6,827 research outputs found
Applying semantic web technologies to knowledge sharing in aerospace engineering
This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy docu-ments via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms. The domain ontologies used for Knowledge Capture also guide the retrieval of the Knowledge extracted from the data using a Semantic Search System that provides support for multiple modalities during search. This approach has been applied and evaluated successfully within the aerospace domain, and is currently being extended for use in other domains on an increasingly large scale
Multi Visualization and Dynamic Query for Effective Exploration of Semantic Data
Semantic formalisms represent content in a uniform way according to ontologies. This enables manipulation and reasoning via automated means (e.g. Semantic Web services), but limits the user’s ability to explore the semantic data from a point of view that originates from knowledge representation motivations. We show how, for user consumption, a visualization of semantic data according to some easily graspable dimensions (e.g. space and time) provides effective sense-making of data. In this paper, we look holistically at the interaction between users and semantic data, and propose multiple visualization strategies and dynamic filters to support the exploration of semantic-rich data.
We discuss a user evaluation and how interaction challenges could be overcome to create an effective user-centred framework for the visualization and manipulation of semantic data. The approach has been implemented and evaluated on a real company archive
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
Personalized Fuzzy Text Search Using Interest Prediction and Word Vectorization
In this paper we study the personalized text search problem. The keyword
based search method in conventional algorithms has a low efficiency in
understanding users' intention since the semantic meaning, user profile, user
interests are not always considered. Firstly, we propose a novel text search
algorithm using a inverse filtering mechanism that is very efficient for label
based item search. Secondly, we adopt the Bayesian network to implement the
user interest prediction for an improved personalized search. According to user
input, it searches the related items using keyword information, predicted user
interest. Thirdly, the word vectorization is used to discover potential targets
according to the semantic meaning. Experimental results show that the proposed
search engine has an improved efficiency and accuracy and it can operate on
embedded devices with very limited computational resources
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
A web-based teaching/learning environment to support collaborative knowledge construction in design
A web-based application has been developed as part of a recently completed research which proposed a conceptual framework to collect, analyze and compare different design experiences and to construct structured representations of the emerging knowledge in digital architectural design. The paper introduces the theoretical and practical development of this application as a teaching/learning environment which has significantly contributed to the development and testing of the ideas developed throughout the research. Later in the paper, the application of BLIP in two experimental (design) workshops is reported and evaluated according to the extent to which the application facilitates generation, modification and utilization of design knowledge
Extracting corpus specific knowledge bases from Wikipedia
Thesauri are useful knowledge structures for assisting information retrieval. Yet their production is labor-intensive, and few domains have comprehensive thesauri that cover domain-specific concepts and contemporary usage. One approach, which has been attempted without much success for decades, is to seek statistical natural language processing algorithms that work on free text. Instead, we propose to replace costly professional indexers with thousands of dedicated amateur volunteers--namely, those that are producing Wikipedia. This vast, open encyclopedia represents a rich tapestry of topics and semantics and a huge investment of human effort and judgment. We show how this can be directly exploited to provide WikiSauri: manually-defined yet inexpensive thesaurus structures that are specifically tailored to expose the topics, terminology and semantics of individual document collections. We also offer concrete evidence of the effectiveness of WikiSauri for assisting information retrieval
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