scholarly journals Extracting Semantic Information from Graphic Schemes

2021 ◽  
Vol 20 (4) ◽  
pp. 940-970
Author(s):  
Valeriy Mironov ◽  
Artem Gusarenko ◽  
Gayz Tuguzbaev
Keyword(s):  
Graphical Models ◽  
Semantic Information ◽  
Extraction Process ◽  
Levels Of Abstraction ◽  
Vector Graphics ◽  
External Data ◽  
Situational Model ◽  
Graphic Editor ◽  
Logical Mapping ◽  
Semantic Properties

The problem of extracting semantic information from an electronic document specified in the vector graphics format and containing a graphic model (diagram) built using a graphic editor is considered. The problem is to program retrieving certain structural properties and parametric circuit and entering them into a database for later use. Based on the analysis of the capabilities of graphic editors, a conclusion has made about the relevance of this task for universal editors that are not tied to specific graphic notations and use open graphic document formats, which allows program processing. The proposed approach considers graphic documents at three levels of abstraction: conceptual (semantic properties of a schema), logical (presentation of semantic properties at the internal level of the document) and physical (internal organization of a graphic document). The solution to the problem is based on the construction of a conceptual-logical mapping, i.e., mapping a conceptual model of a circuit to a logical model of a graphic document, according to its physical model. Within the framework of the approach, an algorithm for constructing the indicated mapping is developed, presented in the form of an object-oriented pseudocode. The study of internal markup in open graphic formats made it possible to build models for identifying circuit elements and their connections to each other, which is necessary for a specific application of the algorithm. Expressions for addressing schema elements and accessing their properties are obtained. The proposed approach is implemented on the base of a situation-oriented paradigm, within which the extraction process is driven by a hierarchical situational model. The processed data is specified in the situational model in the form of virtual documents displayed on heterogeneous external data sources. For the problem being solved, we consider the mapping to two variants of vector graphics formats: to a "flat" markup file and to a set of such files in an electronic archive. The practical use of the results is illustrated by the example of extracting semantic information from graphical models developed at various stages of database design.

scholarly journals Vertical intent prediction approach based on Doc2vec and convolutional neural networks for improving vertical selection in aggregated search

2020 ◽  
Vol 10 (4) ◽  
pp. 3869
Author(s):  
Sanae Achsas ◽  
El Habib Nfaoui
Keyword(s):  
Web Search ◽  
Semantic Information ◽  
Machine Learning Techniques ◽  
Information Need ◽  
Levels Of Abstraction ◽  
Unseen Data ◽  
Intent Prediction ◽  
Experimental Findings ◽  
Aggregated Search ◽  
Prediction Approach

Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data.

scholarly journals Ekstraksi Informasi Semantik dan Spatiotemporal pada Artikel Online Terkait Bencana di Indonesia

2020 ◽  
Vol 10 (1) ◽  
pp. 114-121
Author(s):  
Ashr Hafiizh Tantri ◽  
Nur Aini Rakhmawati
Keyword(s):  
Natural Disasters ◽  
Information Structure ◽  
Semantic Information ◽  
Engineering Application ◽  
Extraction Process ◽  
Inference Process ◽  
Literature Study ◽  
Semantic Approach ◽  
Types Of Information ◽  
The Impact

Indonesia is one country that has a high risk of natural disasters. Ranked in 36 out of 172 countries in disaster index and having 2,372 disaster incidents in 2017, actions need to be taken to minimize the impact of natural disasters. One of it is to do a hazard map modeling. In making hazard maps, several approaches can be used, one of which is the semantic approach to extract disaster information. Therefore, this study aims to develop a system that can be used to extract spatiotemporal and semantic information related to natural disasters in Indonesia. This study uses the NLP method in conducting the information extraction process and  carried out using the GATE (General Architecture for Text Engineering) application. In processing Indonesian language articles, it is necessary to develop the plugin because the Indonesian information structure is different from the default information structure in GATE application. The plugin development process is done by using ontology as the basis for determining semantic information. Literature study was carried out related to government regulations that further explained the need for semantic and spatiotemporal information about disaster events. system performance developed produces a precision value of 38% and a recall value of 32%. this is because the system experiences some difficulties in carrying out the information inference process. The reason for low precision rate is because the rules used in the inference process to pair the three types of information still cannot accommodate the variation of information positions in different sentences.

Model-Driven Design and ASM-Based Validation of Embedded Systems

2010 ◽  
pp. 24-54 ◽  
Author(s):  
Angelo Gargantini ◽  
Elvinia Riccobene ◽  
Patrizia Scandurra
Keyword(s):  
Embedded System ◽  
Graphical Models ◽  
Code Generation ◽  
Formal Method ◽  
System Level ◽  
Levels Of Abstraction ◽  
Model Driven ◽  
The Embedded System ◽  
On Chip ◽  
High Level

In the embedded system and System-on-Chip (SoC) design area, the increasing technological complexity coupled with requests for more performance and shorter time to market have caused a high interest for new methods, languages and tools capable of operating at higher levels of abstraction than the conventional system level. This chapter presents a model-driven and tool-assisted development process of SoCs, which is based on high-level UML design of system components, guarantees SystemC code generation from graphical models, and allows validation of system behaviors on formal models automatically derived from UML models. An environment for system design and analysis is also presented, which is based on a UML profile for SystemC and the Abstract State Machine formal method.

A multimedia semantic framework for image understanding and retrieval

2017 ◽  
Vol 01 (01) ◽  
pp. 1650001
Author(s):  
Antonio Penta
Keyword(s):  
Semantic Information ◽  
Image Understanding ◽  
Heterogeneous Data ◽  
Multimedia Data ◽  
Image Domain ◽  
Semantic Framework ◽  
Semantic Relationships ◽  
Heterogeneous Data Sources ◽  
Semantic Properties ◽  
Provided Examples

On the grounds, ontologies have been shown to be a powerful resource for the interpretation and translation of the terminological and semantic relationships within domains of interest but it is still unclear how they can be applied in the context of multimedia data. In this paper, we describe a framework which can capture and manage semantic information related to the multimedia data by modeling in the ontology their features. In particular, the proposed ontology-based framework is organized in the following way: at the lower levels, spatial objects, colors, shapes are represented, and semantic relationships can be established among them; at the higher levels, objects with semantic properties are put into relationship among themselves as well as with the corresponding low-level objects. On this basis, we have designed an ontological system particularly suitable for image retrieval. We have also taken into account the inherent uncertainty related to the representation and detection of multimedia properties in this complex domain. Along this work, we have provided examples from the image domain; moreover, since ontologies provide a semantic means for the semantic comparison of objects and relationships across different formats, the system is easily extensible to other, heterogeneous data sources.

scholarly journals SPEECH RECOGNITION OF KV-PATTERNED INDONESIAN SYLLABLE USING MFCC, WAVELET AND HMM

Kursor ◽  
2016 ◽  
Vol 8 (2) ◽  
pp. 67 ◽  
Author(s):  
Syahroni Hidayat
Keyword(s):  
Speech Recognition ◽  
Euclidean Distance ◽  
Recognition Rate ◽  
Extraction Process ◽  
Training Data ◽  
External Data ◽  
Increase In Accuracy ◽  
High Possibility ◽  
Agglutinative Language ◽  
Data Length

The Indonesian language is an agglutinative language which has complex suffixes and affixes attached on its root. For this reason there is a high possibility to recognize Indonesian speech based on its syllables. The syllable-based Indonesian speech recognition could reduce the database and recognize new Indonesian vocabularies which evolve as the result of language development. MFCC and WPT daubechies 3rd (DB3) and 7th (DB7) order methods are used in feature extraction process and HMM with Euclidean distance probability is applied for classification. The results shows that the best recognition rateis 75% and 70.8% for MFCC and WPT method respectively, which come from the testing using training data test. Meanwhile, for testing using external data test WPT method excel the MFCC method, where the best recognition rate is 53.1% for WPT and 47% for MFCC. For MFCC the accuracy increased asthe data length and the frame length increased. In WPT, the increase in accuracy is influenced by the length of data, type of the wavelet and decomposition level. It is also found that as the variation of state increased the recognition for both methods decreased.

scholarly journals Is Passive Priming Really Impervious to Verb Semantics? A High-Powered Replication of Messenger Et al. (2012)

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
I Made Sena Darmasetiyawan ◽  
Kate Messenger ◽  
Ben Ambridge
Keyword(s):  
Semantic Information ◽  
Sentence Type ◽  
Mixed Effects ◽  
Test Case ◽  
Syntactic Category ◽  
Prime Type ◽  
Continuous Analysis ◽  
Levels Of Abstraction ◽  
Verb Semantics ◽  
The One

The aim of the present study was to conduct a particularly stringent pre-registered in-vestigation of the claim that there exists a level of linguistic representation that “includes syntactic category information but not semantic information” (Branigan & Pickering, 2017: 8). As a test case, we focussed on the English passive; a construction for which previous findings have been somewhat contradictory. On the one hand, several studies using different methodologies have found an advantage for theme-experiencer passives (e.g., The girl was shocked by the tiger; and also agent-patient passives; e.g., The girl was hit by the tiger) over experiencer-theme passives (e.g., The girl was ignored by the tiger). On the other hand, Messenger et al. (2012) found no evidence that theme-experiencer and experiencer-theme passives vary in their propensity to prime production of agent-patient passives. We therefore conducted an online replication of Messen-ger et al (2012) with a pre-registered appropriately powered sample (N=240). Although a large and significant priming effect (i.e., an effect of prime sentence type) was ob-served, a Bayesian analysis yielded only weak/anecdotal evidence (BF=2.11) for the crucial interaction of verb type by prime type; a finding that was robust to different coding and exclusion decisions, operationalizations of verb semantics (dichoto-mous/continuous), analysis frameworks (Bayesian/frequentist) and – as per a mixed-effects-multiverse analyses – random effects structures. Nevertheless, these findings do no not provide evidence for the absence of semantic effects (as has been argued for the findings of Messenger et al, 2012). We conclude that these and related findings are best explained by a model that includes both lexical, exemplar-level representations and rep-resentations at multiple higher levels of abstraction.

scholarly journals ENRICHMENT AND POPULATION OF A GEOSPATIAL ONTOLOGY FOR SEMANTIC INFORMATION EXTRACTION

2018 ◽  
Vol XLII-4 ◽  
pp. 309-314 ◽  
Author(s):  
M. Kokla ◽  
V. Papadias ◽  
E. Tomai
Keyword(s):  
Information Extraction ◽  
Semantic Information ◽  
Extraction Process ◽  
User Generated Content ◽  
Web Content ◽  
Top Down ◽  
Bottom Up ◽  
Great Opportunity ◽  
Domain Specific ◽  
Geospatial Ontology

<p><strong>Abstract.</strong> The massive amount of user-generated content available today presents a new challenge for the geospatial domain and a great opportunity to delve into linguistic, semantic, and cognitive aspects of geographic information. Ontology-based information extraction is a new, prominent field in which a domain ontology guides the extraction process and the identification of pre-defined concepts, properties, and instances from natural language texts. The paper describes an approach for enriching and populating a geospatial ontology using both a top-down and a bottom-up approach in order to enable semantic information extraction. The top-down approach is applied in order to incorporate knowledge from existing ontologies. The bottom-up approach is applied in order to enrich and populate the geospatial ontology with semantic information (concepts, relations, and instances) extracted from domain-specific web content.</p>

Make Information in Science Meaningful Again

2021 ◽  
Vol 12 (3) ◽  
pp. 263-287
Author(s):  
Javier Anta ◽  
Keyword(s):  
Statistical Mechanics ◽  
Semantic Information ◽  
Quantitative Measure ◽  
Theoretical Framework ◽  
The Other ◽  
Semantic Concept ◽  
Scientific Practices ◽  
The Everyday ◽  
Knowledge Dynamics ◽  
Semantic Properties

Although the everyday notion of information has clear semantic properties, the all-pervasive technical concept of Shannon information was defended being a non-semantic concept. In this paper I will show how this measure of information was implicitly ‘semantized’ in the early 1950s by many authors, such as Rothstein's or Brillouin's, in order to explain the knowledge dynamics underlying certain scientific practices such as measurement. On the other hand, I will argue that the main attempts in the literature to develop a quantitative measure of semantic information to clarify science and scientific measurements, such as Carnap-Bar-Hillel, or Dretske, will not successfully achieve this philosophical aim for several reasons. Finally, I will defend the use of a qualitative notion of semantic information within the information-theoretical framework MacKay to assess the informational dynamics underlying scientific practices, particularly measurements in statistical mechanics.

scholarly journals How do the semantic properties of visual explanations guide causal inference?

2021 ◽  
Author(s):  
Holly Huey ◽  
Caren Walker ◽  
Judith Fan
Keyword(s):  
Causal Inference ◽  
Visual Communication ◽  
Semantic Information ◽  
Structural Features ◽  
Abstract Knowledge ◽  
Semantic Properties

What visualization strategies do people use to communicate abstract knowledge to others? We developed a drawing paradigm to elicit visual explanations about novel machines and obtained detailed annotations of the semantic information conveyed in each drawing. We found that these visual explanations contained: (1) greater emphasis on causally relevant parts of the machine, (2) less emphasis on structural features that were visually salient but causally irrelevant, and (3) more symbols, relative to baseline drawings intended only to communicate the machines’ appearance. However, this overall pattern of emphasis did not necessarily improve naive viewers’ ability to infer how to operate the machines, nor their ability to identify them, suggesting a potential mismatch between what people believe a visual explanation contains and what may be most useful. Taken together, our findings advance our understanding of how communicative goals constrain visual communication of abstract knowledge across behavioral contexts.

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