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.

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.

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 Semantic Retrieval Method Based on Ontology

2014 ◽  
Vol 989-994 ◽  
pp. 2179-2183
Author(s):  
Qi Shen ◽  
Meng Zhang
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Language Processing ◽  
Search Method ◽  
Semantic Search ◽  
Semantic Retrieval ◽  
Retrieval Method ◽  
Similarity Algorithm ◽  
Tourism Information ◽  
Concept Similarity

Semantic retrieval method stands at the crossroads between Natural Language Processing and Machine Intelligent. This paper makes analysis on the semantic search method and research on concept similarity algorithm, and discusses the factor of weight’s influence on concept similarity as well. On this basis, this paper proposed a new semantic search method based on ontology, and apply it to the tourism information retrieval, which intellectualized tourism information retrieval service.

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

A Retrieval Method for Failure Mode Based on Semantic

2014 ◽  
Vol 610 ◽  
pp. 258-264 ◽  
Author(s):  
Zhi Ming Lei ◽  
Zi Li Wang ◽  
Yi Ren ◽  
Lin Lin Liu
Keyword(s):  
Failure Mode ◽  
Domain Knowledge ◽  
Semantic Retrieval ◽  
Retrieval Method

According to the characteristics of domain knowledge,a method for the determination of domain sememe is given,and a rule for the description of the domain semantic item is also given.Then,the method and the rule are applied to the failure domain.The model of semantic force is given in the process of semantic retrieval ,and the method for the calculation of semantic force is given.The method for the generation of the theme of failure mode is given.A retrieval method for failure mode based on semantic is given,and there is an example to prove its effectiveness.

scholarly journals Semantic Distance Spreading Across Entities in Linked Open Data

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 15
Author(s):  
Sultan Alfarhood ◽  
Susan Gauch ◽  
Kevin Labille
Keyword(s):  
Recommender Systems ◽  
Linked Data ◽  
Open Data ◽  
Cold Start ◽  
Semantic Distance ◽  
Linked Open Data ◽  
Experimental Results ◽  
Distance Calculation ◽  
Cold Start Problem

Recommender systems can utilize Linked Open Data (LOD) to overcome some challenges, such as the item cold start problem, as well as the problem of explaining the recommendation. There are several techniques in exploiting LOD in recommender systems; one approach, called Linked Data Semantic Distance (LDSD), considers nearby resources to be recommended by calculating a semantic distance between resources. The LDSD approach, however, has some drawbacks such as its inability to measure the semantic distance resources that are not directly linked to each other. In this paper, we first propose another variation of the LDSD approach, called wtLDSD, by extending indirect distance calculations to include the effect of multiple links of differing properties within LOD, while prioritizing link properties. Next, we introduce an approach that broadens the coverage of LDSD-based approaches beyond resources that are more than two links apart. Our experimental results show that approaches we propose improve the accuracy of the LOD-based recommendations over our baselines. Furthermore, the results show that the propagation of semantic distance calculation to reflect resources further away in the LOD graph extends the coverage of LOD-based recommender systems.

scholarly journals Fast Retrieval Method of Forestry Information Features Based on Symmetry Function in Communication Network

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 416
Author(s):  
Hui Wang ◽  
Jie Song
Keyword(s):  
Communication Network ◽  
Information Acquisition ◽  
Function Method ◽  
Personal Digital Assistant ◽  
Retrieval Model ◽  
Retrieval Performance ◽  
Retrieval Method ◽  
Symmetry Function ◽  
Forestry Resources ◽  
Acquisition Method

Aiming at the problem of insufficient integration and sharing of forestry information resources under the current communication network and the lack of the concept set of forestry information attributes, which leads to poor information retrieval performance, a fast retrieval method of forestry information features based on symmetry function is studied in depth, and the method is implemented by PDA (Personal Digital Assistant)-BA (Buliding Automation). Using the SED (Stream Editor) forestry information acquisition method under a communication network to collect forestry information, a forestry signal noise cancellation method based on symmetric function method is obtained. In order to improve the accuracy of forestry information acquisition, denoising of the signal in the information was carried out. Constructing forestry information data ontology, integrating forestry resources, establishing a conceptual set of forestry information attributes, distinguishing forestry information attributes, establishing a fast retrieval model of forestry information features based on the synonym library, and completing the fast retrieval of forestry information features. The experimental results show that the recall and precision of this method are 99.25% and 99.24%, respectively, and the retrieval performance is superior, which has a certain application value.

Semantic text-based image retrieval with multi-modality ontology and DBpedia

2017 ◽  
Vol 35 (6) ◽  
pp. 1191-1214 ◽  
Author(s):  
Yanti Idaya Aspura M.K. ◽  
Shahrul Azman Mohd Noah
Keyword(s):  
Image Retrieval ◽  
Semantic Distance ◽  
Image Features ◽  
Superior Performance ◽  
Semantic Retrieval ◽  
Content Type ◽  
Modality Approach ◽  
High Level ◽  
Image Collection ◽  
Practical Implications

Purpose The purpose of this study is to reduce the semantic distance by proposing a model for integrating indexes of textual and visual features via a multi-modality ontology and the use of DBpedia to improve the comprehensiveness of the ontology to enhance semantic retrieval. Design/methodology/approach A multi-modality ontology-based approach was developed to integrate high-level concepts and low-level features, as well as integrate the ontology base with DBpedia to enrich the knowledge resource. A complete ontology model was also developed to represent the domain of sport news, with image caption keywords and image features. Precision and recall were used as metrics to evaluate the effectiveness of the multi-modality approach, and the outputs were compared with those obtained using a single-modality approach (i.e. textual ontology and visual ontology). Findings The results based on ten queries show a superior performance of the multi-modality ontology-based IMR system integrated with DBpedia in retrieving correct images in accordance with user queries. The system achieved 100 per cent precision for six of the queries and greater than 80 per cent precision for the other four queries. The text-based system only achieved 100 per cent precision for one query; all other queries yielded precision rates less than 0.500. Research limitations/implications This study only focused on BBC Sport News collection in the year 2009. Practical implications The paper includes implications for the development of ontology-based retrieval on image collection. Originality value This study demonstrates the strength of using a multi-modality ontology integrated with DBpedia for image retrieval to overcome the deficiencies of text-based and ontology-based systems. The result validates semantic text-based with multi-modality ontology and DBpedia as a useful model to reduce the semantic distance.

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.

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