Albert Weichselbraun

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This paper describes a system to semi-automatically extend and refine ontologies by mining textual data from the Web sites of international online media. Expanding a seed ontology creates a semantic network through co-occurrence analysis, trigger phrase analysis, and disambiguation based on the WordNet lexical dictionary. Spreading activation then processes(More)
—Web intelligence applications track online sources with economic relevance such as customer reviews, news articles and social media postings. Automated sentiment analysis based on lexical methods or machine learning identifies the polarity of opinions expressed in these sources to assess how stakeholders perceive a topic. This paper introduces a hybrid(More)
The simplicity of using Web 2.0 platforms and services has resulted in an abundance of user-generated content. A significant part of this content contains user opinions with clear economic relevance-customer and travel reviews, for example, or the articles of well-known and respected bloggers who influence purchase decisions. Analyzing and acting upon(More)
The advantages and positive effects of multiple coordinated views on search performance have been documented in several studies. This paper describes the implementation of multiple coordinated views within the Media Watch on Climate Change, a domain-specific news aggregation portal available at www.ecoresearch.net/climate that combines a portfolio of(More)
This paper presents a method to integrate external knowledge sources such as DBpedia and OpenCyc into an ontology learning system that automatically suggests labels for unknown relations in domain ontologies based on large corpora of unstructured text. The method extracts and aggregates verb vectors from semantic relations identified in the corpus. It(More)
This paper presents the US Election 2004 Web Monitor, a public Web portal that cap‐ tured trends in political media coverage before and after the 2004 US Presidential Election (Figure 1). Developed by the authors of this article, the webLyzard suite of Web mining tools provided the required functionality to aggregate and analyze about half a million(More)
Although ontologies are central to the Semantic Web, current ontology learning methods primarily make use of a single evidence source and are agnostic in their internal representations to the evolution of ontology knowledge. This article presents a continuous ontology learning framework that overcomes these shortcomings by integrating evidence from(More)
The identification and labelling of non-hierarchical relations are among the most challenging tasks in ontology learning. This paper describes a bottom-up approach for automatically suggesting ontology link types. The presented method extracts verb vectors from semantic relations identified in the domain corpus, aggregates them by computing centroids for(More)
This paper presents a novel method for contextualizing and enriching large semantic knowledge bases for opinion mining with a focus on Web intelligence platforms and other high-throughput big data applications. The method is not only applicable to traditional sentiment lexicons, but also to more comprehensive , multi-dimensional affective resources such as(More)
Sentiment detection automatically identifies emotions in textual data. The increasing amount of emotive documents available in corporate databases and on the World Wide Web calls for automated methods to process this important source of knowledge. Sentiment detection draws attention from researchers and practitioners alike-to enrich business intelligence(More)