The interpretation of noun noun compounds

This thesis looks at conceptual combination, in particular it investigates how noun noun compounds are interpreted. Several themes run throughout the work. Real compounds (e.g. coat hanger, crab apple) are compared to novel ones (e.g. banjo cactus, zip violin). Also, compounds are examined in each of the possible permutations of artefacts (A) (e.g. coat, banjo) and natural kinds (N) (e.g. crab, cactus), (AA, AN, NA and NN).Experiments 1 - 4 examine noncompositionality in noun noun compounds. Possible sources of noncompositionality are investigated using both feature listing and feature rating tasks. Although some differences were found, results were similar between different types of compound, evidence of noncompositionality being found in each. The results also confirm that most of the meaning of a noun noim compound is derived from the second constituent (noun2).Experiments 5 and 6 look at two different types of compoimd interpretation - slot filling and property mapping. In experiment 5, slot filling is found to be the preferred interpretation type overall, but property mapping is more common in compounds composed of two natural kinds (NN). Experiment 6 examines possible factors influencing the choice between slot filling and property mapping interpretations. It was found that constituent similarity plays an important role, and also that this interacts with whether or not the constituents have important properties which clash. Experiment 7 looks at compound identification. Results suggest that the first constituent (nounl) may be critical in such tasks. Experiment 8 compares the importance of nounl and noun2 in determining the type of interpretation given to a compound. Neither position is found to be more influential than the other, although relational information does seem to be associated with specific nouns in each position. Throughout the thesis findings are related to current theories of conceptual combination, such as prototype models, the concept specialisation model and theories of compound interpretation by analogy

Representing archaeological uncertainty in cultural informatics

This thesis sets out to explore, describe, quantify, and visualise uncertainty in a cultural informatics context, with a focus on archaeological reconstructions. For quite some time, archaeologists and heritage experts have been criticising the often toorealistic appearance of three-dimensional reconstructions. They have been highlighting one of the unique features of archaeology: the information we have on our heritage will always be incomplete. This incompleteness should be reflected in digitised reconstructions of the past. This criticism is the driving force behind this thesis. The research examines archaeological theory and inferential process and provides insight into computer visualisation. It describes how these two areas, of archaeology and computer graphics, have formed a useful, but often tumultuous, relationship through the years. By examining the uncertainty background of disciplines such as GIS, medicine, and law, the thesis postulates that archaeological visualisation, in order to mature, must move towards archaeological knowledge visualisation. Three sequential areas are proposed through this thesis for the initial exploration of archaeological uncertainty: identification, quantification and modelling. The main contributions of the thesis lie in those three areas. Firstly, through the innovative design, distribution, and analysis of a questionnaire, the thesis identifies the importance of uncertainty in archaeological interpretation and discovers potential preferences among different evidence types. Secondly, the thesis uniquely analyses and evaluates, in relation to archaeological uncertainty, three different belief quantification models. The varying ways that these mathematical models work, are also evaluated through simulated experiments. Comparison of results indicates significant convergence between the models. Thirdly, a novel approach to archaeological uncertainty and evidence conflict visualisation is presented, influenced by information visualisation schemes. Lastly, suggestions for future semantic extensions to this research are presented through the design and development of new plugins to a search engine

Application of Machine Learning Algorithms in Hydrocarbon Exploration and Reservoir Characterization

This dissertation presents novel approaches to evaluate complex seismic and well-log data using machine learning algorithms with examples from two different hydrocarbon fields. The applicability of these algorithms for predicting and classifying direct or indirect hydrocarbon indicators are assessed and compared to knowledge-driven methods. The efficacy of the various techniques leads to recommendations for utilizing machine learning algorithms in well planning or later cycle hydrocarbon-field development. In the first study in this dissertation, application of a model-based artificial neural network is compared to the performance of a prestack simultaneous inversion method in predicting hydrocarbon presence in the Heidrun Field, offshore Norway. Low-frequency initial models were used to create 3D Poisson’s ratio models to reflect the fluid within this field and the results were compared based on the accuracy and generalization power of the two methods. The results of both methods confirmed Poisson’s ratio to be a good direct hydrocarbon indicator within the wells used from this field. The direct dependency of the inversion method on the provided input constraints, however, can raise the risk for well planning decisions beyond the known zones. The generalize regression neural network results better matched the observations at the training wells and provided a lower risk of false discoveries in delineating favorable zones beyond the drilled wells. The second study was conducted with the aim of classifying different facies from well logs in wells of the Heidrun Field and in the Kupe Field, offshore New Zealand. Different machine learning approaches were utilized in this study and to investigate quantitatively and qualitatively the accuracy and stability of their predictions. Both supervised methods could successfully predict hydrocarbon-bearing units, with the bagged tree algorithm having a higher overall, and hydrocarbon-related, accuracy rate. Application of the bagged-tree algorithm showed a very low false discovery rate for oil sands and no false discoveries for gas sands in the Heidrun Field. A misclassification of oil sands as brine sands in one Heidrun well is in agreement with relatively high Poisson’s ratios as discussed in the first study. Qualitative investigations of Kupe Field results also demonstrated accurate prediction of hydrocarbon-bearing units, including a shaly hydrocarbon sand class defined for low-quality reservoir sands. Hydrocarbon shows reported in one well that were not predicted by the algorithm, in fact, occur in a very low-porosity section of the reservoir that was not identified as reservoir in reports either. In the last study, the classifications of the litho-fluid facies were extended to three dimensional models using two machine learning methods and were compared with a knowledge-driven approach. The results were examined through a probabilistic approach to reflect the uncertainty of the predicted classes. The probabilistic neural network and the bagged-tree algorithm successfully predicted the variations of litho-fluid facies, especially for hydrocarbon units. Both methods predicted gas sands in certain parts of the field, away from control points, with similar form and lateral dimension. By comparing the results in predicting oil sands and shale, we interpret the bagged-tree method to be more adherent to the known parameters set by the interpreter, such as the OWC and the target classes. Predictions from the probabilistic neural network, however, can deviate from the target facies even close to the wells on which it has been trained. The efficiency of machine learning techniques in increasing the prediction accuracy and decreasing the procedure time, and their objective approach toward the data, make it highly desirable to incorporate them in seismic data analyses. Along with the emphasis on the application of machine learning techniques in the study of subsurface properties, this dissertation presents frameworks for utilizing these techniques as new tools for the interpreter, not as a replacement. The knowledge of the data analyst about the field, and the selection and preparation of the attributes and application of the appropriate algorithm are all crucial factors in this procedure.Release after 01/24/201

Management of data quality when integrating data with known provenance

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Foundations of Fuzzy Logic and Semantic Web Languages

This book is the first to combine coverage of fuzzy logic and Semantic Web languages. It provides in-depth insight into fuzzy Semantic Web languages for non-fuzzy set theory and fuzzy logic experts. It also helps researchers of non-Semantic Web languages get a better understanding of the theoretical fundamentals of Semantic Web languages. The first part of the book covers all the theoretical and logical aspects of classical (two-valued) Semantic Web languages. The second part explains how to generalize these languages to cope with fuzzy set theory and fuzzy logic

Foundations of Fuzzy Logic and Semantic Web Languages

This book is the first to combine coverage of fuzzy logic and Semantic Web languages. It provides in-depth insight into fuzzy Semantic Web languages for non-fuzzy set theory and fuzzy logic experts. It also helps researchers of non-Semantic Web languages get a better understanding of the theoretical fundamentals of Semantic Web languages. The first part of the book covers all the theoretical and logical aspects of classical (two-valued) Semantic Web languages. The second part explains how to generalize these languages to cope with fuzzy set theory and fuzzy logic

Identification of Emerging Scientific Topics in Bibliometric Databases

Bibliometrie, Maschinelles Lernen, LDA, Clustering, Neue Themen Abstract = Frühzeitiges Erkennen von aufkommenden Themengebieten in der Wissenschaft unterstützt sowohl Entscheidungen auf individueller als auch öffentlicher Ebene. Viele bestehende Verfahren beschränken sich auf eine retrospektive (Zitations-)Analyse der Publikationsdaten. Das Ziel der vorliegenden Arbeit war deshalb die Entwicklung eines Verfahrens, das zeitnah und neutral sogenannte "emerging topic candidates" aus einem Set von wissenschaftlichen Publikationen auswählt

Identification of Emerging Scientific Topics in Bibliometric Databases

Bibliometrie, Maschinelles Lernen, LDA, Clustering, Neue Themen Abstract = Frühzeitiges Erkennen von aufkommenden Themengebieten in der Wissenschaft unterstützt sowohl Entscheidungen auf individueller als auch öffentlicher Ebene. Viele bestehende Verfahren beschränken sich auf eine retrospektive (Zitations-)Analyse der Publikationsdaten. Das Ziel der vorliegenden Arbeit war deshalb die Entwicklung eines Verfahrens, das zeitnah und neutral sogenannte "emerging topic candidates" aus einem Set von wissenschaftlichen Publikationen auswählt

SEISMIC ATTRIBUTES ASSISTED QUANTITATIVE UNCONVENTIONAL RESERVOIRS CHARACTERIZATION

Unconventional reservoirs cover a wide range of hydrocarbon-bearing formations and reservoir types that generally do not produce economic rates of hydrocarbons without stimulation. The focus of this dissertation is to develop and calibrate workflows to aid in the characterization of the highly heterogeneous Mississippi Limestone reservoir play of northern Oklahoma. Because natural fractures and faults are the primary pathways for hydrocarbon migration and production in many reservoirs, naturally fractured reservoirs represent a significant percentage of oil and gas reservoirs throughout the world. In 2012, unconventional shale gas productions were accounting for 34% of the total natural gas production in the U.S. (0.68 trillion m3). Because of their complexity and commercial significance, naturally fractured reservoirs have been extensively studied showing that the in-situ stress field, lithology, formation thickness, structural setting and other geological factors all play a role. The understanding of the magnitude, timing, and distribution of these controlling factors could lead to an improvement in the characterization of fractures in reservoirs. Although, the geomechanical history of the rocks is elusive and often speculative, one can infer the magnitude and orientation of paleostresses and thereby hypothesize the degree of fracturing given current structure. Reservoir structure are mapped using petrophysical logs and seismic techniques where seismic attributes such as coherence and curvature are commonly used to map deformation in the subsurface. This dissertation introduces a new edge-detecting seismic attribute, aberrancy that quantitatively estimates the orientation and magnitude of poorly resolved faults as well as flexures in the subsurface. The rate of penetration (ROP) measures drilling speed, which is indicative of the overall time and in general, the cost of the drilling operation process. ROP depends on many engineering factors; however, if these parameters are held constant, ROP is a function of the geology. This dissertation is the first study that links ROP to seismic data and seismic-related attributes using proximal support vector machine. By using this workflow, we anticipate that this process can help better predict a budget or even reduce the cost of drilling when an ROP assessment is made in conjunction with reservoir quality and characteristics. Due to the complexity of fracture characterization, fracture prediction is more commonly the product of comprehensive integration of software, data, and specialize measurements specific to unconventional reservoirs. To calibrate volumetric aberrancy. I integrate seismic attributes and borehole image logs as the input to neural network. The findings on the use of volumetric aberrancy as an aid to structural interpretation and quantitative fracture prediction

Knowledge Representation in Engineering 4.0

This dissertation was developed in the context of the BMBF and EU/ECSEL funded projects GENIAL! and Arrowhead Tools. In these projects the chair examines methods of specifications and cooperations in the automotive value chain from OEM-Tier1-Tier2. Goal of the projects is to improve communication and collaborative planning, especially in early development stages. Besides SysML, the use of agreed vocabularies and on- tologies for modeling requirements, overall context, variants, and many other items, is targeted. This thesis proposes a web database, where data from the collaborative requirements elicitation is combined with an ontology-based approach that uses reasoning capabilities. For this purpose, state-of-the-art ontologies have been investigated and integrated that entail domains like hardware/software, roadmapping, IoT, context, innovation and oth- ers. New ontologies have been designed like a HW / SW allocation ontology and a domain-specific "eFuse ontology" as well as some prototypes. The result is a modular ontology suite and the GENIAL! Basic Ontology that allows us to model automotive and microelectronic functions, components, properties and dependencies based on the ISO26262 standard among these elements. Furthermore, context knowledge that influences design decisions such as future trends in legislation, society, environment, etc. is included. These knowledge bases are integrated in a novel tool that allows for collabo- rative innovation planning and requirements communication along the automotive value chain. To start off the work of the project, an architecture and prototype tool was developed. Designing ontologies and knowing how to use them proved to be a non-trivial task, requiring a lot of context and background knowledge. Some of this background knowledge has been selected for presentation and was utilized either in designing models or for later immersion. Examples are basic foundations like design guidelines for ontologies, ontology categories and a continuum of expressiveness of languages and advanced content like multi-level theory, foundational ontologies and reasoning. Finally, at the end, we demonstrate the overall framework, and show the ontology with reasoning, database and APPEL/SysMD (AGILA ProPErty and Dependency Descrip- tion Language / System MarkDown) and constraints of the hardware / software knowledge base. There, by example, we explore and solve roadmap constraints that are coupled with a car model through a constraint solver.Diese Dissertation wurde im Kontext des von BMBF und EU / ECSEL gefördertem Projektes GENIAL! und Arrowhead Tools entwickelt. In diesen Projekten untersucht der Lehrstuhl Methoden zur Spezifikationen und Kooperation in der Automotive Wertschöp- fungskette, von OEM zu Tier1 und Tier2. Ziel der Arbeit ist es die Kommunikation und gemeinsame Planung, speziell in den frühen Entwicklungsphasen zu verbessern. Neben SysML ist die Benutzung von vereinbarten Vokabularen und Ontologien in der Modellierung von Requirements, des Gesamtkontextes, Varianten und vielen anderen Elementen angezielt. Ontologien sind dabei eine Möglichkeit, um das Vermeiden von Missverständnissen und Fehlplanungen zu unterstützen. Dieser Ansatz schlägt eine Web- datenbank vor, wobei Ontologien das Teilen von Wissen und das logische Schlussfolgern von implizitem Wissen und Regeln unterstützen. Diese Arbeit beschreibt Ontologien für die Domäne des Engineering 4.0, oder spezifischer, für die Domäne, die für das deutsche Projekt GENIAL! benötigt wurde. Dies betrifft Domänen, wie Hardware und Software, Roadmapping, Kontext, Innovation, IoT und andere. Neue Ontologien wurden entworfen, wie beispielsweise die Hardware-Software Allokations-Ontologie und eine domänen-spezifische "eFuse Ontologie". Das Ergebnis war eine modulare Ontologie-Bibliothek mit der GENIAL! Basic Ontology, die es erlaubt, automotive und mikroelektronische Komponenten, Funktionen, Eigenschaften und deren Abhängigkeiten basierend auf dem ISO26262 Standard zu entwerfen. Des weiteren ist Kontextwissen, welches Entwurfsentscheidungen beinflusst, inkludiert. Diese Wissensbasen sind in einem neuartigen Tool integriert, dass es ermöglicht, Roadmapwissen und Anforderungen durch die Automobil- Wertschöpfungskette hinweg auszutauschen. On tologien zu entwerfen und zu wissen, wie man diese benutzt, war dabei keine triviale Aufgabe und benötigte viel Hintergrund- und Kontextwissen. Ausgewählte Grundlagen hierfür sind Richtlinien, wie man Ontologien entwirft, Ontologiekategorien, sowie das Spektrum an Sprachen und Formen von Wissensrepresentationen. Des weiteren sind fort- geschrittene Methoden erläutert, z.B wie man mit Ontologien Schlußfolgerungen trifft. Am Schluss wird das Overall Framework demonstriert, und die Ontologie mit Reason- ing, Datenbank und APPEL/SysMD (AGILA ProPErty and Dependency Description Language / System MarkDown) und Constraints der Hardware / Software Wissensbasis gezeigt. Dabei werden exemplarisch Roadmap Constraints mit dem Automodell verbunden und durch den Constraint Solver gelöst und exploriert