Relevant bibliographies by topics / Semantic gap

Academic literature on the topic 'Semantic gap'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Semantic gap"

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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.

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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.

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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.

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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.

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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
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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.

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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.

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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.

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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.

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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.
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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.

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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.
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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.

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Dissertations / Theses on the topic "Semantic gap"

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Ehrig, Marc. "Ontology alignment : bridging the semantic gap /." New York, NY : Springer, 2007. http://www.loc.gov/catdir/toc/fy0707/2006928852.html.

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Beebe, Caroline. "Bridging the semantic gap : exploring descriptive vocabulary for image structure /." [Bloomington, Ind.] : Indiana University, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3234479.

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Thesis (Ph.D.)--Indiana University, School of Library and Information Science, 2006.
Source: Dissertation Abstracts International, Volume: 67-09, Section: A, page: 3205. Title from PDF t.p. (viewed Oct. 30, 2008). Adviser: Elin K. Jacob.
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Stark, Adam. "Musicians and machines : bridging the semantic gap in live performance." Thesis, Queen Mary, University of London, 2011. http://qmro.qmul.ac.uk/xmlui/handle/123456789/15050.

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This thesis explores the automatic extraction of musical information from live performances - with the intention of using that information to create novel, responsive and adaptive performance tools for musicians. We focus specifically on two forms of musical analysis - harmonic analysis and beat tracking. We present two harmonic analysis algorithms - specifically we present a novel chroma vector analysis technique which we later use as the input for a chord recognition algorithm. We also present a real-time beat tracker, based upon an extension of state of the art non-causal models, that is computationally efficient and capable of strong performance compared to other models. Furthermore, through a modular study of several beat tracking algorithms we attempt to establish methods to improve beat tracking and apply these lessons to our model. Building upon this work, we show that these analyses can be combined to create a beat-synchronous musical representation, with harmonic information segmented at the level of the beat. We present a number of ways of calculating these representations and discuss their relative merits. We proceed by introducing a technique, which we call Performance Following, for recognising repeated patterns in live musical performances. Through examining the real-time beat-synchronous musical representation, this technique makes predictions of future harmonic content in musical performances with no prior knowledge in the form of a score. Finally, we present a number of potential applications for live performances that incorporate the real-time musical analysis techniques outlined previously. The applications presented include audio effects informed by beat tracking, a technique for synchronising video to a live performance, the use of harmonic information to control visual displays and an automatic accompaniment system based upon our performance following technique.
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Alirezaie, Marjan. "Bridging the Semantic Gap between Sensor Data and Ontological Knowledge." Doctoral thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-45908.

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The rapid growth of sensor data can potentially enable a better awareness of the environment for humans. In this regard, interpretation of data needs to be human-understandable. For this, data interpretation may include semantic annotations that hold the meaning of numeric data. This thesis is about bridging the gap between quantitative data and qualitative knowledge to enrich the interpretation of data. There are a number of challenges which make the automation of the interpretation process non-trivial. Challenges include the complexity of sensor data, the amount of available structured knowledge and the inherent uncertainty in data. Under the premise that high level knowledge is contained in ontologies, this thesis investigates the use of current techniques in ontological knowledge representation and reasoning to confront these challenges. Our research is divided into three phases, where the focus of the first phase is on the interpretation of data for domains which are semantically poor in terms of available structured knowledge. During the second phase, we studied publicly available ontological knowledge for the task of annotating multivariate data. Our contribution in this phase is about applying a diagnostic reasoning algorithm to available ontologies. Our studies during the last phase have been focused on the design and development of a domain-independent ontological representation model equipped with a non-monotonic reasoning approach with the purpose of annotating time-series data. Our last contribution is related to coupling the OWL-DL ontology with a non-monotonic reasoner. The experimental platforms used for validation consist of a network of sensors which include gas sensors whose generated data is complex. A secondary data set includes time series medical signals representing physiological data, as well as a number of publicly available ontologies such as NCBO Bioportal repository.
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Jin, Yuan. "Bridging the ontological gap between semantic web and the RESTful web services." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97115.

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Data are produced in large quantities and in various forms around the globe everyday. Researchers advance their research depending on the availability of necessary data and the discovery of them. As people's demand to manage the data grows, however, three problems appear to hinder the attempts to effectively leverage the data. One is the semantic heterogeneity found in linking different data sources. Database designers create data with different semantics; even data within the same domain may differ in meaning. If users want to acquire all the obtainable information, they have to write different queries with different semantics. One solution to such a problem is the use of ontology. An ontology is defined as a specification for the concepts of an agent (or a community of agents) and the relationships between them (Gruber 1995). Concepts and relationships between concepts are extracted from the data to form knowledge network. Other parties wishing to connect their data to the knowledge network could share, enrich and distribute the vocabulary of the ontology. Users could also write queries to the ontology by any RDF query language (Brickly 2004). The use of ontology is part of the Web 3.0's effort to provide a semantic-sensitive global knowledge network. A second problem is about new ways to access data resources with ontology information. People used to build application-specific user interfaces to databases, which were offline. Now many choose to expose data in Web Services. Web services are a system to provide HTTP-based remote request calling services that are described in a machine-readable format (Haas and Brown 2004). They usually provide application (or web) programming interfaces to manage data. The question is Web Services are born in a world of applications relying on conventional ways to connect to data sources. For example, D2RQ (Bizer and Seaborne 2004) translates queries against ontology to SQL queries and it depends on JDBC to read from relational databases. Now the interfaces for these data sources are going to be changed. The Semantic Web world faces the challenge to lose data sources. If Web Services were going to spread over the Internet one day, this lack of connection would hold back me from applying the ontology to connect to heterogeneous data sources.A third problem (or constraint) is working within the specific project domain. I embed this within a humanities cyberinfrastructure that integrates Chinese biographical, historical and geographical data. The data sources come in various forms – local and remote relational databases and, RESTful Web Services. Working with both legacy databases and the new web application interfaces narrowed down my choice of solutions. Commercial products provide ways to "ontologicalize" the Web Services. I argue that they are heavyweight (e.g. unnecessary components bound with the product) and cost-prohibitive for small-scale projects like ours. Several mature open source solutions featuring working with relational databases provide no or very limited access to Web Services. For example, no clue is found in D2RQ to join Web Services into their system, while OpenLink Virtuoso answers calls for SOAP but cannot manage data from RESTful Web Services. I propose to build a connection between ontologies and Web Services. I devise the metadata to represent non-RDF Web Services in ontology, and I revise the code and create new data structures in D2RQ to support ontology queries to data from RESTful Web Services.
Les données sont produites en grandes quantités et sous diverses formes dans le monde et tous les jours. Les chercheurs avancer leurs recherches en fonction de la disponibilité des données nécessaires et la découverte de leur. Comme la demande des gens pour gérer les données croît, toutefois, trois problèmes semblent entraver les tentatives d'exploiter efficacement les données. La première est l'hétérogénéité sémantique dans reliant différentes sources de données. Concepteurs de créer des données de base de données avec une sémantique différente; même les données dans le même domaine peuvent avoir une signification différente. Si les utilisateurs souhaitent obtenir toute l'information obtenue, ils doivent écrire des requêtes différentes avec une sémantique différente. Une solution à ce problème est l'utilisation de l'ontologie. Une ontologie est définie comme une spécification pour les concepts d'un agent (ou d'une communauté d'agents) et les relations entre eux (Gruber 1995). Concepts et les relations entre les concepts sont extraites des données pour former réseau de connaissances. Les autres parties qui souhaitent se connecter leurs données au réseau de connaissances pourraient partager, enrichir et diffuser le vocabulaire de l'ontologie. Les utilisateurs peuvent aussi écrire des requêtes à l'ontologie par une requête RDF langue (Brickley 2004). L'utilisation de l'ontologie est une partie de l'effort de Web 3.0 pour fournir un réseau de connaissances sémantiques sensibles mondiale.Un deuxième problème est sur le point de nouvelles façons d'accéder aux données des ressources de l'information ontologie. Les gens de construire des interfaces utilisateur des applications spécifiques aux bases de données, qui ont été mises hors. Maintenant, de nombreux fournisseurs de données choisir pour exposer les données des services web. Les services web sont un système pour fournir la demande HTTP à distance d'appeler les services qui sont décrits dans un format lisible par machine (Haas and Brown 2004). Par exemple, D2RQ (Bizer and Seaborne 2004) se traduit par des requêtes sur l'ontologie de requêtes SQL, et cela dépend de JDBC pour lire à partir des bases de données relationnelles. Maintenant, les interfaces de ces sources de données vont être modifiées. Le monde du web sémantique doit relever le défi de perdre des sources de données. Si les services web ont été va se répandre sur Internet, un jour, ce manque de connexion tiendrait nous ramène de l'application de l'ontologie de se connecter à des sources de données hétérogènes.Un troisième problème (ou contrainte) est travailler dans le domaine des projets spécifiques. Nous incorporer cela dans une cyber-infrastructure qui intègre les sciences humaines chinois biographiques, des données historiques et géographiques. Les sources de données prennent des formes diverses - bases de données locales et distantes relationnelles et, les services web RESTful. Travailler avec les anciennes bases de données à la fois et l'application web de nouvelles interfaces rétréci vers le bas notre choix de solutions. Produits commerciaux offrent des moyens à ontologicalize les services web. Nous soutenons qu'ils sont lourds (par exemple, les composants inutiles liés au produit) et ils sont coûteuse pour les projets à petite échelle, comme notre projet. Plusieurs solutions open source mature offrant de travailler avec des bases de données relationnelles ne fournissent pas ou peu accès aux services Web. Nous proposons de construire un lien entre les ontologies et les services web. Nous trouver les métadonnées pour représenter les non-RDF services web dans l'ontologie, et nous revoir le code et créer de nouvelles structures de données en D2RQ à l'appui des requêtes ontologie à partir des données des services web RESTful.
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Noonan, Krist Anthony. "Conceptualising the void : Bridging the gap between semantic cognition and cognitive control." Thesis, University of Manchester, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.517858.

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Khatri, Vijay. "Bridging the spatio-temporal semantic gap: A theoretical framework, evaluation and a prototype system." Diss., The University of Arizona, 2002. http://hdl.handle.net/10150/280030.

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The objective of this research is to formally define a spatio-temporal conceptual model that captures data semantics required for temporal and geospatial applications. We show how the proposed model provides a metaphor that bridges the semantic gap between the real world and its spatio-temporal representation in information systems. Our multi-methodological research approach includes: (i) formally defining a spatio-temporal semantic model called ST USM (Spatio-Temporal Unifying Semantic Model); (ii) evaluating the proposed model using a case study and a laboratory study; and (iii) demonstrating practicality of our proposed model using a proof-of-concept prototype system. We describe a spatio-temporal conceptual modeling approach--applicable to any conventional conceptual model--that incorporates sequenced and nonsequenced space/time semantics. We have applied our annotation-based approach to the Unifying Semantic Model (USM)--a conventional conceptual model--to propose ST USM. ST USM is an upward-compatible, snapshot reducible, annotation-based spatio-temporal conceptual model that can comprehensively capture semantics related to space and time without adding any new spatio-temporal constructs. We provide formal semantics of ST USM via a mapping to conventional USM and constraints (expressed in first-order logic), from which the logical schema can be derived. To evaluate the proposed model, we conducted a case study at the US Geological Survey that helped us assess the extent to which the proposed formalism helps capture all the spatio-temporal data semantics for an application. We show that ST USM is ontologically expressive and leads to schemas that completely capture the requisite spatio-temporal semantics. We conducted a laboratory study and found that an annotation-based approach to capturing the spatio-temporal semantics does not adversely impact the schema comprehension as compared with conventional conceptual models (e.g., USM). This implies that annotations provide an intuitive straightforward mechanism to capture the spatio-temporal requirements and can be usefully employed to capture spatio-temporal semantics accurately. We describe DISTIL (DesIgn-support environment for SpaTIo-temporaL data), a web-based conceptual modeling prototype system that can help capture semantics of spatio-temporal data. Using DISTIL, we demonstrate that the annotation-based approach to capturing spatio-temporal requirements is straightforward to implement, satisfies ontology-based and cognition-based requirements, and integrates seamlessly into existing database design methodologies.
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Mbipom, Blessing. "Knowledge driven approaches to e-learning recommendation." Thesis, Robert Gordon University, 2018. http://hdl.handle.net/10059/3121.

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Learners often have difficulty finding and retrieving relevant learning materials to support their learning goals because of two main challenges. The vocabulary learners use to describe their goals is different from that used by domain experts in teaching materials. This challenge causes a semantic gap. Learners lack sufficient knowledge about the domain they are trying to learn about, so are unable to assemble effective keywords that identify what they wish to learn. This problem presents an intent gap. The work presented in this thesis focuses on addressing the semantic and intent gaps that learners face during an e-Learning recommendation task. The semantic gap is addressed by introducing a method that automatically creates background knowledge in the form of a set of rich learning-focused concepts related to the selected learning domain. The knowledge of teaching experts contained in e-Books is used as a guide to identify important domain concepts. The concepts represent important topics that learners should be interested in. An approach is developed which leverages the concept vocabulary for representing learning materials and this influences retrieval during the recommendation of new learning materials. The effectiveness of our approach is evaluated on a dataset of Machine Learning and Data Mining papers, and our approach outperforms benchmark methods. The results confirm that incorporating background knowledge into the representation of learning materials provides a shared vocabulary for experts and learners, and this enables the recommendation of relevant materials. We address the intent gap by developing an approach which leverages the background knowledge to identify important learning concepts that are employed for refining learners' queries. This approach enables us to automatically identify concepts that are similar to queries, and take advantage of distinctive concept terms for refining learners' queries. Using the refined query allows the search to focus on documents that contain topics which are relevant to the learner. An e-Learning recommender system is developed to evaluate the success of our approach using a collection of learner queries and a dataset of Machine Learning and Data Mining learning materials. Users with different levels of expertise are employed for the evaluation. Results from experts, competent users and beginners all showed that using our method produced documents that were consistently more relevant to learners than when the standard method was used. The results show the benefits in using our knowledge driven approaches to help learners find relevant learning materials.
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Zhang, Lei [Verfasser], and R. [Akademischer Betreuer] Studer. "Semantic Annotation and Search: Bridging the Gap between Text, Knowledge and Language / Lei Zhang ; Betreuer: R. Studer." Karlsruhe : KIT-Bibliothek, 2018. http://d-nb.info/1154856771/34.

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Adams, Brett. "Mapping the semantic landscape of film : computational extraction of indices through film grammar /." Curtin University of Technology, School of Computing, 2002. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=13407.

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This thesis presents work aimed at exploiting the grammar of film for the purpose of automated film understanding, and addresses the semantic gap that exists between the simplicity of features that can be currently computed in automated content indexing systems and the richness of semantics in user queries posed for media search and retrieval. The problem is set within the broader context of the need for enabling technologies for multimedia content management, and arises in response to the growing presence of multimedia data made possible by advances in storage, processing, and transmission technologies. The first demonstration of this philosophy uses the attributes of motion and shot length to define and compute a novel measure of film tempo. Tempo flow plots are defined and derived for a number of full length movies, and edge analysis is performed leading to the extraction of dramatic story sections and events signaled by their unique tempo. In addition to the development of this computable tempo measure, a study is conducted as to the usefulness of biasing it toward either of its constituents, namely motion or shot length. Thirdly, a refinement is made to the shot length normalizing mechanism, driven by the peculiar characteristics of shot length distribution exhibited by movies. The next aspect of film examined is film rhythm. In the rhythm model presented, motion behaviour is classified as being either nonexistent, fluid or staccato for a given shot. Shot neighbourhoods in movies are then grouped by proportional makeup of these motion behavioural classes to yield seven high-level rhythmic arrangements that prove adept at indicating likely scene content (e.g., dialogue or chase sequence). The second part of the investigation presents a novel computational model to detect editing patterns as either metric, accelerated, decelerated, or free.
It is also found that combined motion and editing rhythms allow us to determine that the media content has changed and hypothesize as to why this is so. Three such categories are presented along with their efficacy for capturing useful film elements (e.g., scene change precipitated by plot event). Finally, the first attempt to extract narrative structure, the prevalent 3-Act storytelling paradigm in film, is detailed. The identification of act boundaries in the narrative allows for structuralizing film at a level far higher than existing segmentation frameworks which include shot detection and scene identification, and provides a reliable basis for inferences about the semantic content of dramatic events in film. Additionally, the narrative constructs identified have analogues in many other domains, including news, training video, sitcoms, etc., making these ideas widely applicable. A novel act boundary posterior function for Act 1 and 2 is derived using a Bayesian formulation under guidance from film grammar, tested under many configurations, and the results are reported for experiments involving 25 full-length movies. The framework is shown to have a role in both the automatic and semi-interactive setting for semantic analysis of film.
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Books on the topic "Semantic gap"

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Kwaśnicka, Halina, and Lakhmi C. Jain, eds. Bridging the Semantic Gap in Image and Video Analysis. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73891-8.

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Paliouras, Georgios. Knowledge-Driven Multimedia Information Extraction and Ontology Evolution: Bridging the Semantic Gap. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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Hopfgartner, Frank. Unterstanding video retrieval: Approaches to bridge the Semantic Gap, evaluation strategies and state-of-the-art systems. Saarbrücken: VDM, Verlag Dr. Müller, 2007.

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Roger G. van de Velde. On meaning gaps and illusions. Bochum: N. Brockmeyer, 1991.

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C. H. J. van der Merwe. The Old Hebrew particle gam: A syntactic-semantic description of gam in Gn-2Kg. St. Ottilien [Germany]: EOS-Verlag, 1990.

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Xun gu xue gai lun. Fuzhou: Fujian ren min chu ban she, 1988.

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Xun gu wen xue cong gao. Nanjing Shi: Jiangsu gu ji chu ban she, 2001.

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Zhang, Shilu. Zhongguo wen zi xue gai yao. [Beijing: Beijing zhong xian tuo fang ke ji fa zhan you xian gong si, 2012.

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Han yu yu yi zhi xiang lun gao. Changchun: Dong Bei shi fan da xue chu ban she, 2005.

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"Shuo wen" yu xun gu yu fa lun gao. Hefei Shi: Anhui da xue chu ban she, 2005.

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Book chapters on the topic "Semantic gap"

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Sikos, Leslie F. "The Semantic Gap." In Description Logics in Multimedia Reasoning, 51–66. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54066-5_3.

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Hellmann, Sebastian. "The Semantic Gap of Formalized Meaning." In Lecture Notes in Computer Science, 462–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13489-0_44.

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Grosky, William I., and Rong Zhao. "Negotiating the Semantic Gap: From Feature Maps to Semantic Landscapes." In SOFSEM 2001: Theory and Practice of Informatics, 33–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45627-9_3.

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Kaffee, Lucie-Aimée, Hady Elsahar, Pavlos Vougiouklis, Christophe Gravier, Frédérique Laforest, Jonathon Hare, and Elena Simperl. "Mind the (Language) Gap: Generation of Multilingual Wikipedia Summaries from Wikidata for ArticlePlaceholders." In The Semantic Web, 319–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93417-4_21.

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Cabrio, Elena, Julien Cojan, and Fabien Gandon. "Mind the Cultural Gap: Bridging Language-Specific DBpedia Chapters for Question Answering." In Towards the Multilingual Semantic Web, 137–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43585-4_9.

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Mika, Peter, Edgar Meij, and Hugo Zaragoza. "Investigating the Semantic Gap through Query Log Analysis." In Lecture Notes in Computer Science, 441–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04930-9_28.

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Dorai, Chitra, and Svetha Venkatesh. "Bridging the Semantic Gap in Content Management Systems." In Media Computing, 1–9. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1119-9_1.

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Izquierdo, Ebroul, and Divna Djordjevic. "Using Relevance Feedback to Bridge the Semantic Gap." In Lecture Notes in Computer Science, 19–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11670834_2.

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Carvalho, Rommel N., Kathryn B. Laskey, and Paulo C. G. Costa. "PR-OWL 2.0 – Bridging the Gap to OWL Semantics." In Uncertainty Reasoning for the Semantic Web II, 1–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35975-0_1.

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Kwaśnicka, Halina, and Lakhmi C. Jain. "Semantic Gap in Image and Video Analysis: An Introduction." In Intelligent Systems Reference Library, 1–6. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73891-8_1.

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Conference papers on the topic "Semantic gap"

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Guha, Arjun, and Shriram Krishnamurthi. "Minding the (semantic) gap." In the FSE/SDP workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1882362.1882395.

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Millard, David E., Nicholas M. Gibbins, Danius T. Michaelides, and Mark J. Weal. "Mind the semantic gap." In the sixteenth ACM conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1083356.1083367.

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Pang, Yanwei, Yazhao Li, Jianbing Shen, and Ling Shao. "Towards Bridging Semantic Gap to Improve Semantic Segmentation." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00433.

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Jiang, Lu, Shoou-I. Yu, Deyu Meng, Teruko Mitamura, and Alexander G. Hauptmann. "Bridging the Ultimate Semantic Gap." In ICMR '15: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2671188.2749399.

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Li, Baoxin, James Errico, Hao Pan, and M. Ibrahim Sezan. "Bridging the semantic gap in sports." In Electronic Imaging 2003, edited by Minerva M. Yeung, Rainer W. Lienhart, and Chung-Sheng Li. SPIE, 2003. http://dx.doi.org/10.1117/12.476261.

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Zhuang, Qinsheng, Junkang Feng, and Hong Bao. "Measuring Semantic Gap: An Information Quantity Perspective." In 2007 5th IEEE International Conference on Industrial Informatics. IEEE, 2007. http://dx.doi.org/10.1109/indin.2007.4384853.

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Alzubaidi, Mohammad A. "Narrowing the semantic gap in natural images." In 2014 5th International Conference on Information and Communication Systems (ICICS). IEEE, 2014. http://dx.doi.org/10.1109/iacs.2014.6841972.

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Elahi, Mehdi, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Leonardo Cella, Stefano Cereda, and Paolo Cremonesi. "Exploring the Semantic Gap for Movie Recommendations." In RecSys '17: Eleventh ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3109859.3109908.

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Pardillo, Jesús, Jose-Norberto Mazón, and Juan Trujillo. "Bridging the semantic gap in OLAP models." In Proceeding of the ACM 11th international workshop. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1458432.1458448.

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Liu, Jiemin, Qi Tian, Yijuan Lu, Changhu Wang, Lei Zhang, Xiaokang Yang, and Shipeng Li. "A lexica family with small semantic gap." In 2009 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2009. http://dx.doi.org/10.1109/icme.2009.5202784.

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Reports on the topic "Semantic gap"

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Tripakis, Stavros, Rhishikesh Limaye, Kaushik Ravindran, and Guoqiang Wang. On Tokens and Signals: Bridging the Semantic Gap between Dataflow Models and Hardware Implementations. Fort Belvoir, VA: Defense Technical Information Center, June 2012. http://dx.doi.org/10.21236/ada563897.

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