Will reading different stories about the same event in the world result in a similar image of the world? Will reading the same story by different people result in a similar proxy for experiencing the story? The answer to both questions is no because language is abstract by definition and relies on our episodic experience to turn a story into a more concrete mental movie. Since our episodic knowledge differs, also the mental movie will be different. Language leaves out details, (...) and this becomes specifically clear when building machines that read texts to represent events and to establish event relations across mentions, such as co-reference, causality, subevents, scripts, timelines, and storylines. There is a lot of information and knowledge on the event that is not in the text but is needed to reconstruct these relations and understand the story. Machines lack this knowledge and experience and likewise make explicit what it takes to understand stories from text. In this paper, we report on experiments to automatically model event descriptions and instances across different news articles. We will show that event information is scattered over the text but also varies a lot in the degree it abstracts from details, which makes establishing event identity and relations extremely difficult. The variation in granularity of event descriptions seems to vary with pragmatic communicative strategies and defines the problem at different levels of complexity. (shrink)

In order to exchange knowledge, humans need to share a common lexicon of words as well as to access the world models underlying that lexicon. What is a natural process for a human turns out to be an extremely hard task for a machine: computers can’t represent knowledge as effectively as humans do, which hampers, for example, meaning disambiguation and communication. Applied ontologies and NLP have been developed to face these challenges. Integrating ontologies with (possibly multilingual) lexical resources is an (...) essential requirement to make human language understandable by machines, and also to enable interoperability and computability across information systems and, ultimately, in the Web. This book explores recent advances in the integration of ontologies and lexical resources, including questions such as building the required infrastructure (e.g., the Semantic Web) and different formalisms, methods and platforms for eliciting, analyzing and encoding knowledge contents (e.g., multimedia, emotions, events, etc.). The contributors look towards next-generation technologies, shifting the focus from the state of the art to the future of Ontologies and Lexical Resources. This work will be of interest to research scientists, graduate students, and professionals in the fields of knowledge engineering, computational linguistics, and semantic technologies. (shrink)