1,489 research outputs found
Large-Scale information extraction from textual definitions through deep syntactic and semantic analysis
We present DEFIE, an approach to large-scale Information Extraction (IE) based on a syntactic-semantic analysis of textual definitions. Given a large corpus of definitions we leverage syntactic dependencies to reduce data sparsity, then disambiguate the arguments and content words of the relation strings, and finally exploit the resulting information to organize the acquired relations hierarchically. The output of DEFIE is a high-quality knowledge base consisting of several million automatically acquired semantic relations
A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web
Over the past decade, rapid advances in web technologies, coupled with
innovative models of spatial data collection and consumption, have generated a
robust growth in geo-referenced information, resulting in spatial information
overload. Increasing 'geographic intelligence' in traditional text-based
information retrieval has become a prominent approach to respond to this issue
and to fulfill users' spatial information needs. Numerous efforts in the
Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the
Linking Open Data initiative have converged in a constellation of open
knowledge bases, freely available online. In this article, we survey these open
knowledge bases, focusing on their geospatial dimension. Particular attention
is devoted to the crucial issue of the quality of geo-knowledge bases, as well
as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic
Network, is outlined as our contribution to this area. Research directions in
information integration and Geographic Information Retrieval (GIR) are then
reviewed, with a critical discussion of their current limitations and future
prospects
280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification
We propose a simple, yet effective, approach towards inducing multilingual
taxonomies from Wikipedia. Given an English taxonomy, our approach leverages
the interlanguage links of Wikipedia followed by character-level classifiers to
induce high-precision, high-coverage taxonomies in other languages. Through
experiments, we demonstrate that our approach significantly outperforms the
state-of-the-art, heuristics-heavy approaches for six languages. As a
consequence of our work, we release presumably the largest and the most
accurate multilingual taxonomic resource spanning over 280 languages
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