Search results for: semantic processing

Authors: Seyedeh Khadijeh. Mirbazel, Masoumeh Arjmandi

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Semantics is a linguistic approach and a scientific stream, and like all scientific streams, it is dynamic. The study of meaning is carried out in the broad semantic collections of words that form the discourse. In other words, meaning is not something that can be found in a word; rather, the formation of meaning is a process that takes place in a discourse as a whole. One of the contemporary semantic theories is Izutsu's Semantic Field Theory. According to this theory, the discovery of meaning depends on the function of words and takes place within the context of language. The purpose of this research is to identify the meaning of the word "Nas" in the discourse of verse 24 of Surah Al-Baqarah, which introduces "Nas" as the firewood of hell, but the translators have translated it as "people". The present research has investigated the semantic structure of the word "Nas" using the aforementioned theory through the descriptive-analytical method. In the process of investigation, by matching the semantic fields of the Quranic word "Nas", this research came to the conclusion that "Nas" implies those persons who have forgotten God and His covenant in believing in His Oneness. For this reason, God called them "Nas (the forgetful)" - the imperfect participle of the noun /næsiwoɔn/ in single trinity of Arabic language, which means “to forget”. Therefore, the intended meaning of "Nas" in the verses that have the word "Nas" is not equivalent to "People" which is a general noun.

Keywords: Nas, people, semantics, semantic field theory.

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Authors: Ma. Gracia Corazon Cayanan, Kai Yuen Cheong, Li Sha

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Training a language model for a minority language has been a challenging task. The lack of available corpora to train and fine-tune state-of-the-art language models is still a challenge in the area of Natural Language Processing (NLP). Moreover, the need for high computational resources and bulk data limit the attainment of this task. In this paper, we presented the following contributions: (1) we introduce and used a translation pair set of Tagalog and English (TL-EN) in pre-training a language model to a minority language resource; (2) we fine-tuned and evaluated top-ranking and pre-trained semantic textual similarity binary task (STSB) models, to both TL-EN and STS dataset pairs. (3) then, we reduced the size of the model to offset the need for high computational resources. Based on our results, the models that were pre-trained to translation pairs and STS pairs can perform well for STSB task. Also, having it reduced to a smaller dimension has no negative effect on the performance but rather has a notable increase on the similarity scores. Moreover, models that were pre-trained to a similar dataset have a tremendous effect on the model’s performance scores.

Keywords: semantic matching, semantic textual similarity binary task, low resource minority language, fine-tuning, dimension reduction, transformer models

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Authors: Shoohira Aftab, Hammad Afzal

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Information integration is a well known procedure to provide consolidated view on sets of heterogeneous information sources. It not only provides better statistical analysis of information but also facilitates users to query without any knowledge on the underlying heterogeneous information sources The problem of providing a consolidated view of information can be handled using Semantic data (information stored in such a way that is understandable by machines and integrate-able without manual human intervention). However, integrating information using semantic web technology without any access management enforced, will results in increase of privacy and confidentiality concerns. In this research we have designed and developed a framework that would allow information from heterogeneous formats to be consolidated, thus resolving the issue of interoperability. We have also devised an access control system for defining explicit privacy constraints. We designed and applied our framework on both semantic and non-semantic data from heterogeneous resources. Our approach is validated using scenario based testing.

Keywords: information integration, semantic data, interoperability, security, access control system

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Authors: Yogendra Sisodia

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Researchers are becoming more interested in extracting useful information from legal documents thanks to the development of large-scale language models in natural language processing (NLP), and deep learning has accelerated the creation of powerful text mining models. Legal fields like contracts benefit greatly from semantic text search since it makes it quick and easy to find related clauses. After collecting sentence embeddings, it is relatively simple to locate sentences with a comparable meaning throughout the entire legal corpus. The author of this research investigated two pre-trained language models for this task: MiniLM and Roberta, and further fine-tuned them on Legal Contracts. The author used Multiple Negative Ranking Loss for the creation of sentence transformers. The fine-tuned language models and sentence transformers showed promising results.

Keywords: legal contracts, multiple negative ranking loss, natural language inference, sentence transformers, semantic textual similarity

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Authors: Ming-Jen Huang, Chun-Fang Huang, Chiching Wei

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With the recent advance of the deep neural network, we observe new applications of NLP (natural language processing) and CV (computer vision) powered by deep neural networks for processing business documents. However, creating a real-world document processing system needs to integrate several NLP and CV tasks, rather than treating them separately. There is a need to have a unified approach for processing documents containing textual and graphical elements with rich formats, diverse layout arrangement, and distinct semantics. In this paper, a framework that fulfills this unified approach is presented. The framework includes a representation model definition for holding the information generated by various tasks and specifications defining the coordination between these tasks. The framework is a blueprint for building a system that can process documents with rich formats, styles, and multiple types of elements. The flexible and lightweight design of the framework can help build a system for diverse business scenarios, such as contract monitoring and reviewing.

Keywords: document processing, framework, formal definition, machine learning

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Authors: Liu Jia’nan

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The default accounts hold the view that there exists a kind of scalar implicature which can be processed without context and own a psychological privilege over other scalar implicatures which depend on context. In contrast, the Relevance Theorist regards context as a must because all the scalar implicatures have to meet the need of relevance in discourse. However, in Katsos, the experimental results showed: Although quantitatively the adults rejected under-informative utterance with lexical scales (context-independent) and the ad hoc scales (context-dependent) at almost the same rate, adults still regarded the violation of utterance with lexical scales much more severe than with ad hoc scales. Neither default account nor Relevance Theory can fully explain this result. Thus, there are two questionable points to this result: (1) Is it possible that the strange discrepancy is due to other factors instead of the generation of scalar implicature? (2) Are the ad hoc scales truly formed under the possible influence from mental context? Do the participants generate scalar implicatures with ad hoc scales instead of just comparing semantic difference among target objects in the under- informative utterance? In my Experiment 1, the question (1) will be answered by repetition of Experiment 1 by Katsos. Test materials will be showed by PowerPoint in the form of pictures, and each procedure will be done under the guidance of a tester in a quiet room. Our Experiment 2 is intended to answer question (2). The test material of picture will be transformed into the literal words in DMDX and the target sentence will be showed word-by-word to participants in the soundproof room in our lab. Reading time of target parts, i.e. words containing scalar implicatures, will be recorded. We presume that in the group with lexical scale, standardized pragmatically mental context would help generate scalar implicature once the scalar word occurs, which will make the participants hope the upcoming words to be informative. Thus if the new input after scalar word is under-informative, more time will be cost for the extra semantic processing. However, in the group with ad hoc scale, scalar implicature may hardly be generated without the support from fixed mental context of scale. Thus, whether the new input is informative or not does not matter at all, and the reading time of target parts will be the same in informative and under-informative utterances. People’s mind may be a dynamic system, in which lots of factors would co-occur. If Katsos’ experimental result is reliable, will it shed light on the interplay of default accounts and context factors in scalar implicature processing? We might be able to assume, based on our experiments, that one single dominant processing paradigm may not be plausible. Furthermore, in the processing of scalar implicature, the semantic interpretation and the pragmatic interpretation may be made in a dynamic interplay in the mind. As to the lexical scale, the pragmatic reading may prevail over the semantic reading because of its greater exposure in daily language use, which may also lead the possible default or standardized paradigm override the role of context. However, those objects in ad hoc scale are not usually treated as scalar membership in mental context, and thus lexical-semantic association of the objects may prevent their pragmatic reading from generating scalar implicature. Only when the sufficient contextual factors are highlighted, can the pragmatic reading get privilege and generate scalar implicature.

Keywords: scalar implicature, ad hoc scale, dynamic interplay, default account, Mandarin Chinese processing

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Authors: Ricardo Avila, Gabriel Lopes, Vania Vidal, Jose Macedo

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This proposal aims for semantic enrichment between glossaries using the Simple Knowledge Organization System (SKOS) vocabulary to discover synonyms, hyponyms and hyperonyms semiautomatically, in Brazilian Portuguese, generating new semantic relationships based on WordNet. To evaluate the quality of this proposed model, experiments were performed by the use of two sets containing new relations, being one generated automatically and the other manually mapped by the domain expert. The applied evaluation metrics were precision, recall, f-score, and confidence interval. The results obtained demonstrate that the applied method in the field of Oil Production and Extraction (E&P) is effective, which suggests that it can be used to improve the quality of terminological mappings. The procedure, although adding complexity in its elaboration, can be reproduced in others domains.

Keywords: ontology matching, mapping enrichment, semantic web, linked data, SKOS

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Authors: Yan Huang

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Event-related potentials (ERPs) technique helps researchers to make continuous measures on the whole process of language comprehension, with an excellent temporal resolution at the level of milliseconds. The research on sentence processing has developed from the behavioral level to the neuropsychological level, which brings about a variety of sentence processing theories and models. However, the applicability of these models to L2 learners is still under debate. Therefore, the present study aims to investigate the neural mechanisms underlying English subjunctive mood processing by Chinese ESL learners. To this end, English subject clauses with subjunctive moods are used as the stimuli, all of which follow the same syntactic structure, “It is + adjective + that … + (should) do + …” Besides, in order to examine the role that language proficiency plays on L2 processing, this research deals with two groups of Chinese ESL learners (18 males and 22 females, mean age=21.68), namely, high proficiency group (Group H) and low proficiency group (Group L). Finally, the behavioral and neurophysiological data analysis reveals the following findings: 1) Syntax and semantics interact with each other on the SECOND phase (300-500ms) of sentence processing, which is partially in line with the Three-phase Sentence Model; 2) Language proficiency does affect L2 processing. Specifically, for Group H, it is the syntactic processing that plays the dominant role in sentence processing while for Group L, semantic processing also affects the syntactic parsing during the THIRD phase of sentence processing (500-700ms). Besides, Group H, compared to Group L, demonstrates a richer native-like ERPs pattern, which further demonstrates the role of language proficiency in L2 processing. Based on the research findings, this paper also provides some enlightenment for the L2 pedagogy as well as the L2 proficiency assessment.

Keywords: Chinese ESL learners, English subjunctive mood, ERPs, L2 processing

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Authors: O. Poleshchuk, E. Komarov

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In this paper, semantic spaces with the properties of completeness and orthogonality (complete orthogonal semantic spaces) were chosen as models of expert evaluations. As the theoretical and practical studies have shown all the properties of complete orthogonal semantic spaces correspond to the thinking activity of experts that is why these semantic spaces were chosen for modeling. Two methods of construction such spaces were proposed. Models of comparative and fuzzy cluster analysis of expert evaluations were developed. The practical application of the developed methods has demonstrated their viability and validity.

Keywords: expert evaluation, comparative analysis, fuzzy cluster analysis, theoretical and practical studies

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Authors: Mohammed Hassan Alshaikhi

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Vocabulary is essential factor of learning and mastering any languages, and it helps learners to communicate with others and to be understood. The aim of this study was to examine whether semantic mapping technique was helpful in terms of improving student's English vocabulary learning comparing to the traditional technique. The students’ age was between 11 and 13 years old. There were 60 students in total who participated in this study. 30 students were in the treatment group (target vocabulary items were taught with semantic mapping). The other 30 students were in the control group (the target vocabulary items were taught by a traditional technique). A t-test was used with the results of pre-test and post-test in order to examine the outcomes of using semantic mapping when teaching vocabulary. The results showed that the vocabulary mastery in the treatment group was increased more than the control group.

Keywords: English language, learning vocabulary, Saudi teachers, semantic mapping, teaching vocabulary strategies

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Authors: Laila Khaled Almawaldi, Hiew Khai Hang, Sugumaran A. l. Nallusamy

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Legacy systems are crucial for organizations, but their intricacy and lack of documentation pose challenges for maintenance and enhancement. Redocumentation of legacy systems is vital for automatically or semi-automatically creating documentation for software lacking sufficient records. It aims to enhance system understandability, maintainability, and knowledge transfer. However, existing redocumentation methods need improvement in data processing performance and document generation efficiency. This stems from the necessity to efficiently handle the extensive and complex code of legacy systems. This paper proposes a method for semi-automatic legacy system re-documentation using semantic parallel processing and ontology. Leveraging parallel processing and ontology addresses current challenges by distributing the workload and creating documentation with logically interconnected data. The paper outlines challenges in legacy system redocumentation and suggests a method of redocumentation using parallel processing and ontology for improved efficiency and effectiveness.

Keywords: legacy systems, redocumentation, big data analysis, parallel processing

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Authors: J. Zhang, K. Agyapong-Kodua

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Digital factory based on visual design and simulation has emerged as a mainstream to reduce digital development life cycle. Some basic industrial systems are being integrated via semantic modelling, and products (P) matching process (P)-resource (R) requirements are designed to fulfill current customer demands. Nevertheless, product design is still limited to fixed product models and known knowledge of product engineers. Therefore, this paper presents a rapid reconfiguration method based on semantic technologies with PPR ontologies to reuse known and unknown knowledge. In order to avoid the influence of big data, our system uses a cloud manufactory and distributed database to improve the efficiency of querying meeting PPR requirements.

Keywords: semantic technologies, factory system, digital factory, cloud manufactory

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Authors: Gridaphat Sriharee

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Building semantic-relatedness Thai word ontology can be implemented by considering word forms and word meaning. This research proposed the methodology for building the ontology, which can be used for semantic analysis. There are four categories of words: similar form and the same meaning, similar form and similar meaning, different form and opposite/same meaning, and different form and similar meaning, which will be used as initial words for building the proposed ontology. Extension of the ontology can be augmented by considering the messages that give the meaning of the word from the dictionaries. Exploiting WordNet to construct the proposed ontology was investigated and discussed. The proposed ontology was evaluated for its quality. With the proposed methodology, it is promising that the constructed ontology is a well-defined ontology.

Keywords: Thai, NLP, semantics, ontology

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Authors: Mohammad J. Rezai, Fereshteh Davarpanah

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Producing a text in a language which is not one’s mother tongue can be a demanding task for language learners. Examining lexical errors committed by EFL learners is a challenging area of investigation which can shed light on the process of second language acquisition. Despite the considerable number of investigations into grammatical errors, few studies have tackled formal and semantic errors of lexis committed by EFL learners. The current study aimed at examining Persian learners’ formal and semantic errors of lexis in English. To this end, 60 students at three different proficiency levels were asked to write on 10 different topics in 10 separate sessions. Finally, 600 essays written by Persian EFL learners were collected, acting as the corpus of the study. An error taxonomy comprising formal and semantic errors was selected to analyze the corpus. The formal category covered misselection and misformation errors, while the semantic errors were classified into lexical, collocational and lexicogrammatical categories. Each category was further classified into subcategories depending on the identified errors. The results showed that there were 2583 errors in the corpus of 9600 words, among which, 2030 formal errors and 553 semantic errors were identified. The most frequent errors in the corpus included formal error commitment (78.6%), which were more prevalent at the advanced level (42.4%). The semantic errors (21.4%) were more frequent at the low intermediate level (40.5%). Among formal errors of lexis, the highest number of errors was devoted to misformation errors (98%), while misselection errors constituted 2% of the errors. Additionally, no significant differences were observed among the three semantic error subcategories, namely collocational, lexical choice and lexicogrammatical. The results of the study can shed light on the challenges faced by EFL learners in the second language acquisition process.

Keywords: collocational errors, lexical errors, Persian EFL learners, semantic errors

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Authors: Madiha Khan, Sidrah Ramzan, Seemab Khan, Shahzad Hassan, Kamran Saeed

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Measuring semantic similarity between texts is calculating semantic relatedness between texts using various techniques. Our web application (Measuring Relatedness of Concepts-MRC) allows user to input two text corpuses and get semantic similarity percentage between both using WordNet. Our application goes through five stages for the computation of semantic relatedness. Those stages are: Preprocessing (extracts keywords from content), Feature Extraction (classification of words into Parts-of-Speech), Synonyms Extraction (retrieves synonyms against each keyword), Measuring Similarity (using keywords and synonyms, similarity is measured) and Visualization (graphical representation of similarity measure). Hence the user can measure similarity on basis of features as well. The end result is a percentage score and the word(s) which form the basis of similarity between both texts with use of different tools on same platform. In future work we look forward for a Web as a live corpus application that provides a simpler and user friendly tool to compare documents and extract useful information.

Keywords: Graphviz representation, semantic relatedness, similarity measurement, WordNet similarity

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Authors: Maria Fernanda Ordoñez Martinez, Alvaro Mauricio Montenegro

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This work presents a statistical methodology for measuring and founding constructs in Latent Semantic Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations present on Item Response Theory. More precisely, we propose initially reducing dimensionality with specific use of Principal Component Analysis for the linguistic data and then, producing axes of groups made from a clustering analysis of the semantic data. This approach allows the user to give meaning to previous clusters and found the real latent structure presented by data. The methodology is applied in a set of real semantic data presenting impressive results for the coherence, speed and precision.

Keywords: semantic analysis, factorial analysis, dimension reduction, penalized logistic regression

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Authors: Souâad Boudebza, Omar Nouali, Faiçal Azouaou

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Social networks have recently gained a growing interest on the web. Traditional formalisms for representing social networks are static and suffer from the lack of semantics. In this paper, we will show how semantic web technologies can be used to model social data. The SemTemp ontology aligns and extends existing ontologies such as FOAF, SIOC, SKOS and OWL-Time to provide a temporal and semantically rich description of social data. We also present a modeling scenario to illustrate how our ontology can be used to model social networks.

Keywords: ontology, semantic web, social network, temporal modeling

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Authors: Linyu Wang, Liguo Qiao, Jianhong Xiang, Hao Xu

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In semantic communication, the existing joint Source Channel coding (JSCC) wireless communication system without pilot has unstable transmission performance and can not effectively capture the global information and location information of images. In this paper, a pilot-free image transmission system of joint source channel based on multi-level semantic information (Multi-level JSCC) is proposed. The transmitter of the system is composed of two networks. The feature extraction network is used to extract the high-level semantic features of the image, compress the information transmitted by the image, and improve the bandwidth utilization. Feature retention network is used to preserve low-level semantic features and image details to improve communication quality. The receiver also is composed of two networks. The received high-level semantic features are fused with the low-level semantic features after feature enhancement network in the same dimension, and then the image dimension is restored through feature recovery network, and the image location information is effectively used for image reconstruction. This paper verifies that the proposed multi-level JSCC algorithm can effectively transmit and recover image information in both AWGN channel and Rayleigh fading channel, and the peak signal-to-noise ratio (PSNR) is improved by 1~2dB compared with other algorithms under the same simulation conditions.

Keywords: deep learning, JSCC, pilot-free picture transmission, multilevel semantic information, robustness

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Authors: Somayeh Sadat Hashemi Kamangar, Fatemeh Bakouie, Shahriar Gharibzadeh

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In this study, we target to exploit Maximum Spanning Trees (MST) of children's semantic networks to investigate their language development. To do so, we examine the graph-theoretic properties of word-embedding networks. The networks are made of words children learn prior to the age of 30 months as the nodes and the links which are built from the cosine vector similarity of words normatively acquired by children prior to two and a half years of age. These networks are weighted graphs and the strength of each link is determined by the numerical similarities of the two words (nodes) on the sides of the link. To avoid changing the weighted networks to the binaries by setting a threshold, constructing MSTs might present a solution. MST is a unique sub-graph that connects all the nodes in such a way that the sum of all the link weights is maximized without forming cycles. MSTs as the backbone of the semantic networks are suitable to examine developmental changes in semantic network topology in children. From these trees, several parameters were calculated to characterize the developmental change in network organization. We showed that MSTs provides an elegant method sensitive to capture subtle developmental changes in semantic network organization.

Keywords: maximum spanning trees, word-embedding, semantic networks, language development

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Authors: Kais A. Kadhim

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This paper is a semantic analysis of the English modals in Obama’s speech. The main objective of this study is to analyze selected modal auxiliaries identified in selected speeches of Obama’s campaign based on Coates’ (1983) semantic clusters. A total of fifteen speeches of Obama’s campaign were selected as the primary data and the modal auxiliaries selected for analysis include will, would, can, could, should, must, ought, shall, may and might. All the modal auxiliaries taken from the speeches of Barack Obama were analyzed based on the framework of Coates’ semantic clusters. Such analytical framework was carried out to examine how modal auxiliaries are used in the context of persuading people in Obama’s campaign speeches. The findings reveal that modals of intention, prediction, futurity and modals of possibility, ability, permission are mostly used in Obama’s campaign speeches.

Keywords: modals, meaning, persuasion, speech

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Authors: A. Elsehemy, M. Abdeen , T. Nazmy

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Since the appearance of the Semantic web, many semantic search techniques and models were proposed to exploit the information in ontology to enhance the traditional keyword-based search. Many advances were made in languages such as English, German, French and Spanish. However, other languages such as Arabic are not fully supported yet. In this paper we present a framework for ontology based information retrieval for Arabic language. Our system consists of four main modules, namely query parser, indexer, search and a ranking module. Our approach includes building a semantic index by linking ontology concepts to documents, including an annotation weight for each link, to be used in ranking the results. We also augmented the framework with an automatic document categorizer, which enhances the overall document ranking. We have built three Arabic domain ontologies: Sports, Economic and Politics as example for the Arabic language. We built a knowledge base that consists of 79 classes and more than 1456 instances. The system is evaluated using the precision and recall metrics. We have done many retrieval operations on a sample of 40,316 documents with a size 320 MB of pure text. The results show that the semantic search enhanced with text classification gives better performance results than the system without classification.

Keywords: Arabic text classification, ontology based retrieval, Arabic semantic web, information retrieval, Arabic ontology

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

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Authors: Montaceur Zaghdoud, Mohamed Moussaoui, Jalel Akaichi

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Frequent subgraph mining refers usually to graph matching and it is widely used in when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structuralsemantic graph matching to discover a set of frequent subgraphs. It uses simultaneously an approximate structural similarity function based on graph edit distance function and a possibilistic vertices similarity function based on affinity function. Both structural and semantic filters contribute together to prune extracted frequent set. Indeed, new hybrid structural-semantic frequent subgraph mining approach searches will be suitable to be applied to several application such as community detection in social networks.

Keywords: approximate graph matching, hybrid frequent subgraph mining, graph mining, possibility theory

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Authors: Subodh Chandra Shakya, Rajendra Sapkota, Aakash Tamang, Shushant Pudasaini, Sujan Adhikari, Sajjan Adhikari

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Lots of efforts have been made in order to measure the semantic similarity between the text corpora in the documents. Techniques have been evolved to measure the similarity of two documents. One such state-of-art technique in the field of Natural Language Processing (NLP) is word to vector models, which converts the words into their word-embedding and measures the similarity between the vectors. We found this to be quite useful for the task of resume ranking. So, this research paper is the implementation of the word2vec model along with other Natural Language Processing techniques in order to rank the resumes for the particular job description so as to automate the process of hiring. The research paper proposes the system and the findings that were made during the process of building the system.

Keywords: chunking, document similarity, information extraction, natural language processing, word2vec, word embedding

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Authors: Fatima Al-Aswadi, Chan Yong

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With the large volume of unstructured data that increases day by day on the web, the motivation of representing the knowledge in this data in the machine processable form is increased. Ontology is one of the major cornerstones of representing the information in a more meaningful way on the semantic Web. The goal of Ontology learning from text is to elicit and represent domain knowledge in the machine readable form. This paper aims to give a follow-up review on the ontology learning systems from text and some of their defects. Furthermore, it discusses how far the ontology learning process will enhance in the future.

Keywords: concept discovery, deep learning, ontology learning, semantic relation, semantic web

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Authors: Joanna Wielochowska, Aneta Wielochowska

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Whilst the relationship between scientific areas such as cognitive psychology, neurobiology and philosophy of mind has been emphasized in recent decades of scientific research, concepts and discoveries made in both fields overlap and complement each other in their quest for answers to similar questions. The object of the following case study is to describe, analyze and illustrate the nature and characteristics of a certain cognitive experience which appears to display features of synaesthesia, or rather high-level synaesthesia (ideasthesia). The following research has been conducted on the subject of two authors, monozygotic twins (both polysynaesthetes) experiencing involuntary associations of identical nature. Authors made attempts to identify which cognitive and conceptual dependencies may guide this experience. Operating on self-introduced nomenclature, the described phenomenon- multi-dimensional processing of textual and visual information- aims to define a relationship that involuntarily and immediately couples the content introduced by means of text or image a sensation of appearing in a certain place in the mind’s eye. More precisely: (I) defining a concept introduced by means of textual content during activity of reading or writing, or (II) defining a concept introduced by means of visual content during activity of looking at image(s) with simultaneous sensation of being allocated to a given place in the mind’s eye. A place can be then defined as a cognitive representation of a certain concept. During the activity of processing information, a person has an immediate and involuntary feel of appearing in a certain place themselves, just like a character of a story, ‘observing’ a venue or a scenery from one or more perspectives and angles. That forms a unique and unified experience, constituting a background mental landscape of text or image being looked at. We came to a conclusion that semantic allocations to a given place could be divided and classified into the categories and subcategories and are naturally linked with an individual’s semantic knowledge-base. A place can be defined as a representation one’s unique idea of a given concept that has been established in their semantic knowledge base. A multi-level structure of selectivity of places in the mind’s eye, as a reaction to a given information (one stimuli), draws comparisons to structures and patterns found in botany. Double-flowered varieties of flowers and a whorl system (arrangement) which is characteristic to components of some flower species were given as an illustrative example. A composition of petals that fan out from one single point and wrap around a stem inspired an idea that, just like in nature, in philosophy of mind there are patterns driven by the logic specific to a given phenomenon. The study intertwines terms perceived through the philosophical lens, such as definition of meaning, subjectivity of meaning, mental atmosphere of places, and others. Analysis of this rare experience aims to contribute to constantly developing theoretical framework of the philosophy of mind and influence the way human semantic knowledge base and processing given content in terms of distinguishing between information and meaning is researched.

Keywords: information and meaning, information processing, mental atmosphere of places, patterns in nature, philosophy of mind, selectivity, semantic knowledge base, senses, synaesthesia

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Authors: Firas A. Al Laban, Mohamed Chabi, Sammani Danwawu Abdullahi

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There are a real needs to integrate types of Open Educational Resources (OER) with an intelligent system to extract information and knowledge in the semantic searching level. Those needs raised because most of current learning standard adopted web based learning and the e-learning systems does not always serve all educational goals. Semantic Web systems provide educators, students, and researchers with intelligent queries based on a semantic knowledge management learning system. An ontology-based learning system is an advanced system, where ontology plays the core of the semantic web in a smart learning environment. The objective of this paper is to discuss the potentials of ontologies and mapping different kinds of ontologies; heterogeneous or homogenous to manage and control different types of Open Educational Resources. The important contribution of this research is to approach a methodology uses logical rules and conceptual relations to map between ontologies of different educational resources. We expect from this methodology to establish for an intelligent educational system supporting student tutoring, self and lifelong learning system.

Keywords: knowledge management systems, ontologies, semantic web, open educational resources

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Authors: Tamás Matuszka, Attila Kiss

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Due the proliferation of smartphones in everyday use, several different outdoor navigation systems have become available. Since these smartphones are able to connect to the Internet, the users can obtain location-based information during the navigation as well. The users could interactively get to know the specifics of a particular area (for instance, ancient cultural area, Statue Park, cemetery) with the help of thus obtained information. In this paper, we present an Augmented Reality system which uses Semantic Web technologies and is based on the interaction between the user and the smartphone. The system allows navigating through a specific area and provides information and details about the sight an interactive manner.

Keywords: augmented reality, semantic web, human computer interaction, mobile application

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Authors: Mohsen Maraoui

Abstract:

In the aim of natural language processing applications improvement, such as information retrieval, machine translation, lexical disambiguation, we focus on statistical approach to semantic indexing for multilingual text documents based on conceptual network formalism. We propose to use this formalism as an indexing language to represent the descriptive concepts and their weighting. These concepts represent the content of the document. Our contribution is based on two steps. In the first step, we propose the extraction of index terms using the multilingual lexical resource Euro WordNet (EWN). In the second step, we pass from the representation of index terms to the representation of index concepts through conceptual network formalism. This network is generated using the EWN resource and pass by a classification step based on association rules model (in attempt to discover the non-taxonomic relations or contextual relations between the concepts of a document). These relations are latent relations buried in the text and carried by the semantic context of the co-occurrence of concepts in the document. Our proposed indexing approach can be applied to text documents in various languages because it is based on a linguistic method adapted to the language through a multilingual thesaurus. Next, we apply the same statistical process regardless of the language in order to extract the significant concepts and their associated weights. We prove that the proposed indexing approach provides encouraging results.

Keywords: concept extraction, conceptual network formalism, fuzzy association rules, multilingual thesaurus, semantic indexing

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Authors: Marina Fomina

Abstract:

The paper focuses on the category of semantic subject within the framework of a functional approach to linguistics. The semantic subject is related to similar notions such as the grammatical subject and the bearer of predicative feature. It is the multifaceted nature of the category of subject that 1) triggers a number of issues that, syntax-wise, remain to be dealt with (cf. semantic vs. syntactic functions / sentence parts vs. parts of speech issues, etc.); 2) results in a variety of approaches to the category of subject, such as formal grammatical, semantic/syntactic (functional), communicative approaches, etc. Many linguists consider the prototypical approach to the category of subject to be the most instrumental as it reveals the integrity of denotative and linguistic components of the conceptual category. This approach relates to subject as a source of non-passive predicative feature, an element of subject-predicate-object situation that can take on a variety of semantic roles, cf.: 1) an agent (He carefully surveyed the valley stretching before him), 2) an experiencer (I feel very bitter about this), 3) a recipient (I received this book as a gift), 4) a causee (The plane broke into three pieces), 5) a patient (This stove cleans easily), etc. It is believed that the variety of roles stems from the radial (prototypical) structure of the category with some members more central than others. Translation-wise, the most “treacherous” subject types are the peripheral ones. The paper 1) features a peripheral status of spatial impersonal sentence models such as U menia v ukhe zvenit (lit. I-Gen. in ear buzzes) within the category of semantic subject, 2) makes a structural and semantic analysis of the models, 3) focuses on their Russian-English translation patterns, 4) reveals non-prototypical features of subjects in the English equivalents.

Keywords: bearer of predicative feature, grammatical subject, impersonal sentence model, semantic subject

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