Ontologien für wissensbasierte Trendanalysen

1 Einleitung . . . . . 3 1.1 Was sind Ontologien . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Ziel der Ontologie . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Relevante Ontologien . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.1 Simple Knowledge Organization System - SKOS . . . . . 7 1.3.2 OWL-Time . . . . . . . . . . . . . . . . . . . . . 9 2 Entwicklung der Metaontologie . . . . . 12 2.1 TREMA Ontologien . . . . . . . . . . . . . . . 13 2.2 Metaontologie . . . . . . . . . . . . . . . . . . . 15 2.2.1 Konzeptionelle Anforderungen . . . . . 15 2.2.2 Allgemeine Beschreibung . . . . . . . . 16 2.2.3 Klassen und Relationen . . . . . . . . . 18 2.2.4 Funktionalität . . . . . . . . . 20 3 Anwendung der Metaontologie. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.1 Anwendung für Aktienanalysen . . . . . . . . . . . . . . . . . . . 22 3.1.1 Klassen . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.1.2 Themenbereiche . . . . . . . . . . . . . . . . . . . . . . . 23 4 Experimente und Evaluierung. . . . . . . . . . . . . . . . . . . . . . . 25 4.1 Evaluierung hinsichtlich Kompetenzfragen . . . . . . . . . . . . . . . . . . . . . . . 25 4.2 Vergleich von Prognose und Kursentwicklung. . . . . . . . . . . . . . . . . . . . . . . 30 4.2.1 Auswahl der Indikatoren. . . . . . . . . . . . . . . . . . . . . . . 30 4.2.2 Berechnung der Kennzahlen . . . . . . . . . . . . . . . . . . . . . . . 30 4.2.3 Vergleich der Kursentwicklung . . . . . . . . . . . . . . . . . . . . . . . 31 5 Fazit und Ausblick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.1 Möglichkeiten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.2 Probleme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 5.3 Fazit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 A Ontology Specification Requirements Document. .40 A.1 Zweck . . . . . . . . . . . . . . . . .40 A.2 Anwendungsbereich . . . . . . . . .40 A.3 Grad der Formalisierung . . . . . . .40 A.4 Zielgruppen . . . . . . . . . . . . . .40 A.5 Vorgesehene Nutzung . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 A.6 Kompetenzfragen . . . . . . . . . . . A.7 Glossar der Begriffe. . . . . . . . . . . . . . . . . . . . . . . . .43 B Kompetenzfragen und korrespondierende Abfrage. . . 44 C Aktienkennzahlen und Berechnung. . . . 47The report deals with the largely unexplored field of ontology-driven, knowledge based trend detection by means of text mining, focusing on the development of trend ontologies. The difficulties of trend detection with text mining lie in the ambiguous semantics of natural languages and their various forms, character- istics and dynamics. Due to this it is difficult to formalize knowledge used in trend detection unambiguously and statically. Using ontologies, language com- ponents can be identified and subsequently processed and analyzed regarding their relations to each other. However, due to different languages and specific usages depending on user and application fields, as well as specific trend be- havior in certain application fields, trend ontologies specialized for the intended application are needed. In order to allow the modular development and usage of these different ontologies a standardizing base for trend ontologies is needed. This base can be realized as meta ontology and its development is the central aspect of the report

On first encountering LOGO; some questions for further research

LOGO will force teachers to become more like master teachers who guide others on the path of teaching and learning

Computer Analysis of Talk-Silence Sequences: The FIASSCO System

The study of simple talk and silence indices that characterize conversation is limited by the costly, labor-intensive character of data collection and analysis. In the face of results demonstrating the significance of these data in interpersonal judgments (Hayes & Meltzer, 1972; Lustig, Note I), more efficient collection, storage, and analysis methods are required. This report describes a hardware and software system, FIASSCO, that collects, stores, and analyzes two-person separate-channel audio-recorded conversations for various indices of talk and silence. Data output are both continuous and discrete measures in time sequence. Further, data on the validity and reliability of FIASSCO output are provided along with sample analyses of computer results

Stochastic trends in simultaneous equation systems.

The estimation of univariate and multiple regression models with stochastic trend components has been considered in the time domain and in the frequency domain. Such models assume as regressors weakly exogenous variables. However if the regression equations are part of a simultaneous equation system some of the regressors will no longer be weakly exogenous and estimators obtained by ignoring this fact will be inconsistent. One way of proceeding in such situations is to estimate the whole system, that is, to construct full information maximum (FIML) estimators. Alternatively, single equation estimators such as limited information maximum likelihood (LIML) can be constructed, as well as estimators based on the instrumental variable (IV) principle which possess the merit of consistency. As in the analogous situation in classical simultaneous equation systems, within this class of limited information estimators, LIML is asymptotically efficient. Hence it is appropriate to study the asymptotic properties of LIML and review the possibility of alternative consistent estimators, using LIML as a benchmark. The purpose of the thesis is thus: to examine the issues of identifiability when stochastic trends are present in simultaneous equation systems; -to examine the computational issues associated with FIML, LIML and various IV estimators in simultaneous equation systems with stochastic trends and derive the asymptotic properties in the frequency domain of these estimators; to compare the performance of IV and LIML via Monte Carlo experiments; to apply the methods to real data

Identifying User Innovations through AI in Online Communities– A Transfer Learning Approach

Identifying innovative users and their ideas is crucial, for example, in crowdsourcing. But, analyzing large amounts of unstructured textual data from such online communities poses a challenge for organizations. Therefore, researchers started developing automated approaches to identify innovative users. Our study introduces an advanced machine-learning approach that minimizes manual work by combining transfer learning with a transformer-based design. We train the model on separate datasets, including an online maker community and various internet texts. The maker community posts represent need-solution pairs, which express needs and describe fitting prototypes. Then, we transfer the model and identify potential user innovations in a kitesurfing community. We validate the identified posts by manually checking a subsample and analyzing how words affect the model\u27s classification decision. This study contributes to the growing portfolio of user innovation identification by combining state-of-the-art natural language processing and transfer learning to improve automated identification

Scattering of compact oscillons

We study various aspects of the scattering of generalized compact oscillons in the signum-Gordon model in (1+1) dimensions. Using covariance of the model we construct traveling oscillons and study their interactions and the dependence of these interactions on the oscillons’ initial velocities and their relative phases. The scattering processes transform the two incoming oscillons into two outgoing ones and lead to the generation of extra oscillons which appear in the form of jet-like cascades. Such cascades vanish for some values of free parameters and the scattering processes, even though our model is non-integrable, resemble typical scattering processes normally observed for integrable or quasi-integrable models. Occasionally, in the intermediate stage of the process, we have seen the emission of shock waves and we have noticed that, in general, outgoing oscillons have been more involved in their emission than the initial ones i.e. they have a border in the form of curved worldlines. The results of our studies of the scattering of oscillons suggest that the radiation of the signum-Gordon model has a fractal-like nature