scholarly journals Intelligent Identification of Trademark Case Precedents Using Semantic Ontology

2020 ◽  
Author(s):  
A.S. Li ◽  
A.J.C. Trappey ◽  
C.V. Trappey
Keyword(s):  
Clustering Algorithm ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Semantic Network ◽  
Federal Courts ◽  
Computer Assisted ◽  
Legal Action ◽  
Legal Cases ◽  
Semantic Ontology ◽  
Machine Readable

A registered trademark distinctively identifies a company, its products or services. A trademark (TM) is a type of intellectual property (IP) which is protected by the laws in the country where the trademark is officially registered. TM owners may take legal action when their IP rights are infringed upon. TM legal cases have grown in pace with the increasing number of TMs registered globally. In this paper, an intelligent recommender system automatically identifies similar TM case precedents for any given target case to support IP legal research. This study constructs the semantic network representing the TM legal scope and terminologies. A system is built to identify similar cases based on the machine-readable, frame-based knowledge representations of the judgments/documents. In this research, 4,835 US TM legal cases litigated in the US district and federal courts are collected as the experimental dataset. The computer-assisted system is constructed to extract critical features based on the ontology schema. The recommender will identify similar prior cases according to the values of their features embedded in these legal documents which include the case facts, issues under disputes, judgment holdings, and applicable rules and laws. Term frequency-inverse document frequency is used for text mining to discover the critical features of the litigated cases. Soft clustering algorithm, e.g., Latent Dirichlet Allocation, is applied to generate topics and the cases belonging to these topics. Thus, similar cases under each topic are identified for references. Through the analysis of the similarity between the cases based on the TM legal semantic analysis, the intelligent recommender provides precedents to support TM legal action and strategic planning.

scholarly journals An Efficient Topic Modeling Approach for Text Mining and Information Retrieval through K-means Clustering

2020 ◽  
Vol 39 (1) ◽  
pp. 213-222
Author(s):  
Junaid Rashid ◽  
Syed Muhammad Adnan Shah ◽  
Aun Irtaza
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Topic Modeling ◽  
Clustering Algorithm ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
State Of The Art ◽  
Text Documents ◽  
New Perspective ◽  
Better Than

Topic modeling is an effective text mining and information retrieval approach to organizing knowledge with various contents under a specific topic. Text documents in form of news articles are increasing very fast on the web. Analysis of these documents is very important in the fields of text mining and information retrieval. Meaningful information extraction from these documents is a challenging task. One approach for discovering the theme from text documents is topic modeling but this approach still needs a new perspective to improve its performance. In topic modeling, documents have topics and topics are the collection of words. In this paper, we propose a new k-means topic modeling (KTM) approach by using the k-means clustering algorithm. KTM discovers better semantic topics from a collection of documents. Experiments on two real-world Reuters 21578 and BBC News datasets show that KTM performance is better than state-of-the-art topic models like LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). The KTM is also applicable for classification and clustering tasks in text mining and achieves higher performance with a comparison of its competitors LDA and LSA.

scholarly journals Root cause analysis of COVID-19 cases by enhanced text mining process

2022 ◽  
Vol 12 (2) ◽  
pp. 1807
Author(s):  
Sujatha Arun Kokatnoor ◽  
Balachandran Krishnan
Keyword(s):  
Dirichlet Process ◽  
Clustering Algorithm ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Learning Approaches ◽  
The Public ◽  
Root Cause ◽  
Document Frequency ◽  
Coherence Score ◽  
Index Value

<p>The main focus of this research is to find the reasons behind the fresh cases of COVID-19 from the public’s perception for data specific to India. The analysis is done using machine learning approaches and validating the inferences with medical professionals. The data processing and analysis is accomplished in three steps. First, the dimensionality of the vector space model (VSM) is reduced with improvised feature engineering (FE) process by using a weighted term frequency-inverse document frequency (TF-IDF) and forward scan trigrams (FST) followed by removal of weak features using feature hashing technique. In the second step, an enhanced K-means clustering algorithm is used for grouping, based on the public posts from Twitter®. In the last step, latent dirichlet allocation (LDA) is applied for discovering the trigram topics relevant to the reasons behind the increase of fresh COVID-19 cases. The enhanced K-means clustering improved Dunn index value by 18.11% when compared with the traditional K-means method. By incorporating improvised two-step FE process, LDA model improved by 14% in terms of coherence score and by 19% and 15% when compared with latent semantic analysis (LSA) and hierarchical dirichlet process (HDP) respectively thereby resulting in 14 root causes for spike in the disease.</p>

Similarity Detection Using Latent Semantic Analysis Algorithm

2018 ◽  
Vol 6 (8) ◽  
pp. 102
Author(s):  
Priyanka R. Patil ◽  
Shital A. Patil
Keyword(s):  
Latent Semantic Analysis ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Mining Method ◽  
Research Papers ◽  
Information Measures ◽  
Automated Software ◽  
Day By Day ◽  
Ways Of Life ◽  
Dirichlet Allocation

Similarity View is an application for visually comparing and exploring multiple models of text and collection of document. Friendbook finds ways of life of clients from client driven sensor information, measures the closeness of ways of life amongst clients, and prescribes companions to clients if their ways of life have high likeness. Roused by demonstrate a clients day by day life as life records, from their ways of life are separated by utilizing the Latent Dirichlet Allocation Algorithm. Manual techniques can't be utilized for checking research papers, as the doled out commentator may have lacking learning in the exploration disciplines. For different subjective views, causing possible misinterpretations. An urgent need for an effective and feasible approach to check the submitted research papers with support of automated software. A method like text mining method come to solve the problem of automatically checking the research papers semantically. The proposed method to finding the proper similarity of text from the collection of documents by using Latent Dirichlet Allocation (LDA) algorithm and Latent Semantic Analysis (LSA) with synonym algorithm which is used to find synonyms of text index wise by using the English wordnet dictionary, another algorithm is LSA without synonym used to find the similarity of text based on index. LSA with synonym rate of accuracy is greater when the synonym are consider for matching.

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.

Exploring Global Fashion Sustainability Practices through Dictionary-Based Text Mining

2021 ◽  
pp. 0887302X2199826
Author(s):  
Muzhen Li ◽  
Li Zhao
Keyword(s):  
Latent Dirichlet Allocation ◽  
Corporate Sustainability ◽  
Computer Assisted ◽  
Competitive Advantages ◽  
Bottom Line ◽  
Product Categories ◽  
Sustainability Strategies ◽  
Line Theory ◽  
Sustainability Practices ◽  
Sustainability Theory

Nowadays, more fashion companies have started to adopt various sustainability practices and communicate these practices through their annual public CSR reports. In this study, we aim to provide a holistic perspective of fashion companies’ sustainable development and investigate the sustainability practices of global fashion companies. A total of 181 CSR reports from 29 fashion companies were collected. A Dictionary approach text classification method, combined with Latent Dirichlet Allocation (LDA), a computer-assisted topic modeling algorithm, was implemented to detect and summarize the themes and keywords of detailed practices disclosed in CSR reports. The findings identified 12 main sustainability practices themes based on the triple bottom line theory and the moral responsibility of corporate sustainability theory. In general, waste management and human rights are the most frequently mentioned themes. The findings also suggest that global fashion companies adopted different sustainability strategies based on their product categories and competitive advantages.

Clustering Mashups by Integrating Structural and Semantic Similarities Using Fuzzy AHP

2021 ◽  
Vol 18 (1) ◽  
pp. 34-57
Author(s):  
Weifeng Pan ◽  
Xinxin Xu ◽  
Hua Ming ◽  
Carl K. Chang
Keyword(s):  
Semantic Similarity ◽  
Clustering Algorithm ◽  
Latent Dirichlet Allocation ◽  
Fuzzy Ahp ◽  
Real Data ◽  
Structural Similarity ◽  
Analytic Hierarchy ◽  
Data Set ◽  
Novel Approach ◽  
Hierarchy Process

Mashup technology has become a promising way to develop and deliver applications on the web. Automatically organizing Mashups into functionally similar clusters helps improve the performance of Mashup discovery. Although there are many approaches aiming to cluster Mashups, they solely focus on utilizing semantic similarities to guide the Mashup clustering process and are unable to utilize both the structural and semantic information in Mashup profiles. In this paper, a novel approach to cluster Mashups into groups is proposed, which integrates structural similarity and semantic similarity using fuzzy AHP (fuzzy analytic hierarchy process). The structural similarity is computed from usage histories between Mashups and Web APIs using SimRank algorithm. The semantic similarity is computed from the descriptions and tags of Mashups using LDA (latent dirichlet allocation). A clustering algorithm based on the genetic algorithm is employed to cluster Mashups. Comprehensive experiments are performed on a real data set collected from ProgrammableWeb. The results show the effectiveness of the approach when compared with two kinds of conventional approaches.

Implementation of Programming Languages Syntax and Semantics

2011 ◽  
pp. 1863-1869
Author(s):  
Xiaoqing Wu ◽  
Marjan Mernik ◽  
Barrett R. Bryant ◽  
Jeff Gray
Keyword(s):  
Programming Languages ◽  
Semantic Analysis ◽  
Implementation Process ◽  
Future Trends ◽  
Human Communication ◽  
Natural Languages ◽  
Existing Problems ◽  
Front End ◽  
High Level ◽  
Machine Readable

Unlike natural languages, programming languages are strictly stylized entities created to facilitate human communication with computers. In order to make programming languages recognizable by computers, one of the key challenges is to describe and implement language syntax and semantics such that the program can be translated into machine-readable code. This process is normally considered as the front-end of a compiler, which is mainly related to the programming language, but not the target machine. This article will address the most important aspects in building a compiler front-end; that is, syntax and semantic analysis, including related theories, technologies and tools, as well as existing problems and future trends. As the main focus, formal syntax and semantic specifications will be discussed in detail. The article provides the reader with a high-level overview of the language implementation process, as well as some commonly used terms and development practices.

Open-Ended Questions

2020 ◽  
pp. 202-218
Author(s):  
Subhadra Dutta ◽  
Eric M. O’Rourke
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Written Language ◽  
Text Data ◽  
Employee Survey ◽  
Trade Offs ◽  
Word Relatedness ◽  
Survey Responses

Natural language processing (NLP) is the field of decoding human written language. This chapter responds to the growing interest in using machine learning–based NLP approaches for analyzing open-ended employee survey responses. These techniques address scalability and the ability to provide real-time insights to make qualitative data collection equally or more desirable in organizations. The chapter walks through the evolution of text analytics in industrial–organizational psychology and discusses relevant supervised and unsupervised machine learning NLP methods for survey text data, such as latent Dirichlet allocation, latent semantic analysis, sentiment analysis, word relatedness methods, and so on. The chapter also lays out preprocessing techniques and the trade-offs of growing NLP capabilities internally versus externally, points the readers to available resources, and ends with discussing implications and future directions of these approaches.

Topology and Topic-Aware Service Clustering

2018 ◽  
Vol 15 (3) ◽  
pp. 18-37 ◽  
Author(s):  
Weifeng Pan ◽  
Jilei Dong ◽  
Kun Liu ◽  
Jing Wang
Keyword(s):  
Service Discovery ◽  
Clustering Algorithm ◽  
Latent Dirichlet Allocation ◽  
Empirical Evaluation ◽  
Single Type ◽  
Bipartite Network ◽  
Real World Data ◽  
Service Usage ◽  
Data Set ◽  
Service Clustering

This article describes how the number of services and their types being so numerous makes accurately discovering desired services become a problem. Service clustering is an effective way to facilitate service discovery. However, the existing approaches are usually designed for a single type of service documents, neglecting to fully use the topic and topological information in service profiles and usage histories. To avoid these limitations, this article presents a novel service clustering approach. It adopts a bipartite network to describe the topological structure of service usage histories and uses a SimRank algorithm to measure the topological similarity of services; It applies Latent Dirichlet Allocation to extract topics from service profiles and further quantifies the topic similarity of services; It quantifies the similarity of services by integrating topological and topic similarities; It uses the Chameleon clustering algorithm to cluster the services. The empirical evaluation on real-world data set highlights the benefits provided by the combination of topological and topic similarities.

General Systems Theory in a Horizontal and Vertical Action Process for Cross-Disciplinary Knowledge Transfer

2013 ◽  
Author(s):  
Steffen Vagts ◽  
Josef Schlattmann
Keyword(s):  
Systems Theory ◽  
Semantic Network ◽  
General Systems Theory ◽  
Computer Assisted ◽  
Transfer Of Knowledge ◽  
World Language ◽  
Disciplinary Knowledge ◽  
General Systems ◽  
Knowledge Circulation ◽  
Action Process

Biomimetic product development builds a bridge between the scientific disciplines engineering and biology and represents a cross-disciplinary knowledge circulation, which can produce highly innovative advancements in technology. The methodological support for such projects was initiated by the VDI guideline 6220 by 2012 [1] and still requires further research. The method presented in this abstract tries to provide a significant contribution to the successful transfer of knowledge across disciplines, to convey innovative solutions from biology to technology. The central idea of the method called Heli-Act (an acronym of Helix and Action) described here is based on the system-theoretical analysis of the action and the action carrier, the action circle and the action line under the relevant aspects of the socio-technical integration of methods and tools that will support the action carrier in the biomimetic development process. As a cross-common “language” general systems theory is used, which also includes the mathematical modeling system for both action as well as for object systems, which allows a computer-assisted method implementation. For the association of specialized terminology of the disciplines involved in the cross-disciplinary communication a semantic network is used to derive a translation tool in the Ontology World Language (OWL). Practical application experiences from a current project are presented, which describes the tribological optimization of a technical joint by awareness from the analysis of insects joints.

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