Ontology-Based Analysis of Website Structure for Benchmarking in Retail Business

2021 ◽  
Vol 13 (1) ◽  
pp. 1-19
Author(s):  
Nikola Vlahovic ◽  
Andrija Brljak ◽  
Mirjana Pejic-Bach
Keyword(s):  
Language Processing ◽  
Web Sites ◽  
Information Acquisition ◽  
Semantic Analysis ◽  
Semantic Networks ◽  
Digital Transformation ◽  
Information Organization ◽  
Web Content ◽  
Retail Business ◽  
Information Retrieval Methods

With the growing trend of digital transformation, electronic business has become a crucial part of business operations for many companies. Some of the most critical steps in successful transformation pertain not only to data and information acquisition and digitalization but also to adequate publishing and organization of these information resources. Companies share information through their websites, so the structuring of the web content becomes critical during the digital transformation of business. In this paper, the authors present a conceptual model of the managerial tool that is based on semantic analysis of web sites in order to obtain information about the structure of web sites in a particular domain. The resulting ontological model contains information about best practices in web information organization and can be a valuable resource for management when deciding on the organization of their own web content. The system is based on grounded theory and uses current information retrieval methods, natural language processing, semantic networks, and ontologies.

Designing a Chat-Bot for College Information using Information Retrieval and Automatic Text Summarization Techniques

2020 ◽  
Vol 01 ◽  
Author(s):  
Radha Guha
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Text Summarization ◽  
The Internet ◽  
Specific Domain ◽  
User Query ◽  
College Information ◽  
Chat Bot

Background:: In the era of information overload it is very difficult for a human reader to make sense of the vast information available in the internet quickly. Even for a specific domain like college or university website it may be difficult for a user to browse through all the links to get the relevant answers quickly. Objective:: In this scenario, design of a chat-bot which can answer questions related to college information and compare between colleges will be very useful and novel. Methods:: In this paper a novel conversational interface chat-bot application with information retrieval and text summariza-tion skill is designed and implemented. Firstly this chat-bot has a simple dialog skill when it can understand the user query intent, it responds from the stored collection of answers. Secondly for unknown queries, this chat-bot can search the internet and then perform text summarization using advanced techniques of natural language processing (NLP) and text mining (TM). Results:: The advancement of NLP capability of information retrieval and text summarization using machine learning tech-niques of Latent Semantic Analysis(LSI), Latent Dirichlet Allocation (LDA), Word2Vec, Global Vector (GloVe) and Tex-tRank are reviewed and compared in this paper first before implementing them for the chat-bot design. This chat-bot im-proves user experience tremendously by getting answers to specific queries concisely which takes less time than to read the entire document. Students, parents and faculty can get the answers for variety of information like admission criteria, fees, course offerings, notice board, attendance, grades, placements, faculty profile, research papers and patents etc. more effi-ciently. Conclusion:: The purpose of this paper was to follow the advancement in NLP technologies and implement them in a novel application.

Semantic Web mining for Content-Based Online Shopping Recommender Systems

2019 ◽  
Vol 15 (4) ◽  
pp. 41-56 ◽  
Author(s):  
Ibukun Tolulope Afolabi ◽  
Opeyemi Samuel Makinde ◽  
Olufunke Oyejoke Oladipupo
Keyword(s):  
Semantic Web ◽  
Recommender Systems ◽  
Web Mining ◽  
Online Shopping ◽  
Semantic Analysis ◽  
Second Phase ◽  
Web Content ◽  
Textual Data ◽  
Content Mining ◽  
Bayes Algorithm

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

scholarly journals Developing and Analyzing a Spanish Corpus for Forensic Purposes

2019 ◽  
Vol 3 ◽  
Author(s):  
Ángela Almela ◽  
Gema Alcaraz-Mármol ◽  
Arancha García-Pinar ◽  
Clara Pallejá
Keyword(s):  
Data Collection ◽  
Language Processing ◽  
Ad Hoc ◽  
Semantic Analysis ◽  
Linguistic Features ◽  
Natural Language Processing Tool ◽  
Check Method ◽  
Spanish Universities ◽  
Gender Based ◽  
Main Instrument

In this paper, the methods for developing a database of Spanish writing that can be used for forensic linguistic research are presented, including our data collection procedures. Specifically, the main instrument used for data collection has been translated into Spanish and adapted from Chaski (2001). It consists of ten tasks, by means of which the subjects are asked to write formal and informal texts about different topics. To date, 93 undergraduates from Spanish universities have already participated in the study and prisoners convicted of gender-based abuse have participated. A twofold analysis has been performed, since the data collected have been approached from a semantic and a morphosyntactic perspective. Regarding the semantic analysis, psycholinguistic categories have been used, many of them taken from the LIWC dictionary (Pennebaker et al., 2001). In order to obtain a more comprehensive depiction of the linguistic data, some other ad-hoc categories have been created, based on the corpus itself, using a double-check method for their validation so as to ensure inter-rater reliability. Furthermore, as regards morphosyntactic analysis, the natural language processing tool ALIAS TATTLER is being developed for Spanish.  Results shows that is it possible to differentiate non-abusers from abusers with strong accuracy based on linguistic features.

scholarly journals Task-Dependent Modulation of Regions in the Left Inferior Frontal Cortex during Semantic Processing

2001 ◽  
Vol 13 (6) ◽  
pp. 829-843 ◽  
Author(s):  
A. L. Roskies ◽  
J. A. Fiez ◽  
D. A. Balota ◽  
M. E. Raichle ◽  
S. E. Petersen
Keyword(s):  
Frontal Cortex ◽  
Language Processing ◽  
Semantic Processing ◽  
Semantic Analysis ◽  
Lexical Processing ◽  
Temporal Cortex ◽  
Dependent Manner ◽  
Inferior Frontal Cortex ◽  
Semantic Judgment ◽  
The Right

To distinguish areas involved in the processing of word meaning (semantics) from other regions involved in lexical processing more generally, subjects were scanned with positron emission tomography (PET) while performing lexical tasks, three of which required varying degrees of semantic analysis and one that required phonological analysis. Three closely apposed regions in the left inferior frontal cortex and one in the right cerebellum were significantly active above baseline in the semantic tasks, but not in the nonsemantic task. The activity in two of the frontal regions was modulated by the difficulty of the semantic judgment. Other regions, including some in the left temporal cortex and the cerebellum, were active across all four language tasks. Thus, in addition to a number of regions known to be active during language processing, regions in the left inferior frontal cortex were specifically recruited during semantic processing in a task-dependent manner. A region in the right cerebellum may be functionally related to those in the left inferior frontal cortex. Discussion focuses on the implications of these results for current views regarding neural substrates of semantic processing.

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

A Transformer-Based Approach to Multilingual Fake News Detection in Low-Resource Languages

2022 ◽  
Vol 21 (1) ◽  
pp. 1-20
Author(s):  
Arkadipta De ◽  
Dibyanayan Bandyopadhyay ◽  
Baban Gain ◽  
Asif Ekbal
Keyword(s):  
Language Processing ◽  
English Language ◽  
Neural Model ◽  
Fake News ◽  
Web Content ◽  
Low Resource ◽  
Domain Specific ◽  
Cross Domain ◽  
Current State ◽  
Domain Transfer

Fake news classification is one of the most interesting problems that has attracted huge attention to the researchers of artificial intelligence, natural language processing, and machine learning (ML). Most of the current works on fake news detection are in the English language, and hence this has limited its widespread usability, especially outside the English literate population. Although there has been a growth in multilingual web content, fake news classification in low-resource languages is still a challenge due to the non-availability of an annotated corpus and tools. This article proposes an effective neural model based on the multilingual Bidirectional Encoder Representations from Transformer (BERT) for domain-agnostic multilingual fake news classification. Large varieties of experiments, including language-specific and domain-specific settings, are conducted. The proposed model achieves high accuracy in domain-specific and domain-agnostic experiments, and it also outperforms the current state-of-the-art models. We perform experiments on zero-shot settings to assess the effectiveness of language-agnostic feature transfer across different languages, showing encouraging results. Cross-domain transfer experiments are also performed to assess language-independent feature transfer of the model. We also offer a multilingual multidomain fake news detection dataset of five languages and seven different domains that could be useful for the research and development in resource-scarce scenarios.

Improving Collaborative Filtering Algorithms

2020 ◽  
pp. 638-657
Author(s):  
Firas Ben Kharrat ◽  
Aymen Elkhleifi ◽  
Rim Faiz
Keyword(s):  
Social Network ◽  
Web Sites ◽  
Opinion Mining ◽  
Semantic Analysis ◽  
Real Data ◽  
Data Sets ◽  
Suggested Approach ◽  
Opinion Score ◽  
Positive Results ◽  
Cold Start Problem

This paper puts forward a new recommendation algorithm based on semantic analysis as well as new measurements. Like Facebook, Social network is considered as one of the most well-prominent Web 2.0 applications and relevant services elaborating into functional ways for sharing opinions. Thereupon, social network web sites have since become valuable data sources for opinion mining. This paper proposes to introduce an external resource a sentiment from comments posted by users in order to anticipate recommendation and also to lessen the cold-start problem. The originality of the suggested approach means that posts are not merely characterized by an opinion score, but receive an opinion grade notion in the post instead. In general, the authors' approach has been implemented with Java and Lenskit framework. The study resulted in two real data sets, namely MovieLens and TripAdvisor, in which the authors have shown positive results. They compared their algorithm to SVD and Slope One algorithms. They have fulfilled an amelioration of 10% in precision and recall along with an improvement of 12% in RMSE and nDCG.

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