Semantic File Annotation and Retrieval on Mobile Devices

Jan, Sadaqat; Li, Maozhen; Al-Sultany, Ghaidaa A.; Al-Raweshidy, Hamed S.; Shah, Ismail

doi:10.1155/2011/320984

Cited by 9 publications

(11 citation statements)

References 30 publications

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“…Our proposed research may also be applied to various domain applications, such as mobile systems 28,29,30,31,32,33,34 , RFID 35,36 , real-time systems 37 , business process 38 , web systems 39,40,41 , and fuzzy data 42 .…”

Section: Resultsmentioning

confidence: 99%

Mining Hierarchical Negative Association Rules

Taniar¹,

Rahayu²,

Daly³

et al. 2012

IJCIS

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The negative association between items in databases is as important and interesting as the positive one. But, it has not been studied as much. We consider negative association in a hierarchical setting, in which we are able to generate negative association rules at different hierarchy levels. It allows to impose restrictions when we proceed to the next level and discover only most interesting negative association rules among the vast number of possible negative association rules. In this paper, we propose two algorithms for mining negative association rules by considering that items are organized in a hierarchy, and this hierarchy is reflected on the association rules we produce. In this way, we can mine for both general and specialized rules of negative association between items.

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Section: Resultsmentioning

confidence: 99%

Mining Hierarchical Negative Association Rules

Taniar¹,

Rahayu²,

Daly³

et al. 2012

IJCIS

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“…To evaluate the performance of OARS comprehensively, we have formulated several test scenarios using the benchmark data sets and the evaluation criteria defined by (10), (11) and (12). The main purposes of these test scenarios are to assess:…”

Section: Similarity Aggregationmentioning

confidence: 99%

“…The annotation process automatically annotates the files with three basic attributes and two user entered fields. The meta-data is automatically parsed and stored in XML structured document as described in [12], [13] and [59]. Fig.8 shows the overall process of the SemFARM search module in which the input file queries are answered after merging two existing ontologies.…”

Section: Integrating Oars Into Semfarmmentioning

confidence: 99%

“…More importantly, the significance of using Rough sets as an aggregation method is also evaluated in this paper. Furthermore, we have integrated OARS into our previously developed SemFARM [12][13], a framework which provides an efficient search mechanism for file annotation and retrieval on mobile devices connected through Bluetooth. The integration of OARS enables SemFARM to utilize the knowledge of multiple ontologies when searching for a file on resourcelimited devices in a network environment which leads to high accuracy in file retrieval.…”

Section: Introductionmentioning

confidence: 99%

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Dealing With Uncertain Entities in Ontology Alignment Using Rough Sets

Jan

Al-Raweshidy

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

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-Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures for mapping entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a Rough sets based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the Ontology Alignment Evaluation Initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision.

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“…As the number of stored files grows on such devices it becomes a challenging issue for users to search efficiently for files of interest. For this purpose, SemFARM [1] framework was presented which annotates the files and provides semantic based file search capabilities. A generic ontology was developed to define the most comely used keywords which can be used as metadata to annotate the stored files.…”

Section: Introductionmentioning

confidence: 99%

Semantic based file retrieval on resource limited devices with ontology alignment support

Jan

Al-Sultany

et al. 2011

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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In this paper, we present a file search mechanism that can utilize the ontology alignments to facilitate file retrieval on resource-limited devices by exploiting the knowledge of more than one ontologies. This enables the device users to take advantage of all ontologies, which might have been developed in the same domain. A semantic-based file retrieval framework was previously presented for resource-limited devices, which annotates files with its basic attributes and provides file retrieval capabilities. A generic ontology was developed to define the most commonly used keywords which can be used as metadata of the stored files. The ontology was employed to exploit the semantic based file retrieval capability. The search mechanism was evaluated by formulating two test cases. In first test case the framework make use of single ontology while in the second test case, the ontology alignments were utilized by aligning two ontologies. The implications of ontology alignment support were analyzed through probabilistic evaluation, which shows the progress in file retrieval efficiency.

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Semantic File Annotation and Retrieval on Mobile Devices

Cited by 9 publications

References 30 publications

Mining Hierarchical Negative Association Rules

Mining Hierarchical Negative Association Rules

Dealing With Uncertain Entities in Ontology Alignment Using Rough Sets

Semantic based file retrieval on resource limited devices with ontology alignment support

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