8,229 research outputs found
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
Biomedical ontology alignment: An approach based on representation learning
While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results
MeLinDa: an interlinking framework for the web of data
The web of data consists of data published on the web in such a way that they
can be interpreted and connected together. It is thus critical to establish
links between these data, both for the web of data and for the semantic web
that it contributes to feed. We consider here the various techniques developed
for that purpose and analyze their commonalities and differences. We propose a
general framework and show how the diverse techniques fit in the framework.
From this framework we consider the relation between data interlinking and
ontology matching. Although, they can be considered similar at a certain level
(they both relate formal entities), they serve different purposes, but would
find a mutual benefit at collaborating. We thus present a scheme under which it
is possible for data linking tools to take advantage of ontology alignments.Comment: N° RR-7691 (2011
Shiva: A Framework for Graph Based Ontology Matching
Since long, corporations are looking for knowledge sources which can provide
structured description of data and can focus on meaning and shared
understanding. Structures which can facilitate open world assumptions and can
be flexible enough to incorporate and recognize more than one name for an
entity. A source whose major purpose is to facilitate human communication and
interoperability. Clearly, databases fail to provide these features and
ontologies have emerged as an alternative choice, but corporations working on
same domain tend to make different ontologies. The problem occurs when they
want to share their data/knowledge. Thus we need tools to merge ontologies into
one. This task is termed as ontology matching. This is an emerging area and
still we have to go a long way in having an ideal matcher which can produce
good results. In this paper we have shown a framework to matching ontologies
using graphs
Towards a Taxonomically Intelligent Phylogenetic Database
This note outlines some of the key intellectual obstacles that stand in the way of creating a usable phylogenetic database. These challenges include the need to accommodate multiple taxonomic names and classifications, and the need for tools to query trees in biologically meaningful ways. Until these problems are addressed, and a taxonomically intelligent phylogenetic database created, much of our phylogenetic knowledge will languish in the pages of journals
The Evaluation of Ontology Matching versus Text
Lately, the ontologies have become more and more complex, and they are used in different domains. Some of the ontologies are domain independent; some are specific to a domain. In the case of text processing and information retrieval, it is important to identify the corresponding ontology to a specific text. If the ontology is of a great scale, only a part of it may be reflected in the natural language text. This article presents metrics which evaluate the degree in which an ontology matches a natural language text, from word counting metrics to text entailment based metrics.Ontology, Natural Language Processing, Metric
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