkzeug für T-YAGO entwickelt. Temporales Wissen kann auch zur Dokumentzusammenfassung genutzt werden

Economic Levers for Mitigating Interest Flooding Attack in Named Data Networking

As a kind of unwelcome, unavoidable, and malicious behavior, distributed denial of service (DDoS) is an ongoing issue in today’s Internet as well as in some newly conceived future Internet architectures. Recently, a first step was made towards assessing DDoS attacks in Named Data Networking (NDN)—one of the promising Internet architectures in the upcoming big data era. Among them, interest flooding attack (IFA) becomes one of the main serious problems. Enlightened by the extensive study on the possibility of mitigating DDoS in today’s Internet by employing micropayments, in this paper we address the possibility of introducing economic levers, say, dynamic pricing mechanism, and so forth, for regulating IFA in NDN

The amendment and empirical test of arbitrage pricing models

The classical APT model is of the form rj − E(rj) = Øj (I − EI ) +ε , where rj − E(rj) is the earning deviation (called basic ariance-profit) of the security j, I is a common factor. This paper considers the impact on the securities return caused by the skewness and kurtosis of the stock returns distributions, and poses a re-modified the arbitrage pricing model as follows rj = E(rj ) + Øj (I − EI ) +θj (I − EI )2 +λj (I − EI )3 +δj (I − EI )4 +ε Based on the regression analysis method, and the fitting degree, one can arrive at this re-modified model has a more reasonable explanation level for securities pricing

A high sensitivity system for luminescence measurement of materials

The authors would like to thank the support of the Fundamental Research Funds for the Central Universities of China, the National Science Foundation of China (No.11205134) and Beijing Higher Education Young Elite Teacher Project (YETP0640). The refurbishment of the RLTLCL system at St Andrews was funded by NERC grant NE/H002715/1.A unique combined and multi-disciplinary wavelength multiplexed spectrometer is described. It is furnished with high-sensitivity imaging plate detectors, the power to which can be gated to provide time-resolved data. The system is capable of collecting spectrally resolved luminescence data following X-ray excitation [radioluminescence (RL) or X-ray excited optical luminescence (XEOL)], electron irradiation [cathodoluminescence (CL)] and visible light from light emitting diodes (LEDs) [photoluminescence (PL)]. Time-resolved PL and CL data can be collected to provide lifetime estimates with half-lives from microsecond timeframes. There are temperature stages for the high and low temperature experiments providing temperature control from 20 to 673 K. Combining irradiation, time resolved (TR) and TR-PL allows spectrally-resolved thermoluminescence (TL) and optically stimulated luminescence (OSL). The design of two detectors with matched gratings gives optimum sensitivity for the system. Examples which show the advantages and multi-use of the spectrometer are listed. Potential future experiments involving lifetime analysis as a function of irradiation, dose and temperature plus pump-probe experiments are discussed.PostprintPostprintPostprintPostprintPeer reviewe

A sensitive and selective fluorescence sensing of bisphenol A based on magnetic Fe3O4@SiO2@QDs@MIPs nanoparticles

887-894A flexible magnetic and fluorescent sensing for the recognition and detection of bisphenol A (BPA) based on a hybrid magnetic Fe3O4@silica nanoparticle@Mn:ZnS quantum dots@molecularly imprinted polymers nanoparticles (Fe3O4@SiO2@QDs@MIPs NPs) has been designed. Because of the high selectivity of MIPs, the strong fluorescence property of Mn:ZnS QDs, good magnetism of Fe3O4 and good water solubility of SiO2, the Fe3O4@SiO2@QDs@MIPs NPs can effectively detect BPA effectively in human urine. In the presence of BPA, a new compound is produced via the amino group of Fe3O4@SiO2@QDs@MIPs NPs and the hydroxyl group of BPA, the energy of the QDs is transferred to the compound, which led to fluorescence quenching. The relative fluorescence intensity has decreased linearly with the increasing of BPA concentration in the range 50 to 700 ng/mL and the detection limit is found to be 1.6 ng/mL. In addition, the sensor has shown a strong selectivity with an imprinting factor of 8.7 and distinguished selectivity is also exhibited to BPA over other possibly competing molecules. On the basis of the merits of the Fe3O4@SiO2@QDs@MIPs NPs, the sensor can be applied to detect BPA in human urine sample successfully

Which Channel to Ask My Question? Personalized Customer Service Request Stream Routing using Deep Reinforcement Learning

Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multiple communication channels to answer customers' questions, for example, chatbot and hotline. On one hand, each channel has limited capacity to respond to customers' requests, on the other hand, customers have different preferences over these channels. The current production systems are mainly built based on business rules, which merely considers tradeoffs between resources and customers' satisfaction. To achieve the optimal tradeoff between resources and customers' satisfaction, we propose a new framework based on deep reinforcement learning, which directly takes both resources and user model into account. In addition to the framework, we also propose a new deep-reinforcement-learning based routing method-double dueling deep Q-learning with prioritized experience replay (PER-DoDDQN). We evaluate our proposed framework and method using both synthetic and a real customer service log data from a large financial technology company. We show that our proposed deep-reinforcement-learning based framework is superior to the existing production system. Moreover, we also show our proposed PER-DoDDQN is better than all other deep Q-learning variants in practice, which provides a more optimal routing plan. These observations suggest that our proposed method can seek the trade-off where both channel resources and customers' satisfaction are optimal.Comment: 13 pages, 7 figure

IntentDial: An Intent Graph based Multi-Turn Dialogue System with Reasoning Path Visualization

Intent detection and identification from multi-turn dialogue has become a widely explored technique in conversational agents, for example, voice assistants and intelligent customer services. The conventional approaches typically cast the intent mining process as a classification task. Although neural classifiers have proven adept at such classification tasks, the issue of neural network models often impedes their practical deployment in real-world settings. We present a novel graph-based multi-turn dialogue system called , which identifies a user's intent by identifying intent elements and a standard query from a dynamically constructed and extensible intent graph using reinforcement learning. In addition, we provide visualization components to monitor the immediate reasoning path for each turn of a dialogue, which greatly facilitates further improvement of the system.Comment: 4pages, 5 figure

Gadolinium Enhancement May Indicate a Condition at Risk of Developing Necrosis in Marchiafava–Bignami Disease: A Case Report and Literature Review

Marchiafava–Bignami disease (MBD) is a rare condition characterized by demyelination, necrosis and atrophy of the corpus callosum (CC), and mainly associated with alcoholism. MBD may present with various clinical manifestations. Brain magnetic resonance imaging (MRI) scan is important in prompt diagnosis and treatment of MBD. Here we reported a case of MBD and reviewed literature about the usage of gadolinium-enhanced MRI in MBD. Gadolinium enhancement may indicate a condition at risk of developing necrosis. We therefore recommend a contrast-enhanced MRI study in severe alcoholics with suspected diagnosis of MBD