An ontology-based semantic retrieval model for Uyghur search engine

2010 ◽  
Author(s):  
Bo Ma ◽  
Yating Yang ◽  
Xi Zhou ◽  
Junlin Zhou
Keyword(s):  
Search Engine ◽  
Semantic Retrieval ◽  
Retrieval Model

A Semantic Retrieval Model Based on Domain Ontology of Orchard Disease and Pests

2016 ◽  
Vol 25 (3) ◽  
pp. 460-466 ◽  
Author(s):  
Jiajia Hou ◽  
Hui Han ◽  
Chengjing Qiu ◽  
Dongmei Li
Keyword(s):  
Domain Ontology ◽  
Semantic Retrieval ◽  
Retrieval Model ◽  
Model Based

What Can Expressive Semantics Tell: Retrieval Model for a Flash-Movie Search Engine

2005 ◽  
pp. 123-133 ◽  
Author(s):  
Dawei Ding ◽  
Jun Yang ◽  
Qing Li ◽  
Wenyin Liu ◽  
Liping Wang
Keyword(s):  
Search Engine ◽  
Retrieval Model ◽  
Flash Movie

scholarly journals SEMANTIC MOTION CONCEPT RETRIEVAL IN NON-STATIC BACKGROUND UTILIZING SPATIAL-TEMPORAL VISUAL INFORMATION

2013 ◽  
Vol 07 (01) ◽  
pp. 43-67 ◽  
Author(s):  
DIANTING LIU ◽  
MEI-LING SHYU
Keyword(s):  
Visual Information ◽  
Semantic Analysis ◽  
Temporal Information ◽  
Semantic Retrieval ◽  
Retrieval Model ◽  
Detection Model ◽  
Region Detection ◽  
Integral Density ◽  
Motion Map ◽  
Static Background

Motion concepts mean those concepts containing motion information such as racing car and dancing. In order to achieve high retrieval accuracy comparing with those static concepts such as car or person in semantic retrieval tasks, the temporal information has to be considered. Additionally, if a video sequence is captured by an amateur using a hand-held camera containing significant camera motion, the complexities of the uncontrolled backgrounds would aggravate the difficulty of motion concept retrieval. Therefore, the retrieval of semantic concepts containing motion in non-static background is regarded as one of the most challenging tasks in multimedia semantic analysis and video retrieval. To address such a challenge, this paper proposes a motion concept retrieval framework including a motion region detection model and a concept retrieval model that integrates the spatial and temporal information in video sequences. The motion region detection model uses a new integral density method (adopted from the idea of integral images) to quickly identify the motion regions in an unsupervised way. Specially, key information locations on video frames are first obtained as maxima and minima of the result of Difference of Gaussian (DoG) function. Then a motion map of adjacent frames is generated from the diversity of the outcomes from the Simultaneous Partition and Class Parameter Estimation (SPCPE) framework. The usage of the motion map is to filter key information locations into key motion locations (KMLs) that imply the regions containing motion. The motion map can also indicate the motion direction which guides the proposed "integral density" approach to locate the motion regions quickly and accurately. Based on the motion region detection model, moving object-level information is extracted for semantic retrieval. In the proposed conceptual retrieval model, temporally semantic consistency among the consecutive shots is analyzed and presented into a conditional probability model, which is then used to re-rank the similarity scores to improve the final retrieval results. The results of our proposed novel motion concept retrieval framework are not only illustrated visually demonstrating its robustness in non-static background, but also verified by the promising experimental results demonstrating that the concept retrieval performance can be improved by integrating the spatial and temporal visual information.

Semantic Retrieval for Ontology-Based Aircraft Fault Knowledge

2014 ◽  
Vol 945-949 ◽  
pp. 3410-3417
Author(s):  
Yang Zhou ◽  
Bin Tian ◽  
Zhao Yang Zeng ◽  
Zhi Yu Jia ◽  
Yan Zhou
Keyword(s):  
Semantic Distance ◽  
Semantic Retrieval ◽  
Retrieval Model ◽  
Retrieval Method ◽  
Conceptual Similarity ◽  
Aviation Maintenance ◽  
Distance Calculation ◽  
Ontology Model ◽  
Similarity Algorithm ◽  
Density Factor

With consideration of the actual requirement of fault knowledge retrieval in current aviation maintenance, semantic retrieval method for ontology-based aircraft fault knowledge is studied. Based on the concepts of semantic and semantic retrieval, it is proposed that the knowledge representation of ontology is applicable for semantic retrieval. On the basis of aircraft fault ontology model, the semantic retrieval model has been established, and the concept-matching similarity algorithm using semantic distance of ontology concepts is proposed. In the semantic retrieval method, the depth factor and density factor of ontology model are expressed, and the semantic distance calculation and the transformation function from semantic distance to conceptual similarity are proposed. The semantic retrieval research provides support for efficient application of ontology-based aircraft fault knowledge.

Research on Ontology-Based Chinese Semantic Retrieval Model

2014 ◽  
Author(s):  
Qingling Chang ◽  
Yuanchun Zhou ◽  
Lixiao Zheng ◽  
Shiting Xu ◽  
Jianhui Li ◽  
...  
Keyword(s):  
Semantic Retrieval ◽  
Retrieval Model

Study on Application of Domain Ontology in Semantic Information Retrieval

2013 ◽  
Vol 433-435 ◽  
pp. 1662-1665
Author(s):  
Huan Hai Yang ◽  
Ming Yu Sun
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity ◽  
Calculation Method ◽  
Semantic Information ◽  
Domain Ontology ◽  
Semantic Retrieval ◽  
Retrieval Model ◽  
Retrieval Method ◽  
Similarity Calculation ◽  
Semantic Information Retrieval

Considering weakness of the traditional retrieval method based on keyword matching, the paper introduced semantic into information retrieval, and proposed a semantic retrieval model based on ontology. The paper offered a construction method of domain ontology and implemented semantic reasoning using Jena and improved a semantic similarity calculation method.

Research of Intelligent Search Engine Based on Multi-Ontology

2012 ◽  
Vol 241-244 ◽  
pp. 1659-1663
Author(s):  
Shu Dong Zhang ◽  
Can Zhang ◽  
Jing Wang
Keyword(s):  
Semantic Web ◽  
Search Engine ◽  
Domain Knowledge ◽  
Recall Rate ◽  
Domain Ontology ◽  
Semantic Retrieval ◽  
Engine Model ◽  
Cross Domain ◽  
User Query ◽  
Primary Means

With the development of the Semantic Web, ontology has become the primary means of expression of many fields of knowledge. Introducing the Semantic Web technology into the field of search engine is a valuable research topic. In order to meet the complex semantic retrieval demands, the paper proposes a search engine model based on multi-domain ontology, the model using ontology mapping rewrite the user query to achieve multiple ontology query, and provide a richer and accurate semantic information for the retrieval of cross-domain knowledge; And the paper proposes a method of cross-domain ontology annotation, providing a basis for the user semantic retrieval. The experimental results show that the search results improve the precision and recall rate.

Ontology Based Domain Resource Semantic Retrieval Model

2013 ◽  
Vol 347-350 ◽  
pp. 2804-2808
Author(s):  
Shi Liu Wang ◽  
Gong Jie Zhang
Keyword(s):  
Experimental Results ◽  
Knowledge Organization ◽  
Semantic Matching ◽  
Semantic Retrieval ◽  
Retrieval Model ◽  
Retrieval Models ◽  
Semantic Level ◽  
Model Experiments ◽  
Proposed Model

Retrieval technology has been widely used, but most of the current retrieval models are based on the logic matching of characters without considering users query requirements and objectives in semantic level, which makes the retrieval results deviate from the retrieval intention of users. Based on the knowledge organization ontology, a semantic retrieval model is proposed. The proposed model abstracts semantic vectors in the form of concept and attributes, and establishes formulas for semantic matching. Based on the proposed model, experiments are performed, and the feasibility and effectiveness are proved by the experimental results.

The Semantic Retrieval Model of Manufacturing Resource Based on Rules and Similarity

2011 ◽  
Vol 130-134 ◽  
pp. 483-486
Author(s):  
Jun Ying Wei ◽  
Pei Si Zhong
Keyword(s):  
Information Content ◽  
Semantic Similarity ◽  
Semantic Retrieval ◽  
Retrieval Model ◽  
Node Density ◽  
Weighted Distance ◽  
Similarity Algorithm ◽  
Manufacturing Resource ◽  
The Common ◽  
Concept Attribute

Based on the common similarity algorithm between ontology concepts, this paper makes use of semantic weighted distance and introduces such factors as node density, concept attribute and concept information content, and presents an improved semantic similarity algorithm to make the measuring semantic similarity more accurate. Combined the similarity algorithm with SWRL, this paper establishes a semantic retrieval model of manufacturing resource based on rules and similarity , which applies the rules of SWRL to carry on conditional matching in the ontology concepts of manufacturing resource in order to retrieve more eligible results.

A Research on Linked Data-Based Chinese Semantic Retrieval Model

2014 ◽  
pp. 561-567
Author(s):  
Qingling Chang ◽  
Yuanchun Zhou ◽  
Shiting Xu ◽  
Jianhui Li ◽  
Baoping Yan
Keyword(s):  
Linked Data ◽  
Semantic Retrieval ◽  
Retrieval Model
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