Research Directions, Challenges and Issues in Opinion Mining

Rapid growth of Internet and availability of user reviews on the web for any product has provided a need for an effective system to analyze the web reviews. Such reviews are useful to some extent, promising both the customers and product manufacturers. For any popular product, the number of reviews can be in hundreds or even thousands. This creates difficulty for a customer to analyze them and make important decisions on whether to purchase the product or to not. Mining such product reviews or opinions is termed as opinion mining which is broadly classified into two main categories namely facts and opinions. Though there are several approaches for opinion mining, there remains a challenge to decide on the recommendation provided by the system. In this paper, we analyze the basics of opinion mining, challenges, pros & cons of past opinion mining systems and provide some directions for the future research work, focusing on the challenges and issues

Contextual Understanding in Neural Dialog Systems: the Integration of External Knowledge Graphs for Generating Coherent and Knowledge-rich Conversations

The integration of external knowledge graphs has emerged as a powerful approach to enrich conversational AI systems with coherent and knowledge-rich conversations. This paper provides an overview of the integration process and highlights its benefits. Knowledge graphs serve as structured representations of information, capturing the relationships between entities through nodes and edges. They offer an organized and efficient means of representing factual knowledge. External knowledge graphs, such as DBpedia, Wikidata, Freebase, and Google's Knowledge Graph, are pre-existing repositories that encompass a wide range of information across various domains. These knowledge graphs are compiled by aggregating data from diverse sources, including online encyclopedias, databases, and structured repositories. To integrate an external knowledge graph into a conversational AI system, a connection needs to be established between the system and the knowledge graph. This can be achieved through APIs or by importing a copy of the knowledge graph into the AI system's internal storage. Once integrated, the conversational AI system can query the knowledge graph to retrieve relevant information when a user poses a question or makes a statement. When analyzing user inputs, the conversational AI system identifies entities or concepts that require additional knowledge. It then formulates queries to retrieve relevant information from the integrated knowledge graph. These queries may involve searching for specific entities, retrieving related entities, or accessing properties and attributes associated with the entities. The obtained information is used to generate coherent and knowledge-rich responses. By integrating external knowledge graphs, conversational AI systems can augment their internal knowledge base and provide more accurate and up-to-date responses. The retrieved information allows the system to extract relevant facts, provide detailed explanations, or offer additional context. This integration empowers AI systems to deliver comprehensive and insightful responses that enhance user experience. As external knowledge graphs are regularly updated with new information and improvements, conversational AI systems should ensure their integrated knowledge graphs remain current. This can be achieved through periodic updates, either by synchronizing the system's internal representation with the external knowledge graph or by querying the external knowledge graph in real-time

Redesigning OP2 Compiler to Use HPX Runtime Asynchronous Techniques

Maximizing parallelism level in applications can be achieved by minimizing overheads due to load imbalances and waiting time due to memory latencies. Compiler optimization is one of the most effective solutions to tackle this problem. The compiler is able to detect the data dependencies in an application and is able to analyze the specific sections of code for parallelization potential. However, all of these techniques provided with a compiler are usually applied at compile time, so they rely on static analysis, which is insufficient for achieving maximum parallelism and producing desired application scalability. One solution to address this challenge is the use of runtime methods. This strategy can be implemented by delaying certain amount of code analysis to be done at runtime. In this research, we improve the parallel application performance generated by the OP2 compiler by leveraging HPX, a C++ runtime system, to provide runtime optimizations. These optimizations include asynchronous tasking, loop interleaving, dynamic chunk sizing, and data prefetching. The results of the research were evaluated using an Airfoil application which showed a 40-50% improvement in parallel performance.Comment: 18th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC 2017

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Cross-cultural Knowledge Management

The success of international companies in providing high quality products and outstanding services is subject, on the one hand, to the increasing dynamic of the economic environment and on the other hand to the adoption of worldwide quality standards and procedures. As market place is becoming more and more global, products and services offered worldwide by international companies must face the multi-cultural environment challenges. These challenges manifest themselves not only at customer relationship level but also deep inside companies, at employee level. Important support in facing all these challenges has been provided at cognitive level by management system models and at technological level by information cutting edge technologies Business Intelligence & Knowledge Management Business Intelligence is already delivering its promised outcomes at internal business environment and, with the explosive deployment of public data bases, expand its analytical power at national, regional and international level. Quantitative measures of economic environment, wherever available, may be captured and integrated in companies’ routine analysis. As for qualitative data, some effort is still to be done in order to integrate measures of social, political, legal, natural and technological environment in companies’ strategic analysis. An increased difficulty is found in treating cultural differences, common knowledge making the most hidden part of any foreign environment. Managing cultural knowledge is crucial to success in cultivating and maintaining long-term business relationships in multicultural environments. Knowledge Management provides the long needed technological support for cross-cultural management in the tedious task of improving knowledge sharing in multi-national companies and using knowledge effectively in international joint ventures. The paper is approaching the conceptual frameworks of knowledge management and proposes an unified model of knowledge oriented enterprise and a structural model of a global knowledge management system.Global Business, Intercultural Competencies, Business Intelligence, Multicultural Knowledge Management, Business Knowledge Frameworks, Knowledge Capital

Practices for Business Intelligence Development - Identifying the Knowledge Management Leveraging During the Strategic Tool Creation Process : Action Research in Hitachi Energy

Aim: This study aims to elaborate on knowledge management practice utilization during the designing and implementation of strategic tools for business intelligence, primarily focusing on different organizational levels. Theory: The study covers two research streams: knowledge management and strategy-as-practice. The synthesis of these forms the framework of the study, which is used to observe the participation of different organizational levels in the strategic tool creation process and the information and knowledge received from individual practitioners. Methodology: The empirical part of this study consists of a case study for Hitachi Energy through action research. The data was gathered through process observations and structured and unstructured interviews with practitioners from different organizational levels. Finally, the data analysis is carried out as thematic analysis, which focuses on describing implicit and explicit interpretations. Findings and contribution: The strategy tool design and implementation process usually involve practitioners from different organizational levels with different backgrounds that will bring a unique set of information and knowledge. As a result, it is essential to identify the practitioners who positively contribute to the desired outcome. The findings of the study emphasize the importance of senior and middle management in terms of knowledge input and identify the lower management and operational level as assistive strategy practitioners. The knowledge input of these practitioners can be enhanced by creating agile draft versions during various project stages. The draft versions allow the parties involved to get a better overall view of the project and improve the quality and accuracy of the feedback provided, development suggestions, and other relevant observations. In addition to these findings, significant findings for the strategic tool developer were keeping the big picture in mind and understanding the essential knowledge-related characteristics of different organizational levels