1,598 research outputs found
Semantic Query Optimisation with Ontology Simulation
Semantic Web is, without a doubt, gaining momentum in both industry and
academia. The word "Semantic" refers to "meaning" - a semantic web is a web of
meaning. In this fast changing and result oriented practical world, gone are
the days where an individual had to struggle for finding information on the
Internet where knowledge management was the major issue. The semantic web has a
vision of linking, integrating and analysing data from various data sources and
forming a new information stream, hence a web of databases connected with each
other and machines interacting with other machines to yield results which are
user oriented and accurate. With the emergence of Semantic Web framework the
na\"ive approach of searching information on the syntactic web is clich\'e.
This paper proposes an optimised semantic searching of keywords exemplified by
simulation an ontology of Indian universities with a proposed algorithm which
ramifies the effective semantic retrieval of information which is easy to
access and time saving
Ranking Archived Documents for Structured Queries on Semantic Layers
Archived collections of documents (like newspaper and web archives) serve as
important information sources in a variety of disciplines, including Digital
Humanities, Historical Science, and Journalism. However, the absence of
efficient and meaningful exploration methods still remains a major hurdle in
the way of turning them into usable sources of information. A semantic layer is
an RDF graph that describes metadata and semantic information about a
collection of archived documents, which in turn can be queried through a
semantic query language (SPARQL). This allows running advanced queries by
combining metadata of the documents (like publication date) and content-based
semantic information (like entities mentioned in the documents). However, the
results returned by such structured queries can be numerous and moreover they
all equally match the query. In this paper, we deal with this problem and
formalize the task of "ranking archived documents for structured queries on
semantic layers". Then, we propose two ranking models for the problem at hand
which jointly consider: i) the relativeness of documents to entities, ii) the
timeliness of documents, and iii) the temporal relations among the entities.
The experimental results on a new evaluation dataset show the effectiveness of
the proposed models and allow us to understand their limitation
Keyword Search on RDF Graphs - A Query Graph Assembly Approach
Keyword search provides ordinary users an easy-to-use interface for querying
RDF data. Given the input keywords, in this paper, we study how to assemble a
query graph that is to represent user's query intention accurately and
efficiently. Based on the input keywords, we first obtain the elementary query
graph building blocks, such as entity/class vertices and predicate edges. Then,
we formally define the query graph assembly (QGA) problem. Unfortunately, we
prove theoretically that QGA is a NP-complete problem. In order to solve that,
we design some heuristic lower bounds and propose a bipartite graph
matching-based best-first search algorithm. The algorithm's time complexity is
, where is the number of the keywords and is a
tunable parameter, i.e., the maximum number of candidate entity/class vertices
and predicate edges allowed to match each keyword. Although QGA is intractable,
both and are small in practice. Furthermore, the algorithm's time
complexity does not depend on the RDF graph size, which guarantees the good
scalability of our system in large RDF graphs. Experiments on DBpedia and
Freebase confirm the superiority of our system on both effectiveness and
efficiency
Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams
Wildfires are frequent, devastating events in Australia that regularly cause
significant loss of life and widespread property damage. Fire weather indices
are a widely-adopted method for measuring fire danger and they play a
significant role in issuing bushfire warnings and in anticipating demand for
bushfire management resources. Existing systems that calculate fire weather
indices are limited due to low spatial and temporal resolution. Localized
wireless sensor networks, on the other hand, gather continuous sensor data
measuring variables such as air temperature, relative humidity, rainfall and
wind speed at high resolutions. However, using wireless sensor networks to
estimate fire weather indices is a challenge due to data quality issues, lack
of standard data formats and lack of agreement on thresholds and methods for
calculating fire weather indices. Within the scope of this paper, we propose a
standardized approach to calculating Fire Weather Indices (a.k.a. fire danger
ratings) and overcome a number of the challenges by applying Semantic Web
Technologies to the processing of data streams from a wireless sensor network
deployed in the Springbrook region of South East Queensland. This paper
describes the underlying ontologies, the semantic reasoning and the Semantic
Fire Weather Index (SFWI) system that we have developed to enable domain
experts to specify and adapt rules for calculating Fire Weather Indices. We
also describe the Web-based mapping interface that we have developed, that
enables users to improve their understanding of how fire weather indices vary
over time within a particular region.Finally, we discuss our evaluation results
that indicate that the proposed system outperforms state-of-the-art techniques
in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure
- …