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

Trend Mining

In terms of Information Retrieval (IR), a trend is defined as a topic area that is growing in interest and utility over time. An example of a trend would thus be the general topic financial crisis that started to appear on the market in late 2007 and early 2008, or the Arab Spring that started to appear on the news in 2011. Several approaches based on methods from text mining and machine learning can be successfully applied to the problem of mining trends in text collections. Among others, the most popular are probabilistic topic models and diverse clustering methods. The weakness of the existing research in automatic trend detection in texts lies in: 1\. inconsistency in the definition of a trend 2\. lack of a general scientific approach for trend mining 3\. lack of the integration of explicit knowledge and therefore the difficulty in the interpretation of algorithm's results. The scientific contribution of this research is contained in the suggestion to deal with the trend detection from the perspective of trend mining that is being defined here. As a solution for the problem of difficulty in the interpretation of the results from the common trend detection techniques, this research proposes the trend template that is a knowledge-based trend mining approach. Based on this trend template, two directions of implementation are introduced: trend ontology and trend-indication (the trend weighting method). The trend ontology works as an a-priori model and enables the discovery of a trend structure in the web documents corpus. Tests with this method on a test corpus show that mining trends with an a-priori model while integrating explicit knowledge leads to a better quality of results considering their interpretability. The trend-indication approach is based on time-incorporating weighting methods for selection of trend features from web documents. It enables the reduction of features that are considered in the process of trend mining, and therefore reduces the data so that only time-relevant information is considered for further analysis. This method's results on our web document corpus show that time-based weighting functions alone can help in discovering trend-relevant features. Both the trend ontology and the trend-indication approaches are implemented in the tremit tool (TREnd MIning Tool), a test tool developed for this thesis, and are tested on a test corpus. The test corpus consists of 35,635 business news and 4,696 DAX (Deutscher Aktienindex - German stock market) reports from German web sites in a late 2007 and early 2008. The results are compared with the standard method results of a LDA-based topic model and the k-means clustering algorithm on the same test corpus. Discussion of the results is contained in the experimental part of the thesis.Ein Trend im Kontext des Information Retrievals (IR) ist ein Themengebiet, das über einen Zeitraum an Nutzwert und Interesse gewinnt, wie z. B. das allgemeine Thema Finanzkrise im Zeitraum 2008-2012 oder Arabischer Frühling im Zeitraum 2010-2011. Es gibt Verfahren, verankert in Bereichen des Data Minings, Text Minings und des Maschinellen Lernens, die zur Lösung des Problems der Trenderkennung in Texten herangezogen werden. Zu den oft verwendeten gehören die probabilistischen Topic Models sowie verschiedene Clusteringverfahren. Die Schwachstellen der existierenden Forschung über automatische Trenderkennung in Texten liegen in: 1\. inkonsistenten Definitionen des Trends 2\. fehlendem wissenschaftlichen Ansatz des Trend Mining 3\. fehlendem Bezug zum expliziten Wissen und damit schlechter Interpretierbarkeit der Ergebnisse Der wissenschaftliche Beitrag dieser Arbeit besteht in dem Vorschlag, die Forschung zur automatischen Trenderkennung aus der Sicht des Trend Mining zu betrachten, dessen Definition in dieser Arbeit vorgeschlagen wird. Als Lösung für das Problem der schlechten Interpretierbarkeit der Ergebnisse von gängigen Trenderkennungsalgorithmen wird trend template vorgeschlagen, das ein wissensbasierter Ansatz für trend mining ist. Ausgehend von diesem trend template werden zwei Implementierungsrichtungen gezeigt: die Trendontologie und das Trend- Indication-Verfahren. Die Trendontologie funktioniert nach dem Prinzip eines A -priori-Modells und ermöglicht die Entdeckung einer Trendstruktur in dem Webdokumentenkorpus. Tests mit diesem Verfahren auf dem Testkorpus zeigen, dass Trenderkennung mit einem A-priori-Modell unter Einbezug von explizitem Wissen, zu qualitativ besseren Ergebnissen, vor allem in Hinsicht auf die Interpretierbarkeit, führt. Das Trend-Indication-Verfahren baut auf den zeitbasierten Gewichtungsfunktionen auf und konzentriert sich auf die Selektion der Trend Features aus den Webdokumenten. Mithilfe dieses Verfahrens wird die Dimension der zu untersuchenden Daten im Hinblick auf die Trenderkennung sinnvoll reduziert und somit nur die zeitrelevante Information aus den Texten für weitere Analysen bereitgestellt. Die Tests mit diesem Verfahren zeigen, dass zeitrelevante Trendbegriffe alleine durch geeignete Gewichtungsfunktionen gut aufgedeckt werden. Beide Methoden werden in dem tremit (TREnd MIning Tool), das für diese Arbeit entwickelte Testtool, implementiert und auf dem Testkorpus getestet. Der Testkorpus besteht aus 35.635 Wirtschaftsnachrichten und 4.696 DAX-Berichten des deutschsprachigen Webs aus dem Zeitraum September 2007 bis April 2008. Die Ergebnisse werden mit den Ergebnissen der gängigen Verfahren - LDA-basiertem Topic Model und k-means Clustering - auf dem gleichen gleichen Korpus verglichen und im Experimentierteil der Arbeit diskutiert und evaluiert

requirements and use cases

In this report, we introduce our initial vision of the Corporate Semantic Web as the next step in the broad field of Semantic Web research. We identify requirements of the corporate environment and gaps between current approaches to tackle problems facing ontology engineering, semantic collaboration, and semantic search. Each of these pillars will yield innovative methods and tools during the project runtime until 2013. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human-centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge. We propose an initial layout for an integrative architecture of a Corporate Semantic Web provided by these three core pillars

concept paper

In this concept paper, we outline our working plan for the next phase of the Corporate Semantic Web project. The plan covers the period from March 2009 to March 2010. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human- centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge. Each of these pillars will yield innovative methods and tools during the project runtime until 2013. We propose a concept draft and a working plan covering the next twelve months for an integrative architecture of a Corporate Semantic Web provided by these three core pillars

prototypical implementations ; working packages in project phase II

In this technical report, we present the concepts and first prototypical imple- mentations of innovative tools and methods for personalized and contextualized (multimedia) search, collaborative ontology evolution, ontology evaluation and cost models, and dynamic access and trends in distributed (semantic) knowledge. The concepts and prototypes are based on the state of art analysis and identified requirements in the CSW report IV

state of the art analysis ; working packages in project phase II

In this report, we introduce our goals and present our requirement analysis for the second phase of the Corporate Semantic Web project. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human-centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge

prototypical implementations

In this technical report, we present prototypical implementations of innovative tools and methods developed according to the working plan outlined in Technical Report TR-B-09-05 [23]. We present an ontology modularization and integration framework and the SVoNt server, the server-side end of an SVN- based versioning system for ontologies in the Corporate Ontology Engineering pillar. For the Corporate Semantic Collaboration pillar, we present the prototypical implementation of a light-weight ontology editor for non-experts and an ontology based expert finder system. For the Corporate Semantic Search pillar, we present a prototype for algorithmic extraction of relations in folksonomies, a tool for trend detection using a semantic analyzer, a tool for automatic classification of web documents using Hidden Markov models, a personalized semantic recommender for multimedia content, and a semantic search assistant developed in co-operation with the Museumsportal Berlin. The prototypes complete the next milestone on the path to an integral Cor- porate Semantic Web architecture based on the three pillars Corporate Ontol- ogy Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search, as envisioned in [23]

Validation and Evaluation

In this technical report, we present prototypical implementations of innovative tools and methods for personalized and contextualized (multimedia) search, collaborative ontology evolution, ontology evaluation and cost models, and dynamic access and trends in distributed (semantic) knowledge, developed according to the working plan outlined in Technical Report TR-B-12-04. The prototypes complete the next milestone on the path to an integral Corporate Semantic Web architecture based on the three pillars Corporate Ontology Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search, as envisioned in TR-B-08-09