The mediator authorization-security model for heterogeneous semantic knowledge bases

2016 ◽  
Vol 55 ◽  
pp. 227-237 ◽  
Author(s):  
Abdullah Alamri ◽  
Peter Bertok ◽  
James A. Thom ◽  
Adil Fahad
Keyword(s):  
Knowledge Bases ◽  
Semantic Knowledge ◽  
Security Model

A semantic-based approach for querying linked data using natural language

2016 ◽  
Vol 42 (6) ◽  
pp. 851-862 ◽  
Author(s):  
Mario Andrés Paredes-Valverde ◽  
Rafael Valencia-García ◽  
Miguel Ángel Rodríguez-García ◽  
Ricardo Colomo-Palacios ◽  
Giner Alor-Hernández
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Bases ◽  
Semantic Knowledge ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Natural Language Interface ◽  
High Quality Information ◽  
Domain Independent

The semantic Web aims to provide to Web information with a well-defined meaning and make it understandable not only by humans but also by computers, thus allowing the automation, integration and reuse of high-quality information across different applications. However, current information retrieval mechanisms for semantic knowledge bases are intended to be only used by expert users. In this work, we propose a natural language interface that allows non-expert users the access to this kind of information through formulating queries in natural language. The present approach uses a domain-independent ontology model to represent the question’s structure and context. Also, this model allows determination of the answer type expected by the user based on a proposed question classification. To prove the effectiveness of our approach, we have conducted an evaluation in the music domain using LinkedBrainz, an effort to provide the MusicBrainz information as structured data on the Web by means of Semantic Web technologies. Our proposal obtained encouraging results based on the F-measure metric, ranging from 0.74 to 0.82 for a corpus of questions generated by a group of real-world end users.

TOLERANCE ROUGH SET BASED ATTRIBUTE EXTRACTION APPROACH FOR MULTIPLE SEMANTIC KNOWLEDGE BASE INTEGRATION

2011 ◽  
Vol 19 (04) ◽  
pp. 659-684 ◽  
Author(s):  
H. Z. GUO ◽  
Q. C. CHEN ◽  
X. L. WANG ◽  
L. CUI
Keyword(s):  
Rough Set ◽  
Semantic Analysis ◽  
Knowledge Bases ◽  
Semantic Knowledge ◽  
Statistical Techniques ◽  
Natural Properties ◽  
Specific Attribute ◽  
Tolerance Rough Set ◽  
Attribute Extraction ◽  
Semantic Knowledge Base

In the integration of multiple semantic knowledge bases (SKBs), the inconsistence of the items or their attributes appeared in different SKBs is still an opening challenge for researchers. To address this issue, this paper presents an innovative approach which bases on extracting common class attributes and establishing unified category-attribute templates. Since the natural properties of uncertainty and vagueness of semantic analysis involved in selecting a specific attribute from numerous candidates, the tolerance rough set (TRS) techniques are applied in constructing class-attribute templates from online SKBs. The extraction of attribute is fulfilled by statistical techniques and is integrated into the TRS framework. Finally, experiments are conducted on random selected categories. Experimental results show the effectiveness of the proposed approach.

An Enhanced Semantic Layer for Hybrid Recommender Systems

2011 ◽  
Vol 7 (1) ◽  
pp. 44-78 ◽  
Author(s):  
Iván Cantador ◽  
Pablo Castells ◽  
Alejandro Bellogín
Keyword(s):  
Recommender Systems ◽  
Domain Knowledge ◽  
Information Overload ◽  
Knowledge Bases ◽  
Semantic Knowledge ◽  
User Preference ◽  
Knowledge Space ◽  
Performance Improvements ◽  
Rich Domain ◽  
Hybrid Recommender

Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources.

An Enhanced Semantic Layer for Hybrid Recommender Systems

Semantic Web ◽  
2013 ◽  
pp. 235-269 ◽  
Author(s):  
Iván Cantador ◽  
Pablo Castells ◽  
Alejandro Bellogín
Keyword(s):  
Recommender Systems ◽  
Domain Knowledge ◽  
Information Overload ◽  
Knowledge Bases ◽  
Semantic Knowledge ◽  
User Preference ◽  
Performance Improvements ◽  
Rich Domain ◽  
Hybrid Recommender ◽  
Semantic Layer

Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources.

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