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1

Thirunarayan, Krishnaprasad, and Amit Sheth. "Semantics-Empowered Big Data Processing with Applications." AI Magazine 36, no. 1 (March 25, 2015): 39–54. http://dx.doi.org/10.1609/aimag.v36i1.2566.

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We discuss the nature of big data and address the role of semantics in analyzing and processing big data that arises in the context of physical-cyber-social systems. To handle volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle variety, we resort to semantic models and annotations of data so that intelligent processing can be done independent of heterogeneity of data formats and media. To handle velocity, we seek to use continuous semantics capability to dynamically create event or situa
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Jin, Min. "Semantics in XML Data Processing." Journal of the Korea Academia-Industrial cooperation Society 12, no. 3 (March 31, 2011): 1327–35. http://dx.doi.org/10.5762/kais.2011.12.3.1327.

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3

Liu, Jun Qiang, and Xiao Ling Guan. "Composite Event Processing for Data Streams and Domain Knowledge." Advanced Materials Research 219-220 (March 2011): 927–31. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.927.

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In recent years the processing of composite event queries over data streams has attracted a lot of research attention. Traditional database techniques were not designed for stream processing system. Furthermore, example continuous queries are often formulated in declarative query language without specifying the semantics. To overcome these deficiencies, this article presents the design, implementation, and evaluation of a system that executes data streams with semantic information. Then, a set of optimization techniques are proposed for handling query. So, our approach not only makes it possib
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4

Xhafa, Fatos, and Leonard Barolli. "Semantics, intelligent processing and services for big data." Future Generation Computer Systems 37 (July 2014): 201–2. http://dx.doi.org/10.1016/j.future.2014.02.004.

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Ramprasad, R., and C. Jayakumari. "A Novel Approach for Mining Big Data Using Multi-Model Fusion Mechanism (MMFM)." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (June 9, 2023): 484–93. http://dx.doi.org/10.17762/ijritcc.v11i5s.7110.

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Big data processing and analytics require sophisticated systems and cutting-edge methodologies to extract useful data from the available data. Extracted data visualization is challenging because of the processing models' dependence on semantics and classification. To categorize and improve information-based semantics that have accumulated over time, this paper introduces the Multi-model fusion mechanism for data mining (MMFM) approach. Information dependencies are organized based on the links between the data model based on attribute values. This method divides the attributes under considerati
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NISHIMURA, SUSUMU, and ATSUSHI OHORI. "Parallel functional programming on recursively defined data via data-parallel recursion." Journal of Functional Programming 9, no. 4 (July 1999): 427–62. http://dx.doi.org/10.1017/s0956796899003457.

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This article proposes a new language mechanism for data-parallel processing of dynamically allocated recursively defined data. Different from the conventional array-based data- parallelism, it allows parallel processing of general recursively defined data such as lists or trees in a functional way. This is achieved by representing a recursively defined datum as a system of equations, and defining new language constructs for parallel transformation of a system of equations. By integrating them with a higher-order functional language, we obtain a functional programming language suitable for desc
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Boyle, Mary. "Semantic Treatments for Word and Sentence Production Deficits in Aphasia." Seminars in Speech and Language 38, no. 01 (February 2017): 052–61. http://dx.doi.org/10.1055/s-0036-1597256.

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The cognitive domains of language and memory are intrinsically connected and work together during language processing. This relationship is especially apparent in the area of semantics. Several disciplines have contributed to a rich store of data about semantic organization and processing, and several semantic treatments for aphasic word and sentence production impairments have been based on these data. This article reviews the relationships between semantics and memory as they relate to word and sentence production, describes the aphasic language impairments that result from deficits in these
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Sejdiu, Besmir, Florije Ismaili, and Lule Ahmedi. "Integration of Semantics Into Sensor Data for the IoT." International Journal on Semantic Web and Information Systems 16, no. 4 (October 2020): 1–25. http://dx.doi.org/10.4018/ijswis.2020100101.

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The internet of things (IoT) as an evolving technology represents an active scientific research field in recognizing research challenges associated with its application in various domains, ranging from consumer convenience, smart energy, and resource saving to IoT enterprises. Sensors are crucial components of IoT that relay the collected data in the form of the data stream for further processing. Interoperability of various connected digital resources is a key challenge in IoT environments. The enrichment of raw sensor data with semantic annotations using concept definitions from ontologies e
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Nguyen Mau Quoc, Hoan, Martin Serrano, Han Mau Nguyen, John G. Breslin, and Danh Le-Phuoc. "EAGLE—A Scalable Query Processing Engine for Linked Sensor Data." Sensors 19, no. 20 (October 9, 2019): 4362. http://dx.doi.org/10.3390/s19204362.

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Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the c
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Kim, Hyeon Gyu. "Exploiting Window Query Semantics in Scalable Data Stream Processing." International Journal of Control and Automation 8, no. 11 (November 30, 2015): 13–20. http://dx.doi.org/10.14257/ijca.2015.8.11.02.

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11

Hamaz, Kamal, and Fouzia Benchikha. "A novel method for providing relational databases with rich semantics and natural language processing." Journal of Enterprise Information Management 30, no. 3 (April 10, 2017): 503–25. http://dx.doi.org/10.1108/jeim-01-2015-0005.

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Purpose With the development of systems and applications, the number of users interacting with databases has increased considerably. The relational database model is still considered as the most used model for data storage and manipulation. However, it does not offer any semantic support for the stored data which can facilitate data access for the users. Indeed, a large number of users are intimidated when retrieving data because they are non-technical or have little technical knowledge. To overcome this problem, researchers are continuously developing new techniques for Natural Language Inter
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Shastri, Shankarayya, Veeragangadhara Swamy Teligi Math, and Patil Nagaraja Siddalingappa. "Sensing complicated meanings from unstructured data: a novel hybrid approach." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (February 1, 2024): 711. http://dx.doi.org/10.11591/ijece.v14i1.pp711-720.

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The majority of data on computers nowadays is in the form of unstructured data and unstructured text. The inherent ambiguity of natural language makes it incredibly difficult but also highly profitable to find hidden information or comprehend complex semantics in unstructured text. In this paper, we present the combination of natural language processing (NLP) and convolution neural network (CNN) hybrid architecture called automated analysis of unstructured text using machine learning (AAUT-ML) for the detection of complex semantics from unstructured data that enables different users to make un
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Mukanova, Assel, Marek Milosz, Assem Dauletkaliyeva, Aizhan Nazyrova, Gaziza Yelibayeva, Dmitrii Kuzin, and Lazzat Kussepova. "LLM-Powered Natural Language Text Processing for Ontology Enrichment." Applied Sciences 14, no. 13 (July 4, 2024): 5860. http://dx.doi.org/10.3390/app14135860.

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This paper describes a method and technology for processing natural language texts and extracting data from the text that correspond to the semantics of an ontological model. The proposed method is distinguished by the use of a Large Language Model algorithm for text analysis. The extracted data are stored in an intermediate format, after which individuals and properties that reflect the specified semantics are programmatically created in the ontology. The proposed technology is implemented using the example of an ontological model that describes the geographical configuration and administrati
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Novikov, Boris, Alice Pigul, and Anna Yarygina. "A Performance Analysis of Semantic Caching for XML Query Processing." International Journal of Knowledge-Based Organizations 3, no. 4 (October 2013): 40–60. http://dx.doi.org/10.4018/ijkbo.2013100103.

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Caching is important for any system attempting to achieve high performance. The semantic caching is an approach trying to benefit from the certain knowledge of data semantics. The authors expect that this information might enable reuse of semantically close data rather than exactly equal to cached data in the traditional system. However, the major obstacle for extensive application of semantic caching for any data model or query language is the computational complexity of the query containment problem, which is, in general, undecidable. In this article the authors introduce and compare three a
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Dell'Aglio, Daniele, Emanuele Della Valle, Jean-Paul Calbimonte, and Oscar Corcho. "RSP-QL Semantics." International Journal on Semantic Web and Information Systems 10, no. 4 (October 2014): 17–44. http://dx.doi.org/10.4018/ijswis.2014100102.

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RDF and SPARQL are established standards for data interchange and querying on the Web. While they have been shown to be useful and applicable in many scenarios, they are not sufficiently adequate for dealing with streams of data and their intrinsic continuous nature. In the last years data and query languages have been proposed to extend both RDF and SPARQL for streams and continuous processing, under the name of RDF Stream Processing – RSP. These efforts resulted in several models and implementations that, at a first look, appear to propose alternative syntaxes but equivalent semantics. Howev
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Azabou, Maha, Ameen Banjar, and Jamel Omar Feki. "Enhancing the Diamond Document Warehouse Model." International Journal of Data Warehousing and Mining 16, no. 4 (October 2020): 1–25. http://dx.doi.org/10.4018/ijdwm.2020100101.

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The data warehouse community has paid particular attention to the document warehouse (DocW) paradigm during the last two decades. However, some important issues related to the semantics are still pending and therefore need a deep research investigation. Indeed, the semantic exploitation of the DocW is not yet mature despite it representing a main concern for decision-makers. This paper aims to enhancing the multidimensional model called Diamond Document Warehouse Model with semantics aspects; in particular, it suggests semantic OLAP (on-line analytical processing) operators for querying the Do
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17

Teuscher, Balthasar, and Martin Werner. "Random Data Distribution for Efficient Parallel Point Cloud Processing." AGILE: GIScience Series 5 (May 30, 2024): 1–10. http://dx.doi.org/10.5194/agile-giss-5-15-2024.

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Abstract. Current point cloud data management systems and formats are heavily specialized and targeted solely towards visualization purposes and fail to address the diverse needs of progressive point cloud workflows like for example semantic segmentation using machine learning. We therefore propose a distributed data infrastructure for dynamic point cloud data management that can support interactive real-time visualization at scale while simultaneously serving as a platform for analytical tasks. By introducing random data distribution, we show that simple query fragmentation and efficient and
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Zhang, Jing, and Xiaoyan Liang. "ADLBiLSTM: A Semantic Generation Algorithm for Multi-Grammar Network Access Control Policies." Applied Sciences 14, no. 11 (May 25, 2024): 4555. http://dx.doi.org/10.3390/app14114555.

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Abstract: Semantic generation of network access control policies can help network administrators accurately implement policies to achieve desired security objectives. Current semantic generation research mainly focuses on semantic generation of single grammar and lacks work on automatically generating semantics for different grammatical strategies. Generating semantics for different grammars is a tedious, inefficient, and non-scalable task. Inspired by sequence labeling in the field of natural language processing, this article models automatic semantic generation as a sequence labeling task. W
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Cimminella, Francesco, Sergio Della Sala, and Moreno I. Coco. "Extra-foveal Processing of Object Semantics Guides Early Overt Attention During Visual Search." Attention, Perception, & Psychophysics 82, no. 2 (December 2, 2019): 655–70. http://dx.doi.org/10.3758/s13414-019-01906-1.

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AbstractEye-tracking studies using arrays of objects have demonstrated that some high-level processing of object semantics can occur in extra-foveal vision, but its role on the allocation of early overt attention is still unclear. This eye-tracking visual search study contributes novel findings by examining the role of object-to-object semantic relatedness and visual saliency on search responses and eye-movement behaviour across arrays of increasing size (3, 5, 7). Our data show that a critical object was looked at earlier and for longer when it was semantically unrelated than related to the o
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Krachunov, Milko, Ognyan Kulev, Valeriya Simeonova, Maria Nisheva, and Dimitar Vassilev. "Manageable Workflows for Processing Parallel Sequencing Data." Serdica Journal of Computing 8, no. 1 (February 2, 2015): 1–14. http://dx.doi.org/10.55630/sjc.2014.8.1-14.

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Data analysis after parallel sequencing is a process that uses combinations of software tools that is often subject to experimentation and on-the-fly substitution, with the necessary file conversion. This article presents a developing system for creating and managing workflows aiding the tasks one encounters after parallel sequences, particularly in the area of metagenomics. The semantics, description language and software implementation aim to allow the creation of flexible, configurable workflows that are suitable for sharing and are easy to manipulate through software or by hand. The execut
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Hausser, Roland. "Database Semantics." Cadernos de Linguística 2, no. 1 (December 14, 2021): e382. http://dx.doi.org/10.25189/2675-4916.2021.v2.n1.id382.

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For long-term upscaling, the computational reconstruction of a complex natural mechanism must be input-output equivalent with the prototype, i.e. the reconstruction must take the same input and produce the same output in the same processing order as the original. Accordingly, the modeling of natural language communication in Database Semantics (DBS) uses a time-linear derivation order for the speaker’s output and the hearer’s input. The language-dependent surfaces serving as the vehicle of content transfer from speaker to hearer are raw data without meaning or any grammatical properties whatso
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Fisher, Ingrid E., and Robert A. Nehmer. "Using Language Processing to Evaluate the Equivalency of the FASB and IASB Standards." Journal of Emerging Technologies in Accounting 13, no. 2 (September 1, 2016): 129–44. http://dx.doi.org/10.2308/jeta-51621.

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ABSTRACT The passage of the Data Transparency and Accountability Act in the United States Congress will necessitate that government agencies provide more data in transparent formats. The issue of how to interpret such data remains an open question. The accounting profession has continued to struggle with common formats since the inception of balance sheets and income statements. The original FASB Conceptual Framework was developed to help construct consistent GAAP standards. XBRL was developed to provide a consistent representation of the data contained in financial statements and other financ
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Tutzauer, P., and N. Haala. "PROCESSING OF CRAWLED URBAN IMAGERY FOR BUILDING USE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 143–49. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-143-2017.

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Recent years have shown a shift from pure geometric 3D city models to data with semantics. This is induced by new applications (e.g. Virtual/Augmented Reality) and also a requirement for concepts like Smart Cities. However, essential urban semantic data like building use categories is often not available. We present a first step in bridging this gap by proposing a pipeline to use crawled urban imagery and link it with ground truth cadastral data as an input for automatic building use classification. We aim to extract this city-relevant semantic information automatically from Street View (SV) i
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Villa, Ferdinando, Stefano Balbi, Ioannis N. Athanasiadis, and Caterina Caracciolo. "Semantics for interoperability of distributed data and models: Foundations for better-connected information." F1000Research 6 (May 17, 2017): 686. http://dx.doi.org/10.12688/f1000research.11638.1.

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Correct and reliable linkage of independently produced information is a requirement to enable sophisticated applications and processing workflows. These can ultimately help address the challenges posed by complex systems (such as socio-ecological systems), whose many components can only be described through independently developed data and model products. We discuss the first outcomes of an investigation in the conceptual and methodological aspects of semantic annotation of data and models, aimed to enable a high standard of interoperability of information. The results, operationalized in the
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Beck, Edgar, Carsten Bockelmann, and Armin Dekorsy. "Semantic Information Recovery in Wireless Networks." Sensors 23, no. 14 (July 12, 2023): 6347. http://dx.doi.org/10.3390/s23146347.

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Motivated by the recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon’s classic design paradigm by aiming to transmit the meaning of a message, i.e., semantics, rather than its exact version and, thus, enables savings in information rate. In this work, we extend the fundamental approach from Basu et al. for modeling semantics to the complete communications Markov chain. Thus, we model semantics by means of hidden random variables and define the semantic communication task as
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Fan, Yunqian, Xiuying Wei, Ruihao Gong, Yuqing Ma, Xiangguo Zhang, Qi Zhang, and Xianglong Liu. "Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 11936–43. http://dx.doi.org/10.1609/aaai.v38i11.29080.

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Lane detection (LD) plays a crucial role in enhancing the L2+ capabilities of autonomous driving, capturing widespread attention. The Post-Processing Quantization (PTQ) could facilitate the practical application of LD models, enabling fast speeds and limited memories without labeled data. However, prior PTQ methods do not consider the complex LD outputs that contain physical semantics, such as offsets, locations, etc., and thus cannot be directly applied to LD models. In this paper, we pioneeringly investigate semantic sensitivity to post-processing for lane detection with a novel Lane Distort
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Terziyan, Vagan, and Anton Nikulin. "Semantics of Voids within Data: Ignorance-Aware Machine Learning." ISPRS International Journal of Geo-Information 10, no. 4 (April 8, 2021): 246. http://dx.doi.org/10.3390/ijgi10040246.

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Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we su
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Poux, F., R. Neuville, P. Hallot, and R. Billen. "MODEL FOR SEMANTICALLY RICH POINT CLOUD DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W5 (October 23, 2017): 107–15. http://dx.doi.org/10.5194/isprs-annals-iv-4-w5-107-2017.

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This paper proposes an interoperable model for managing high dimensional point clouds while integrating semantics. Point clouds from sensors are a direct source of information physically describing a 3D state of the recorded environment. As such, they are an exhaustive representation of the real world at every scale: 3D reality-based spatial data. Their generation is increasingly fast but processing routines and data models lack of knowledge to reason from information extraction rather than interpretation. The enhanced smart point cloud developed model allows to bring intelligence to point clo
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Chernova, Daria A. "Phonological and graphic representations of words in mental lexicon: Homophone processing while reading." Vestnik of Saint Petersburg University. Language and Literature 19, no. 1 (2022): 181–94. http://dx.doi.org/10.21638/spbu09.2022.110.

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This article presents an experimental study of the role of phonological representation of the word in lexical access during silent reading in Russian. The the phonological component in reading (i. e. whether semantics can be accessed via phonological decoding or directly from the orthographic image of the word) is actively discussed in modern psycholinguistics. Homophones can serve as a testing ground for these hypotheses: if graphemes are decoded into phonemes in silent reading in order to access semantics, then homophones will be processed like homonyms, but if semantics are accessed directl
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Mari, Daniele, Elena Camuffo, and Simone Milani. "CACTUS: Content-Aware Compression and Transmission Using Semantics for Automotive LiDAR Data." Sensors 23, no. 12 (June 15, 2023): 5611. http://dx.doi.org/10.3390/s23125611.

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Many recent cloud or edge computing strategies for automotive applications require transmitting huge amounts of Light Detection and Ranging (LiDAR) data from terminals to centralized processing units. As a matter of fact, the development of effective Point Cloud (PC) compression strategies that preserve semantic information, which is critical for scene understanding, proves to be crucial. Segmentation and compression have always been treated as two independent tasks; however, since not all the semantic classes are equally important for the end task, this information can be used to guide data t
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Simov, Kiril, and Petya Osenova. "Special Thematic Section on Semantic Models for Natural Language Processing (Preface)." Cybernetics and Information Technologies 18, no. 1 (March 1, 2018): 93–94. http://dx.doi.org/10.2478/cait-2018-0008.

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Abstract With the availability of large language data online, cross-linked lexical resources (such as BabelNet, Predicate Matrix and UBY) and semantically annotated corpora (SemCor, OntoNotes, etc.), more and more applications in Natural Language Processing (NLP) have started to exploit various semantic models. The semantic models have been created on the base of LSA, clustering, word embeddings, deep learning, neural networks, etc., and abstract logical forms, such as Minimal Recursion Semantics (MRS) or Abstract Meaning Representation (AMR), etc. Additionally, the Linguistic Linked Open Data
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Amenta, Simona, Davide Crepaldi, and Marco Marelli. "Consistency measures individuate dissociating semantic modulations in priming paradigms: A new look on semantics in the processing of (complex) words." Quarterly Journal of Experimental Psychology 73, no. 10 (June 15, 2020): 1546–63. http://dx.doi.org/10.1177/1747021820927663.

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In human language the mapping between form and meaning is arbitrary, as there is no direct connection between words and the objects that they represent. However, within a given language, it is possible to recognise systematic associations that support productivity and comprehension. In this work, we focus on the consistency between orthographic forms and meaning, and we investigate how the cognitive system may exploit it to process words. We take morphology as our case study, since it arguably represents one of the most notable examples of systematicity in form–meaning mapping. In a series of
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Sampaio, Thiago Oliveira da Motta, and Aniela Improta França. "Event-duration semantics in online sentence processing." Letras de Hoje 53, no. 1 (June 5, 2018): 59. http://dx.doi.org/10.15448/1984-7726.2018.1.28695.

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Several experiments in Psycholinguistics found evidences of Iterative Coercion, an effect related to the reanalysis of punctual events used in durative contexts triggering an iterative meaning. We argue that this effect is not related to aspectual features, and that event-duration semantics is accessed during Sentence Processing. We ran a self-paced reading experiment in Brazilian Portuguese whose sentences contain events with an average duration of a few minutes. These sentences were inserted in durative contexts that became the experiment’s conditions following a Latin Square design: control
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Nikitina, V. V., and A. M. Ivanova. "USING BIG DATA IN SEMANTIC RESEARCH: PERSPECTIVES AND APPROACHES." ВЕСТНИК ВОРОНЕЖСКОГО ГОСУДАРСТВЕННОГО ТЕХНИЧЕСКОГО УНИВЕРСИТЕТА, no. 3(42) (December 24, 2023): 25–34. http://dx.doi.org/10.36622/mlmdr.2023.95.56.003.

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Statement of the problem. Digitization and technology are rapidly taking over various fields of humanitarian sciences, including cognitive linguistics that is developing new algorithms and methods of language research, with linguists being encouraged to acquire new skills in data collection and processing as well as to expand their sources of language materials. In cognitive semantics and discourse studies, the biggest game-changer has been ever increasing (to the points of apparent limitlessness) volumes of various language data from digitalized texts available on the Internet that effectivel
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Lin, Yi, Hongwei Ding, and Yang Zhang. "Prosody Dominates Over Semantics in Emotion Word Processing: Evidence From Cross-Channel and Cross-Modal Stroop Effects." Journal of Speech, Language, and Hearing Research 63, no. 3 (March 23, 2020): 896–912. http://dx.doi.org/10.1044/2020_jslhr-19-00258.

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Purpose Emotional speech communication involves multisensory integration of linguistic (e.g., semantic content) and paralinguistic (e.g., prosody and facial expressions) messages. Previous studies on linguistic versus paralinguistic salience effects in emotional speech processing have produced inconsistent findings. In this study, we investigated the relative perceptual saliency of emotion cues in cross-channel auditory alone task (i.e., semantics–prosody Stroop task) and cross-modal audiovisual task (i.e., semantics–prosody–face Stroop task). Method Thirty normal Chinese adults participated i
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El Guemhioui, Karim, and Steven A. Demurjian. "Semantic Reconciliation of Electronic Health Records Using Semantic Web Technologies." International Journal of Information Technology and Web Engineering 12, no. 2 (April 2017): 26–48. http://dx.doi.org/10.4018/ijitwe.2017040102.

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In this paper, the authors present an approach to reconcile the semantics of distinct medical terms found in personal health records (PHRs - that store data controlled by patients) and electronic medical records (EMRs - that store data controlled by providers) that are utilized to describe the same concept in different systems. The authors present a solution for semantic reconciliation based on RDF and related semantic web technologies. As part of the solution, the authors utilize a centralized repository of ontologies to: uniformly interrogate the medical coding systems in which those terms a
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Mehta, Ansh. "Emotion Detection using Social Media Data." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1456–59. http://dx.doi.org/10.22214/ijraset.2021.39027.

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Abstract: Previous research on emotion recognition of Twitter users centered on the use of lexicons and basic classifiers on pack of words models, despite the recent accomplishments of deep learning in many disciplines of natural language processing. The study's main question is if deep learning can help them improve their performance. Because of the scant contextual information that most posts offer, emotion analysis is still difficult. The suggested method can capture more emotion sematic than existing models by projecting emoticons and words into emoticon space, which improves the performan
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Никитина, В. В., and А. М. Иванова. "USING BIG DATA IN SEMANTIC RESEARCH: PERSPERCIVES AND APPROACHES." НАУЧНЫЙ ЖУРНАЛ СОВРЕМЕННЫЕ ЛИНГВИСТИЧЕСКИЕ И МЕТОДИКО-ДИДАКТИЧЕСКИЕ ИССЛЕДОВАНИЯ, no. 3(59) (October 2, 2023): 38–49. http://dx.doi.org/10.36622/vstu.2023.32.12.003.

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Постановка задачи. Эра цифровизации влияет на алгоритмы лингвистических методов и методологий исследования, требуя от исследователей новых компетенций в области сбора и обработки языкового материала, а также расширения эмпирической базы исследования за пределы конкретных дискурсивных жанров. Предположительно, легко доступный и неограниченный объем больших данных (Big Data) лингвистического характера, размещенных на различных ресурсах сети Интернет, предоставляет исследователям как обширную эмпирическую базу, так и налагает ограничения, связанные с возможностями обработки и интерпретации получа
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Chersoni, E., E. Santus, L. Pannitto, A. Lenci, P. Blache, and C. R. Huang. "A structured distributional model of sentence meaning and processing." Natural Language Engineering 25, no. 4 (July 2019): 483–502. http://dx.doi.org/10.1017/s1351324919000214.

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AbstractMost compositional distributional semantic models represent sentence meaning with a single vector. In this paper, we propose a structured distributional model (SDM) that combines word embeddings with formal semantics and is based on the assumption that sentences represent events and situations. The semantic representation of a sentence is a formal structure derived from discourse representation theory and containing distributional vectors. This structure is dynamically and incrementally built by integrating knowledge about events and their typical participants, as they are activated by
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40

Yang, Fan. "A Computational Linguistic Approach to English Lexicography." Transactions on Computer Science and Intelligent Systems Research 2 (December 21, 2023): 39–44. http://dx.doi.org/10.62051/wepk6t89.

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Focusing on computational linguistic approaches to English linguistics, this research explores how computational methods can be applied to dissect, understand and utilise the English language. We first looked at text analysis and processing, delving into natural language processing techniques such as text categorisation, sentiment analysis and machine translation, and their application to social media and automated text processing. In the area of lexicography and semantics, we explored how techniques such as distributed word vectors, semantic role labelling and sentiment analysis can deepen ou
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Nicklas, Daniela, Thomas Schwarz, and Bernhard Mitschang. "A Schema-Based Approach to Enable Data Integration on the Fly." International Journal of Cooperative Information Systems 26, no. 01 (March 2017): 1650010. http://dx.doi.org/10.1142/s0218843016500106.

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On-the-fly data integration, i.e. at query time, happens mostly in tightly coupled, homogeneous environments where the partitioning of the data can be controlled or is known in advance. During the process of data fusion, the information is homogenized and data inconsistencies are hidden from the application. Beyond this, we propose in this paper the Nexus metadata model and a processing approach that support on-the-fly data integration in a loosely coupled federation of autonomous data providers, thereby advancing the status quo in terms of flexibility and expressive power. It is able to repre
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42

Hogrefe, Katharina, Georg Goldenberg, Ralf Glindemann, Madleen Klonowski, and Wolfram Ziegler. "Nonverbal Semantics Test (NVST)—A Novel Diagnostic Tool to Assess Semantic Processing Deficits: Application to Persons with Aphasia after Cerebrovascular Accident." Brain Sciences 11, no. 3 (March 11, 2021): 359. http://dx.doi.org/10.3390/brainsci11030359.

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Assessment of semantic processing capacities often relies on verbal tasks which are, however, sensitive to impairments at several language processing levels. Especially for persons with aphasia there is a strong need for a tool that measures semantic processing skills independent of verbal abilities. Furthermore, in order to assess a patient’s potential for using alternative means of communication in cases of severe aphasia, semantic processing should be assessed in different nonverbal conditions. The Nonverbal Semantics Test (NVST) is a tool that captures semantic processing capacities throug
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Haris, Erum, and Keng Hoon Gan. "Extraction and Visualization of Tourist Attraction Semantics from Travel Blogs." ISPRS International Journal of Geo-Information 10, no. 10 (October 18, 2021): 710. http://dx.doi.org/10.3390/ijgi10100710.

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Travel blogs are a significant source for modeling human travelling behavior and characterizing tourist destinations owing to the presence of rich geospatial and thematic content. However, the bulk of unstructured text requires extensive processing for an efficient transformation of data to knowledge. Existing works have studied tourist places, but results lack a coherent outline and visualization of the semantic knowledge associated with tourist attractions. Hence, this work proposes place semantics extraction based on a fusion of content analysis and natural language processing (NLP) techniq
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Huff, S. M., P. J. Haug, D. A. Evans, B. E. Bray, and R. A. Rocha. "Evaluation of a Semantic Data Model for Chest Radiology: Application of aNew Methodology." Methods of Information in Medicine 37, no. 04/05 (October 1998): 477–90. http://dx.doi.org/10.1055/s-0038-1634549.

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AbstractAn essential step toward the effective processing of the medical language is the development of representational models that formalize the language semantics. These models, also known as semantic data models, help to unlock the meaning of descriptive expressions, making them accessible to computer systems. The present study tries to determine the quality of a semantic data model created to encode chest radiology findings . The evaluation methodology relied on the ability of physicians to extract information from textual and encoded representations of chest X-ray reports, whilst answeri
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45

Jajaga, Edmond, and Lule Ahmedi. "C-SWRL: A Unique Semantic Web Framework for Reasoning Over Stream Data." International Journal of Semantic Computing 11, no. 03 (September 2017): 391–409. http://dx.doi.org/10.1142/s1793351x17400165.

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The synergy of Data Stream Management Systems and Semantic Web applications has steered towards a new paradigm known as Stream Reasoning. The Semantic Web standards for knowledge base modeling and querying, namely RDF, OWL and SPARQL, has extensively been used by the Stream Reasoning community. However, the Semantic Web rule languages, such as SWRL and RIF, have never been used in stream data applications. Instead, different non-Semantic Web rule systems have been approached. Since RIF is primarily intended for exchanging rules among systems, we focused on SWRL applications with stream data. T
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Zhang, Kaile, and Ichiro Koshijima. "Trend analysis of online travel review text mining over time." Journal of Modelling in Management 15, no. 2 (November 18, 2019): 491–508. http://dx.doi.org/10.1108/jm2-10-2018-0178.

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Purpose The reviews of online tourism have not been taken advantage of effectively because the text data of such reviews is enormous and its current, in-depth research is still in infancy. Therefore, it is expected that the text data could be processed by the method of text mining to better understand the implicit information. The purpose of this paper is to contribute to tourism practitioners and tourists to conveniently use the texts through appropriate visualization processing techniques. In particular, time-changing reviews can be used to reflect the changes in tourists’ feedback and conce
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47

Аkanova, Akerke, Nazira Ospanova, Saltanat Sharipova, Gulalem Мauina, and Zhanat Abdugulova. "Development of a thematic and neural network model for data learning." Eastern-European Journal of Enterprise Technologies 4, no. 2(118) (August 31, 2022): 40–50. http://dx.doi.org/10.15587/1729-4061.2022.263421.

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Research in the field of semantic text analysis begins with the study of the structure of natural language. The Kazakh language is unique in that it belongs to agglutinative languages and requires careful study. The object of this study is the text in the Kazakh language. Existing approaches to the study of the semantic analysis of text in the Kazakh language do not consider text analysis using the methods of thematic modeling and learning of neural networks. The purpose of this study is to determine the quality of a topic model based on the LDA (Latent Dirichlet Allocation) method with Gibbs
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48

Rao, Yunbo, Menghan Zhang, Zhanglin Cheng, Junmin Xue, Jiansu Pu, and Zairong Wang. "Semantic Point Cloud Segmentation Using Fast Deep Neural Network and DCRF." Sensors 21, no. 8 (April 13, 2021): 2731. http://dx.doi.org/10.3390/s21082731.

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Accurate segmentation of entity categories is the critical step for 3D scene understanding. This paper presents a fast deep neural network model with Dense Conditional Random Field (DCRF) as a post-processing method, which can perform accurate semantic segmentation for 3D point cloud scene. On this basis, a compact but flexible framework is introduced for performing segmentation to the semantics of point clouds concurrently, contribute to more precise segmentation. Moreover, based on semantics labels, a novel DCRF model is elaborated to refine the result of segmentation. Besides, without any s
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PAUL, MANOJ, and S. K. GHOSH. "A SERVICE-ORIENTED APPROACH FOR INTEGRATING HETEROGENEOUS SPATIAL DATA SOURCES REALIZATION OF A VIRTUAL GEO-DATA REPOSITORY." International Journal of Cooperative Information Systems 17, no. 01 (March 2008): 111–53. http://dx.doi.org/10.1142/s0218843008001774.

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Searching and accessing geospatial information in the open and distributed environments of geospatial information systems poses several challenges due to the heterogeneity in geospatial data. Geospatial data is highly heterogeneous — both at the syntactic and semantic level. The requirement for an integration architecture for seamless access of geospatial data has been raised over the past decades. The paper proposes a service-based model for geospatial integration where each geospatial data provider is interfaced on the web as services. The interface for these services has been described with
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50

González, Víctor, Laura Martín, Juan Ramón Santana, Pablo Sotres, Jorge Lanza, and Luis Sánchez. "Reshaping Smart Cities through NGSI-LD Enrichment." Sensors 24, no. 6 (March 14, 2024): 1858. http://dx.doi.org/10.3390/s24061858.

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The vast amount of information stemming from the deployment of the Internet of Things and open data portals is poised to provide significant benefits for both the private and public sectors, such as the development of value-added services or an increase in the efficiency of public services. This is further enhanced due to the potential of semantic information models such as NGSI-LD, which enable the enrichment and linkage of semantic data, strengthened by the contextual information present by definition. In this scenario, advanced data processing techniques need to be defined and developed for
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