Journal articles: 'Semantic gap'

1

Loewenstein, Paul, and Andrew Fox. "Closing the semantic gap." Microprocessing and Microprogramming 24, no. 1-5 (August 1988): 767–72. http://dx.doi.org/10.1016/0165-6074(88)90146-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Li, Xirong, Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees G. M. Snoek, and Alberto Del Bimbo. "Socializing the Semantic Gap." ACM Computing Surveys 49, no. 1 (July 28, 2016): 1–39. http://dx.doi.org/10.1145/2906152.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Jain, Bhushan, Mirza Basim Baig, Dongli Zhang, Donald E. Porter, and Radu Sion. "Introspections on the Semantic Gap." IEEE Security & Privacy 13, no. 2 (March 2015): 48–55. http://dx.doi.org/10.1109/msp.2015.35.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Ahn, Byeongtae. "A Study on Image Search System using Semantics Based on Smartphone." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 381–85. http://dx.doi.org/10.35940/ijeat.e2841.0610521.

Full text

Abstract:

Image semantic retrieval has been a crux to bridge "semantic gap" between the simple visual features and the abundant semantics delivered by a image. Effective image retrieval using semantics is one of the major challenges in image retrieval. We suggest a semantic retrieval and clustering method of image using image annotation user interface. And also design and implement a image semantic search management system that facilitates image management and semantic retrieval, which fully relies on the MPEG-7 standard as information base, and using a native XML database, which is Berkeley DB XML

APA, Harvard, Vancouver, ISO, and other styles

5

Zhao, Rong, and W. I. Grosky. "Negotiating the semantic gap: from feature maps to semantic landscapes." Pattern Recognition 35, no. 3 (March 2002): 593–600. http://dx.doi.org/10.1016/s0031-3203(01)00062-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Horsthemke, William H., Daniela S. Raicu, and Jacob D. Furst. "Evaluation Challenges for Bridging Semantic Gap." International Journal of Healthcare Information Systems and Informatics 4, no. 1 (January 2009): 17–33. http://dx.doi.org/10.4018/jhisi.2009010102.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

D'Oro, Giuseppina. "THE GAP IS SEMANTIC, NOT EPISTEMOLOGICAL." Ratio 20, no. 2 (June 2007): 168–78. http://dx.doi.org/10.1111/j.1467-9329.2007.00355.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Liu, Zeng Rong, Zhi Li, and Xue Li Yu. "A Survey on Emotional Semantic Mapping in Image Retrieval." Advanced Materials Research 532-533 (June 2012): 1297–302. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1297.

Full text

Abstract:

Emotion plays an important role in the human perception and decision-making process. Human comprehension and perception of images is subjective, and not merely rely on lower-level visual features. Semantic gap is regarded as the most important challenge of image retrieval. In this paper, we analyzed the emotional features as well as emotional semantic description of images, which comes from the image emotional semantics retrieval framework. And also the mapping ways and means were summarized from image visual features to emotional semantics. Finally, the disadvantages of emotional semantic mapping and developing tendency were discussed.

APA, Harvard, Vancouver, ISO, and other styles

9

AL AGHBARI, ZAHER. "REGION-BASED SEMANTIC IMAGE CLASSIFICATION." International Journal of Image and Graphics 06, no. 03 (July 2006): 357–75. http://dx.doi.org/10.1142/s021946780600229x.

Full text

Abstract:

In the field of content-based image retrieval, there exist a gap between low-level descriptions of image content and the semantic needs of users to query image databases. This paper demonstrates an approach to image retrieval founded on classifying image regions hierarchically based on their semantics (e.g. sky, snow, rocks, etc.) that resemble peoples' perception rather than on low-level features (e.g. color, texture, shape, etc.). Particularly, we consider outdoor images and automatically classify their regions based on their semantics using a support vector machines (SVMs). The SVMs learns the semantics of specified classes from specific low-level feature of the test image regions. Image regions are, first, segmented using a hill-climbing approach. Then, those regions are classified by the SVMs. Such semantic classification allows the implementation of intuitive query interface. As we show in our experiments, the high precision of semantic classification justifies the feasibility of our approach.

APA, Harvard, Vancouver, ISO, and other styles

10

Mira, J., and J. M. Ferrández. "The internal observer and the semantic gap." Neurocomputing 72, no. 4-6 (January 2009): 789–91. http://dx.doi.org/10.1016/j.neucom.2008.10.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Nikhil Rasiwasia, P. J. Moreno, and N. Vasconcelos. "Bridging the Gap: Query by Semantic Example." IEEE Transactions on Multimedia 9, no. 5 (August 2007): 923–38. http://dx.doi.org/10.1109/tmm.2007.900138.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Katz, Graham. "semantic account of the stative adverb gap." ZAS Papers in Linguistics 17 (January 1, 2000): 135–51. http://dx.doi.org/10.21248/zaspil.17.2000.44.

Full text

Abstract:

It is argued that there is a surprising gap in the distribution of adverbial modifiers, namely that there are (practically) no adverbs that modify exclusively stative verbs. Given the general range of selectional restrictions associated with adverb/verb modification, this comes as a surprise. It is argued that this gap cannot be the result of standard selectional restrictions. An independently motivated account of the state-event verb contrast, in which state verbs are proposed to lack Davidsonian arguments is presented and argued to account for this stative adverb gap. Some apparent and real problems with the analysis are discussed.

APA, Harvard, Vancouver, ISO, and other styles

13

Ma, Ying, Lao Mo Zhang, and Jin Xing Ma. "A Content-Based Image Retrieval System with Image Semantic." Advanced Materials Research 159 (December 2010): 638–43. http://dx.doi.org/10.4028/www.scientific.net/amr.159.638.

Full text

Abstract:

With the development of information technology and multimedia technology, more and more images appear and have become a part of our daily life. Efficient image searching, storing, retrieval and browsing tools are in high need in various domains, including face and fingerprint recognition, publishing, medicine, architecture, remote sensing, fashion etc. Thus, many image retrieval systems have been developed to meet the need. The aim of content-based retrieval systems is to provide maximum support in bridging the semantic gap between the simplicity of available visual features and the richness of the user semantics. In this paper, we discuss the main technologies for reducing the semantic gap, namely, object-ontology, machine learning, relevance feedback.

APA, Harvard, Vancouver, ISO, and other styles

14

Guo, Yu Tang, and Chang Gang Han. "Automatic Image Annotation Using Semantic Subspace Graph Spectral Clustering Algorithm." Advanced Materials Research 271-273 (July 2011): 1090–95. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1090.

Full text

Abstract:

Due to the existing of the semantic gap, images with the same or similar low level features are possibly different on semantic level. How to find the underlying relationship between the high-level semantic and low level features is one of the difficult problems for image annotation. In this paper, a new image annotation method based on graph spectral clustering with the consistency of semantics is proposed with detailed analysis on the advantages and disadvantages of the existed image annotation methods. The proposed method firstly cluster image into several semantic classes by semantic similarity measurement in the semantic subspace. Within each semantic class, images are re-clustered with visual features of region Then, the joint probability distribution of blobs and words was modeled by using Multiple-Bernoulli Relevance Model. We can annotate a unannotated image by using the joint distribution. Experimental results show the the effectiveness of the proposed approach in terms of quality of the image annotation. the consistency of high-level semantics and low level features is efficiently achieved.

APA, Harvard, Vancouver, ISO, and other styles

15

JUNG, Min Young, and Sung Han PARK. "Video Retrieval System for Bridging the Semantic Gap." IEICE Transactions on Information and Systems E92-D, no. 12 (2009): 2516–19. http://dx.doi.org/10.1587/transinf.e92.d.2516.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Suwanit Rungratri, and Sasiporn Usanavasin. "Semantic based Approach Supporting CMMI Gap Analysis Process." Journal of Convergence Information Technology 7, no. 20 (November 30, 2012): 127–37. http://dx.doi.org/10.4156/jcit.vol7.issue20.16.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Xu, Xiaoyan, Bo Zhao, Xiaorui Wang, and Rongcai Zhao. "Research on Semantic Gap Problem of Virtual Machine." Wireless Personal Communications 97, no. 4 (August 17, 2017): 5983–6004. http://dx.doi.org/10.1007/s11277-017-4823-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Dorai, C., and S. Venkatesh. "Bridging the semantic gap with computational media aesthetics." IEEE Multimedia 10, no. 2 (April 2003): 15–17. http://dx.doi.org/10.1109/mmul.2003.1195157.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Pardede, Jasman, and Benhard Sitohang. "Reduce Semantic Gap in Content-Based Image Retrieval." Advanced Science Letters 23, no. 11 (November 1, 2017): 10664–71. http://dx.doi.org/10.1166/asl.2017.10126.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Hu, Xintao, Kaiming Li, Junwei Han, Xiansheng Hua, Lei Guo, and Tianming Liu. "Bridging the Semantic Gap via Functional Brain Imaging." IEEE Transactions on Multimedia 14, no. 2 (April 2012): 314–25. http://dx.doi.org/10.1109/tmm.2011.2172201.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Druschel, Peter. "Technical perspectiveNarrowing the semantic gap in distributed programming." Communications of the ACM 52, no. 11 (November 2009): 86. http://dx.doi.org/10.1145/1592761.1592784.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Marchesin, Stefano. "Developing unsupervised knowledge-enhanced models to reduce the semantic gap in information retrieval." ACM SIGIR Forum 55, no. 1 (June 2021): 1–2. http://dx.doi.org/10.1145/3476415.3476433.

Full text

Abstract:

In this thesis we tackle the semantic gap, a long-standing problem in Information Retrieval (IR). The semantic gap can be described as the mismatch between users' queries and the way retrieval models answer to such queries. Two main lines of work have emerged over the years to bridge the semantic gap: (i) the use of external knowledge resources to enhance the bag-of-words representations used by lexical models, and (ii) the use of semantic models to perform matching between the latent representations of queries and documents. To deal with this issue, we first perform an in-depth evaluation of lexical and semantic models through different analyses [Marchesin et al., 2019]. The objective of this evaluation is to understand what features lexical and semantic models share, if their signals are complementary, and how they can be combined to effectively address the semantic gap. In particular, the evaluation focuses on (semantic) neural models and their critical aspects. Each analysis brings a different perspective in the understanding of semantic models and their relation with lexical models. The outcomes of this evaluation highlight the differences between lexical and semantic signals, and the need to combine them at the early stages of the IR pipeline to effectively address the semantic gap. Then, we build on the insights of this evaluation to develop lexical and semantic models addressing the semantic gap. Specifically, we develop unsupervised models that integrate knowledge from external resources, and we evaluate them for the medical domain - a domain with a high social value, where the semantic gap is prominent, and the large presence of authoritative knowledge resources allows us to explore effective ways to address it. For lexical models, we investigate how - and to what extent - concepts and relations stored within knowledge resources can be integrated in query representations to improve the effectiveness of lexical models. Thus, we propose and evaluate several knowledge-based query expansion and reduction techniques [Agosti et al., 2018, 2019; Di Nunzio et al., 2019]. These query reformulations are used to increase the probability of retrieving relevant documents by adding to or removing from the original query highly specific terms. The experimental analyses on different test collections for Precision Medicine - a particular use case of Clinical Decision Support (CDS) - show the effectiveness of the proposed query reformulations. In particular, a specific subset of query reformulations allow lexical models to achieve top performing results in all the considered collections. Regarding semantic models, we first analyze the limitations of the knowledge-enhanced neural models presented in the literature. Then, to overcome these limitations, we propose SAFIR [Agosti et al., 2020], an unsupervised knowledge-enhanced neural framework for IR. SAFIR integrates external knowledge in the learning process of neural IR models and it does not require labeled data for training. Thus, the representations learned within this framework are optimized for IR and encode linguistic features that are relevant to address the semantic gap. The evaluation on different test collections for CDS demonstrate the effectiveness of SAFIR when used to perform retrieval over the entire document collection or to retrieve documents for Pseudo Relevance Feedback (PRF) methods - that is, when it is used at the early stages of the IR pipeline. In particular, the quantitative and qualitative analyses highlight the ability of SAFIR to retrieve relevant documents affected by the semantic gap, as well as the effectiveness of combining lexical and semantic models at the early stages of the IR pipeline - where the complementary signals they provide can be used to obtain better answers to semantically hard queries.

APA, Harvard, Vancouver, ISO, and other styles

23

Almela-Sánchez, Moisés. "Collocation and Selectional Preferences: A Frame-based Approach." Journal of English Studies 17 (December 18, 2019): 3. http://dx.doi.org/10.18172/jes.3905.

Full text

Abstract:

Most of the research conducted into collocation and semantic frames has dealt with these phenomena separately. The study of collocation has not figured prominently in the research agenda of frame semantics, and frame semantics has only sporadically been used as an analytical framework for collocation. This article is a contribution to narrowing the gap between the two fields. It does so by addressing key issues in the design of a frame-based approach to collocation, with a special focus on the relation between collocational patterns and semantic valency, and by providing arguments for the efficacy of the frame-semantic theoretical apparatus in explaining verb-adjective links that are not accounted for by the existing models of collocation. The methodology combines lexicographic resources as well as quantitative and qualitative analysis of examples and data from an English web corpus (ukWaC).

APA, Harvard, Vancouver, ISO, and other styles

24

Wei, Hui. "A Bio-Inspired Integration Method for Object Semantic Representation." Journal of Artificial Intelligence and Soft Computing Research 6, no. 3 (July 1, 2016): 137–54. http://dx.doi.org/10.1515/jaiscr-2016-0011.

Full text

Abstract:

Abstract We have two motivations. Firstly, semantic gap is a tough problem puzzling almost all sub-fields of Artificial Intelligence. We think semantic gap is the conflict between the abstractness of high-level symbolic definition and the details, diversities of low-level stimulus. Secondly, in object recognition, a pre-defined prototype of object is crucial and indispensable for bi-directional perception processing. On the one hand this prototype was learned from perceptional experience, and on the other hand it should be able to guide future downward processing. Human can do this very well, so physiological mechanism is simulated here. We utilize a mechanism of classical and non-classical receptive field (nCRF) to design a hierarchical model and form a multi-layer prototype of an object. This also is a realistic definition of concept, and a representation of denoting semantic. We regard this model as the most fundamental infrastructure that can ground semantics. Here a AND-OR tree is constructed to record prototypes of a concept, in which either raw data at low-level or symbol at high-level is feasible, and explicit production rules are also available. For the sake of pixel processing, knowledge should be represented in a data form; for the sake of scene reasoning, knowledge should be represented in a symbolic form. The physiological mechanism happens to be the bridge that can join them together seamlessly. This provides a possibility for finding a solution to semantic gap problem, and prevents discontinuity in low-order structures.

APA, Harvard, Vancouver, ISO, and other styles

25

Wang, Taehyung, Astushi Kitazawa, and Phillip Sheu. "Semantic software engineering." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1630012. http://dx.doi.org/10.1142/s2425038416300123.

Full text

Abstract:

One of the most challenging task in software development is developing software requirements. There are two types of software requirements — user requirement (mostly described by natural language) and system requirements (also called as system specifications and described by formal or semi-formal methods). Therefore, there is a gap between these two types of requirements because of inherently unique features between natural language and formal or semi-formal methods. We describe a semantic software engineering methodology using the design principles of SemanticObjects for object-relational software development with an example. We also survey other semantic approaches and methods for software and Web application development.

APA, Harvard, Vancouver, ISO, and other styles

26

Yan, Yan, and Jiwen Lu. "Guest editorial: Bridging the semantic gap in multimedia understanding." Neurocomputing 208 (October 2016): 1–2. http://dx.doi.org/10.1016/j.neucom.2016.05.051.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Ma, Hao, Jianke Zhu, Michael Rung-Tsong Lyu, and Irwin King. "Bridging the Semantic Gap Between Image Contents and Tags." IEEE Transactions on Multimedia 12, no. 5 (August 2010): 462–73. http://dx.doi.org/10.1109/tmm.2010.2051360.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Jinhui Tang, Zheng-Jun Zha, Dacheng Tao, and Tat-Seng Chua. "Semantic-Gap-Oriented Active Learning for Multilabel Image Annotation." IEEE Transactions on Image Processing 21, no. 4 (April 2012): 2354–60. http://dx.doi.org/10.1109/tip.2011.2180916.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Huang, Thomas S. "Can the World-Wide Web Bridge the Semantic Gap?" Image and Vision Computing 30, no. 8 (August 2012): 463–64. http://dx.doi.org/10.1016/j.imavis.2011.10.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Du, Yufeng, Quan Zhao, and Xiaochun Lu. "Semantic Extraction of Basketball Game Video Combining Domain Knowledge and In-Depth Features." Scientific Programming 2021 (September 4, 2021): 1–12. http://dx.doi.org/10.1155/2021/9080120.

Full text

Abstract:

The team sports game video features complex background, fast target movement, and mutual occlusion between targets, which poses great challenges to multiperson collaborative video analysis. This paper proposes a video semantic extraction method that integrates domain knowledge and in-depth features, which can be applied to the analysis of a multiperson collaborative basketball game video, where the semantic event is modeled as an adversarial relationship between two teams of players. We first designed a scheme that combines a dual-stream network and learnable spatiotemporal feature aggregation, which can be used for end-to-end training of video semantic extraction to bridge the gap between low-level features and high-level semantic events. Then, an algorithm based on the knowledge from different video sources is proposed to extract the action semantics. The algorithm gathers local convolutional features in the entire space-time range, which can be used to track the ball/shooter/hoop to realize automatic semantic extraction of basketball game videos. Experiments show that the scheme proposed in this paper can effectively identify the four categories of short, medium, long, free throw, and scoring events and the semantics of athletes’ actions based on the video footage of the basketball game.

APA, Harvard, Vancouver, ISO, and other styles

31

Okoye, Kingsley. "Semantic process mining: A conceptual application of main tools, framework and model analysis." International Journal of Hybrid Intelligent Systems 16, no. 3 (September 28, 2020): 127–47. http://dx.doi.org/10.3233/his-200286.

Full text

Abstract:

Semantics has been a major challenge when applying the process mining (PM) technique to real-time business processes. The several theoretical and practical efforts to bridge the semantic gap has spanned the advanced notion of the semantic-based process mining (SPM). Fundamentally, the SPM devotes its methods to the idea of making use of existing (semantic) technologies to support the analysis of PM techniques. In principle, the semantic-based process mining method is applied through the acquisition and representation of abstract knowledge about the domain processes in question. To this effect, this paper demonstrates how the semantic concepts and process modelling (reasoning) methods are used to improve the outcomes of PM techniques from the syntactic to a more conceptual level. To do this, the study proposes an SPM-based framework that shows to be intelligent with a high level of semantic reasoning aptitudes. Technically, this paper introduces a process mining approach that uses information (semantics) about different activities that can be found in any given process to make inferences and generate rules or patterns through the method for annotation, semantic reasoning, and conceptual assertions. In turn, the method is theoretically applied to enrich the informative values of the resultant models. Also, the study conducts and systematically reviews the current tools and methods that are used to support the outcomes of the process mining as well as evaluates the results of the different methods to determine the levels of impact and its implications for process mining.

APA, Harvard, Vancouver, ISO, and other styles

32

MÜLLER, CHRISTOF, IRYNA GUREVYCH, and MAX MÜHLHÄUSER. "CLOSING THE VOCABULARY GAP FOR COMPUTING TEXT SIMILARITY AND INFORMATION RETRIEVAL." International Journal of Semantic Computing 02, no. 02 (June 2008): 253–72. http://dx.doi.org/10.1142/s1793351x08000452.

Full text

Abstract:

This paper studies the integration of lexical semantic knowledge in two related semantic computing tasks: ad-hoc information retrieval and computing text similarity. For this purpose, we compare the performance of two algorithms: (i) using semantic relatedness, and (ii) using a conventional extended Boolean model [13] with additional query expansion. For the evaluation, we use two different test collections in the German language especially suitable to study the vocabulary gap problem: (i) GIRT [5] for the information retrieval task, and (ii) a collection of descriptions of professions built to evaluate a system for electronic career guidance in the information retrieval and text similarity tasks. We found that integrating lexical semantic knowledge increases the performance for both tasks. On the GIRT corpus, the performance is improved only for short queries. The performance on the collection of professional descriptions is improved, but crucially depends on the accurate preprocessing of the natural language essays employed as topics.

APA, Harvard, Vancouver, ISO, and other styles

33

Liu, Yongmei, Tanakrit Wongwitit, and Linsen Yu. "Automatic Image Annotation Based on Scene Analysis." International Journal of Image and Graphics 14, no. 03 (July 2014): 1450012. http://dx.doi.org/10.1142/s0219467814500120.

Full text

Abstract:

Automatic image annotation is an important and challenging job for image analysis and understanding such as content-based image retrieval (CBIR). The relationship between the keywords and visual features is too complicated due to the semantic gap. We present an approach of automatic image annotation based on scene analysis. With the constrain of scene semantics, the correlation between keywords and visual features becomes simpler and clearer. Our model has two stages of process. The first stage is training process which groups training image data set into semantic scenes using the extracted semantic feature and visual scenes constructed from the calculation distances of visual features for every pairs of training images by using Earth mover's distance (EMD). Then, combine a pair of semantic and visual scene together and apply Gaussian mixture model (GMM) for all scenes. The second stage is to test and annotate keywords for test image data set. Using the visual features provided by Duygulu, experimental results show that our model outperforms probabilistic latent semantic analysis (PLSA) & GMM (PLSA&GMM) model on Corel5K database.

APA, Harvard, Vancouver, ISO, and other styles

34

Ri, Chang Yong, and Min Yao. "Research on High-Level Semantic Image Retrieval." Advanced Materials Research 268-270 (July 2011): 1427–32. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1427.

Full text

Abstract:

This paper presented the key problems to shorten “semantic gap” between low-level visual features and high-level semantic features to implement high-level semantic image retrieval. First, introduced ontology based semantic image description and semantic extraction methods based on machine learning. Then, illustrated image grammar on the high-level semantic image understanding and retrieval, and-or graph and context based methods of semantic image. Finally, we discussed the development directions and research emphases in this field.

APA, Harvard, Vancouver, ISO, and other styles

35

Li, Xiangtai, Houlong Zhao, Lei Han, Yunhai Tong, Shaohua Tan, and Kuiyuan Yang. "Gated Fully Fusion for Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11418–25. http://dx.doi.org/10.1609/aaai.v34i07.6805.

Full text

Abstract:

Semantic segmentation generates comprehensive understanding of scenes through densely predicting the category for each pixel. High-level features from Deep Convolutional Neural Networks already demonstrate their effectiveness in semantic segmentation tasks, however the coarse resolution of high-level features often leads to inferior results for small/thin objects where detailed information is important. It is natural to consider importing low level features to compensate for the lost detailed information in high-level features. Unfortunately, simply combining multi-level features suffers from the semantic gap among them. In this paper, we propose a new architecture, named Gated Fully Fusion(GFF), to selectively fuse features from multiple levels using gates in a fully connected way. Specifically, features at each level are enhanced by higher-level features with stronger semantics and lower-level features with more details, and gates are used to control the propagation of useful information which significantly reduces the noises during fusion. We achieve the state of the art results on four challenging scene parsing datasets including Cityscapes, Pascal Context, COCO-stuff and ADE20K.

APA, Harvard, Vancouver, ISO, and other styles

36

Wan, Min, and Kun Liu. "A Research of the Essence of SQL Injection Attacks Vulnerability." Applied Mechanics and Materials 719-720 (January 2015): 935–40. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.935.

Full text

Abstract:

Semantic Gap problem is the essence of the SQL Injection Attacks vulnerability in Web applications. Web application loses the semantic information while the SQL statement is constructed dynamically. This paper analyzes the cause of the SQLIA vulnerability. And then it analyzes several suggested techniques, such as the filtering techniques and the static analysis, and points out their drawbacks in the SOLIA prevention, which leads to the conclusion that the key problem for the eradication of SQLIA is to solve the semantic gap problem causing by the unstructured SQL statement in the process of constructing a Web system dynamically.

APA, Harvard, Vancouver, ISO, and other styles

37

Li, Pu, Yuncheng Jiang, Ju Wang, and Zhilei Yin. "Semantic Extension of Query for the Linked Data." International Journal on Semantic Web and Information Systems 13, no. 4 (October 2017): 109–33. http://dx.doi.org/10.4018/ijswis.2017100106.

Full text

Abstract:

With the advent of Big Data Era, users prefer to get knowledge rather than pages from Web. Linked Data, a new form of knowledge representation and publishing described by RDF, can provide a more precise and comprehensible semantic structure to satisfy the aforementioned requirement. Further, the SPARQL query language for RDF is the foundation of many current researches about Linked Data querying. However, these SPARQL-based methods cannot fully express the semantics of the query, so they cannot unleash the potential of Linked Data. To fill this gap, this paper designs a new querying method which extends the SPARQL pattern. Firstly, the authors present some new semantic properties for predicates in RDF triples and design a Semantic Matrix for Predicates (SMP). They then establish a well-defined framework for the notion of Semantically-Extended Query Model for the Linked Data (SEQMLD). Moreover, the authors propose some novel algorithms for executing queries by integrating semantic extension into SPARQL pattern. Lastly, experimental results show that the authors' proposal has a good generality and performs better than some of the most representative similarity search methods.

APA, Harvard, Vancouver, ISO, and other styles

38

Kropff, Emilio, and Alessandro Treves. "Semantic cognition: Distributed, but then attractive." Behavioral and Brain Sciences 31, no. 6 (December 2008): 718–19. http://dx.doi.org/10.1017/s0140525x08005943.

Full text

Abstract:

AbstractThe parallel distributed processing (PDP) perspective brings forward the important point that all semantic phenomena are based on analog underlying mechanisms, involving the weighted summation of multiple inputs by individual neurons. It falls short of indicating, however, how the essentially discrete nature of semantic processing may emerge at the cognitive level. Bridging this gap probably requires attractor networks.

APA, Harvard, Vancouver, ISO, and other styles

39

Sudha, D., and J. Priyadarshini. "Reducing Semantic Gap in Video Retrieval with Fusion: A Survey." Procedia Computer Science 50 (2015): 496–502. http://dx.doi.org/10.1016/j.procs.2015.04.020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Celma, Òscar, and Xavier Serra. "FOAFing the music: Bridging the semantic gap in music recommendation." Journal of Web Semantics 6, no. 4 (November 2008): 250–56. http://dx.doi.org/10.1016/j.websem.2008.09.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

LI, B., J. ERRICO, H. PAN, and I. SEZAN. "Bridging the semantic gap in sports video retrieval and summarization." Journal of Visual Communication and Image Representation 15, no. 3 (September 2004): 393–424. http://dx.doi.org/10.1016/s1047-3203(04)00034-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Schofield, P. N., G. V. Gkoutos, M. Gruenberger, J. P. Sundberg, and J. M. Hancock. "Phenotype ontologies for mouse and man: bridging the semantic gap." Disease Models & Mechanisms 3, no. 5-6 (April 28, 2010): 281–89. http://dx.doi.org/10.1242/dmm.002790.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Kim, Hwanju, Hyeontaek Lim, Jinkyu Jeong, Heeseung Jo, Joonwon Lee, and Seungryoul Maeng. "Transparently bridging semantic gap in CPU management for virtualized environments." Journal of Parallel and Distributed Computing 71, no. 6 (June 2011): 758–73. http://dx.doi.org/10.1016/j.jpdc.2010.11.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Sieber, Tanja, and Matthias Kammerer. "Bridging the gap between data and semiotics: semantic data model." International Journal of Teaching and Case Studies 1, no. 4 (2008): 283. http://dx.doi.org/10.1504/ijtcs.2008.022983.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Ciocca, Gianluigi, Claudio Cusano, Simone Santini, and Raimondo Schettini. "Halfway through the semantic gap: Prosemantic features for image retrieval." Information Sciences 181, no. 22 (November 2011): 4943–58. http://dx.doi.org/10.1016/j.ins.2011.06.025.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Belkhatir, Mohammed. "A three-level architecture for bridging the image semantic gap." Multimedia Systems 17, no. 2 (November 16, 2010): 135–48. http://dx.doi.org/10.1007/s00530-010-0207-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Mousavi, S. Hamzeh, and Mohammad Amouzadeh. "‘I hear the smell of roses’." Review of Cognitive Linguistics 18, no. 2 (December 4, 2020): 397–427. http://dx.doi.org/10.1075/rcl.00065.mou.

Full text

Abstract:

Abstract This paper investigates the synaesthetic constructions in Persian with the aim of finding out what motivates them despite their incongruous syntactic-semantic assignments. It is argued that these paradoxical elements require a metaphoric/metonymic frame to assign appropriate lexical units (LUs) to their corresponding syntactic categories (NP + rɑ +VP and NP + AP). The discrepancy derives from the semantic aspects for which frame semantics provides two types of explanations: internal and external frame factors. Internal factors deal with the metaphoric/metonymic compatibility or similarity between frames, while external factors underline the use of lexical items from one subframe to fill the vocabulary gap of a different subframe. The argument is that this gap owes much to the indirect contact between the Phenomenon (e.g., an odorous substance) and the Body-part (e.g., nose) that perceives it. In short, the analysis of our data reveals that synaesthesia is not only an economical strategy for modifying the senses, but also a natural mental strategy for interpreting vague experiences. A configuration of the incongruent construction of ‘smell’ and ‘hearing’ will be proposed to generalize such an analysis.

APA, Harvard, Vancouver, ISO, and other styles

48

Messaoudi, Wassim, Mohamed Farah, and Imed Riadh Farah. "Fuzzy Spatio-Spectro-Temporal Ontology for Remote Sensing Image Annotation and Interpretation: Application to Natural Risks Assessment." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, no. 05 (October 2019): 815–40. http://dx.doi.org/10.1142/s0218488519500363.

Full text

Abstract:

This research deals with semantic interpretation of Remote Sensing Images (RSIs) using ontologies which are considered as one of the main challenging methods for modeling high-level knowledge, and reducing the semantic gap between low-level features and high-level semantics of an image. In this paper, we propose a new ontology which allows the annotation as well as the interpretation of RSI with respect to natural risks, while taking into account uncertainty of data, object dynamics in natural scenes, and specificities of sensors. In addition, using this ontology, we propose a methodology to (i) annotate the semantic content of RSI, and (ii) deduce the susceptibility of the land cover to natural phenomena such as erosion, floods, and fires, using case-based reasoning supported by the ontology. This work is tested using LANDSAT and SPOT images of the region of Kef which is situated in the north-west of Tunisia. Results are rather promising.

APA, Harvard, Vancouver, ISO, and other styles

49

Hsu, I.-Ching, Jang Yang Lee, Der-Chen Huang, and Kuan-Yang Lai. "Integrating Semantic Web technologies with XML Schema using role-mapping annotations." Electronic Library 32, no. 2 (April 1, 2014): 147–69. http://dx.doi.org/10.1108/el-07-2012-0096.

Full text

Abstract:

Purpose – XML Schema is used to define schema of XML documents that have become standards for data exchange in various Web-based information applications. The main problem of XML Schema is that it emphasizes syntax and format rather than semantics and knowledge representation. Hence, even though having the advantage of describing the structure and constraining the contents of XML documents, XML Schema lacks the computer-interpretability to support knowledge representation for existing information systems. The purpose of this study is to propose role-mapping annotations for XML Schema (RMAXS) to integrate Semantic Web with XML Schema, which allows the facilitation interoperability between adjoining layers of the Semantic Web stack. Design/methodology/approach – The XML, XML Schema, ontology, and rule can be completely integrated into a multi-layered intelligent framework (MIF) for XML-based applications in the current web environment. This work presents a semantic-role-mapping intelligent system, called SRMIS, based on the MIF. SRMIS consists of XML-based document repository, search engine, inference engine and transformation engine, which provides different approaches to present the various metadata and knowledge representations. Findings – The traditional Semantic Web stack has three gaps between adjoining layers. The first gap, between the XML and XML Schema layers can be bridged with an XMLSchema-instance mechanism. The third gap, between the ontology and rule layers can be connected by building rules on top of ontologies. This study proposes RMAXS to couple the second gap, between the XML schema and ontology layers. The proposed multi-layered intelligent framework (MIF) adopts these coupling technologies to facilitate interoperability between adjoining layers. Therefore, the XML, XML Schema, ontology, and rule can be completely integrated into the MIF for intelligent applications in the web environment. Practical implications – To demonstrate the SRMIS applications, this work implements a prototype that helps researchers to find interested papers. Originality/value – This work presents a semantic-role-mapping intelligent system, called SRMIS, based on the MIF. SRMIS consists of XML-based document repository, search engine, inference engine and transformation engine, which provides different approaches to present the various metadata and knowledge representations. The proposed SRMIS can be applied in various application domains.

APA, Harvard, Vancouver, ISO, and other styles

50

Lin, Dazhen, Donglin Cao, Yanping Lv, and Zheng Cai. "GIF Video Sentiment Detection Using Semantic Sequence." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/6863174.

Full text

Abstract:

With the development of social media, an increasing number of people use short videos in social media applications to express their opinions and sentiments. However, sentiment detection of short videos is a very challenging task because of the semantic gap problem and sequence based sentiment understanding problem. In this context, we propose a SentiPair Sequence based GIF video sentiment detection approach with two contributions. First, we propose a Synset Forest method to extract sentiment related semantic concepts from WordNet to build a robust SentiPair label set. This approach considers the semantic gap between label words and selects a robust label subset which is related to sentiment. Secondly, we propose a SentiPair Sequence based GIF video sentiment detection approach that learns the semantic sequence to understand the sentiment from GIF videos. Our experiment results on GSO-2016 (GIF Sentiment Ontology) data show that our approach not only outperforms four state-of-the-art classification methods but also shows better performance than the state-of-the-art middle level sentiment ontology features, Adjective Noun Pairs (ANPs).

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Semantic gap'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles