13,634 research outputs found
Ontology based Approach for Precision Agriculture
In this paper, we propose a framework of knowledge for an agriculture
ontology which can be used for the purpose of smart agriculture systems. This
ontology not only includes basic concepts in the agricultural domain but also
contains geographical, IoT, business subdomains, and other knowledge extracted
from various datasets. With this ontology, any users can easily understand
agricultural data links between them collected from many different data
resources. In our experiment, we also import country, sub-country and disease
entities into this ontology as basic entities for building agricultural linked
datasets later
Evaluating the semantic web: a task-based approach
The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e. by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicity provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape
Comparing human and automatic thesaurus mapping approaches in the agricultural domain
Knowledge organization systems (KOS), like thesauri and other controlled
vocabularies, are used to provide subject access to information systems across
the web. Due to the heterogeneity of these systems, mapping between
vocabularies becomes crucial for retrieving relevant information. However,
mapping thesauri is a laborious task, and thus big efforts are being made to
automate the mapping process. This paper examines two mapping approaches
involving the agricultural thesaurus AGROVOC, one machine-created and one human
created. We are addressing the basic question "What are the pros and cons of
human and automatic mapping and how can they complement each other?" By
pointing out the difficulties in specific cases or groups of cases and grouping
the sample into simple and difficult types of mappings, we show the limitations
of current automatic methods and come up with some basic recommendations on
what approach to use when.Comment: 10 pages, Int'l Conf. on Dublin Core and Metadata Applications 200
Automatic multi-label subject indexing in a multilingual environment
This paper presents an approach to automatically subject index fulltext documents with multiple labels based on binary support vector machines(SVM). The aim was to test the applicability of SVMs with a real world dataset. We have also explored the feasibility of incorporating multilingual background knowledge, as represented in thesauri or ontologies, into our text document representation for indexing purposes. The test set for our evaluations has been compiled from an extensive document base maintained by the Food and Agriculture Organization (FAO) of the United Nations (UN). Empirical results show that SVMs are a good method for automatic multi- label classification of documents in multiple languages
Semantic-driven Configuration of Internet of Things Middleware
We are currently observing emerging solutions to enable the Internet of
Things (IoT). Efficient and feature rich IoT middeware platforms are key
enablers for IoT. However, due to complexity, most of these middleware
platforms are designed to be used by IT experts. In this paper, we propose a
semantics-driven model that allows non-IT experts (e.g. plant scientist, city
planner) to configure IoT middleware components easier and faster. Such tools
allow them to retrieve the data they want without knowing the underlying
technical details of the sensors and the data processing components. We propose
a Context Aware Sensor Configuration Model (CASCoM) to address the challenge of
automated context-aware configuration of filtering, fusion, and reasoning
mechanisms in IoT middleware according to the problems at hand. We incorporate
semantic technologies in solving the above challenges. We demonstrate the
feasibility and the scalability of our approach through a prototype
implementation based on an IoT middleware called Global Sensor Networks (GSN),
though our model can be generalized into any other middleware platform. We
evaluate CASCoM in agriculture domain and measure both performance in terms of
usability and computational complexity.Comment: 9th International Conference on Semantics, Knowledge & Grids (SKG),
Beijing, China, October, 201
Text-mining and ontologies: new approaches to knowledge discovery of microbial diversity
Microbiology research has access to a very large amount of public information
on the habitats of microorganisms. Many areas of microbiology research uses
this information, primarily in biodiversity studies. However the habitat
information is expressed in unstructured natural language form, which hinders
its exploitation at large-scale. It is very common for similar habitats to be
described by different terms, which makes them hard to compare automatically,
e.g. intestine and gut. The use of a common reference to standardize these
habitat descriptions as claimed by (Ivana et al., 2010) is a necessity. We
propose the ontology called OntoBiotope that we have been developing since
2010. The OntoBiotope ontology is in a formal machine-readable representation
that enables indexing of information as well as conceptualization and
reasoning.Comment: 5 page
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