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Language

f

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Source

Concepts in context: Proceedings of the Cologne Conference on Interoperability and Semantics in Knowledge Organization July 19th - 20th, 2010. Eds.: F. Boteram, W. Gödert u. J. Hubrich

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Date

22. 2.2003 17:25:39
22. 2.2003 18:17:40

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Date

30. 3.2001 13:32:22

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Abstract

The Future of Information Architecture examines issues surrounding why information is processed, stored and applied in the way that it has, since time immemorial. Contrary to the conventional wisdom held by many scholars in human history, the recurrent debate on the explanation of the most basic categories of information (eg space, time causation, quality, quantity) has been misconstrued, to the effect that there exists some deeper categories and principles behind these categories of information - with enormous implications for our understanding of reality in general. To understand this, the book is organised in to four main parts: Part I begins with the vital question concerning the role of information within the context of the larger theoretical debate in the literature. Part II provides a critical examination of the nature of data taxonomy from the main perspectives of culture, society, nature and the mind. Part III constructively invesitgates the world of information network from the main perspectives of culture, society, nature and the mind. Part IV proposes six main theses in the authors synthetic theory of information architecture, namely, (a) the first thesis on the simpleness-complicatedness principle, (b) the second thesis on the exactness-vagueness principle (c) the third thesis on the slowness-quickness principle (d) the fourth thesis on the order-chaos principle, (e) the fifth thesis on the symmetry-asymmetry principle, and (f) the sixth thesis on the post-human stage.

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Editor

Guerra, F.

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Abstract

Relation extraction aims at finding meaningful relationships between two named entities from within unstructured textual content. In this paper, we define the problem of information extraction as a matrix completion problem where we employ the notion of universal schemas formed as a collection of patterns derived from open information extraction systems as well as additional features derived from grammatical clause patterns and statistical topic models. One of the challenges with earlier work that employ matrix completion methods is that such approaches require a sufficient number of observed relation instances to be able to make predictions. However, in practice there is often insufficient number of explicit evidence supporting each relation type that could be used within the matrix model. Hence, existing work suffer from a low recall. In our work, we extend the work in the state of the art by proposing novel ways of integrating two sets of features, i.e., topic models and grammatical clause structures, for alleviating the low recall problem. More specifically, we propose that it is possible to (1) employ grammatical clause information from textual sentences to serve as an implicit indication of relation type and argument similarity. The basis for this is that it is likely that similar relation types and arguments are observed within similar grammatical structures, and (2) benefit from statistical topic models to determine similarity between relation types and arguments. We employ statistical topic models to determine relation type and argument similarity based on their co-occurrence within the same topics. We have performed extensive experiments based on both gold standard and silver standard datasets. The experiments show that our approach has been able to address the low recall problem in existing methods, by showing an improvement of 21% on recall and 8% on f-measure over the state of the art baseline.

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Date

1. 2.2016 18:25:22