Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Social Search: retrieving information in Online Social Platforms -- A Survey

Social Search research deals with studying methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new taxonomy and highlighting current limitations and open research directions. We divide the Social Search area into three subcategories, where the social aspect plays a pivotal role: Social Question&Answering, Social Content Search, and Social Collaborative Search. For each subcategory, we present the key concepts and selected representative approaches in the literature in greater detail. We found that, up to now, a large body of studies model users' preferences and their relations by simply combining social features made available by social platforms. It paves the way for significant research to exploit more structured information about users' social profiles and behaviors (as they can be inferred from data available on social platforms) to optimize their information needs further

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Software expert discovery via knowledge domain embeddings in a collaborative network

© 2018 Elsevier B.V. Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considering that massive amount of users are participating online and generating huge amount data, management of knowledge here systematically can be challenging. Expert recommendation is one of the major challenges, as it highlights users in CQA with potential expertise, which may help match unresolved questions with existing high quality answers while at the same time may help external services like human resource systems as another reference to evaluate their candidates. In this paper, we in this work we propose to exploring experts in CQA websites. We take advantage of recent distributed word representation technology to help summarize text chunks, and in a semantic view exploiting the relationships between natural language phrases to extract latent knowledge domains. By domains, the users’ expertise is determined on their historical performance, and a rank can be compute to given recommendation accordingly. In particular, Stack Overflow is chosen as our dataset to test and evaluate our work, where inclusive experiment shows our competence

Hierarchical Expert Recommendation on Community Question Answering Platforms

The community question answering (CQA) platforms, such as Stack Overflow, have become the primary source of answers to most questions in various topics. CQA platforms offer an opportunity for sharing and acquiring knowledge at a low cost, where users, many of whom are experts in a specific topic, can potentially provide high-quality solutions to a given question. Many recommendation methods have been proposed to match questions to potential good answerers. However, most existing methods have focused on modelling the user-question interaction — a user might answer multiple questions and a question might be answered by multiple users — using simple collaborative filtering approaches, overlooking the rich information in the question’s title and body when modelling the users’ expertise. This project fills the research gap by thoroughly examining machine learning and deep learning approaches that can be applied to the expert recommendation problem. It proposes a Hierarchical Expert Recommendation (HER) model, a deep learning recommender system that recommends experts to answer a given question in the CQA platform. Although choosing a deep learning over a machine learning solution for this problem can be justified considering the degree of complexity of the available datasets, we assess performance of each family of methods and evaluate the trade-off between them to pick the perfect fit for our problem. We analyzed various machine learning algorithms to determine their performances in the expert recommendation problem, which narrows down the potential ways for tackling this problem using traditional recommendation methods. Furthermore, we investigate the recommendation models based on matrix factorization to establish the baselines for our proposed model and shed light on the weaknesses and strengths of matrix- based solutions, which shape our final deep learning model. In the last section, we introduce the Hierarchical Expert Recommendation System (HER) that utilizes hierarchical attention-based neural networks to rep- resent the questions better and ultimately model the users’ expertise through user-question interactions. We conducted extensive experiments on a large real-world Stack Overflow dataset and benchmarked HER against the state-of-the-art baselines. The results from our extensive experiments show that HER outperforms the state-of-the-art baselines in recommending experts to answer questions in Stack Overflow

SOCIALQ&A: A NOVEL APPROACH TO NOTIFIYING THE CORRECT USERS IN QUESTION AND ANSWERING SYSTEMS

Question and Answering (Q&A) systems are currently in use by a large number of Internet users. Q&A systems play a vital role in our daily life as an important platform for information and knowledge sharing. Hence, much research has been devoted to improving the performance of Q&A systems, with a focus on improving the quality of answers provided by users, reducing the wait time for users who ask questions, using a knowledge base to provide answers via text mining, and directing questions to appropriate users. Due to the growing popularity of Q&A systems, the number of questions in the system can become very large; thus, it is unlikely for an answer provider to simply stumble upon a question that he/she can answer properly. The primary objective of this research is to improve the quality of answers and to decrease wait times by forwarding questions to users who exhibit an interest or expertise in the area to which the question belongs. To that end, this research studies how to leverage social networks to enhance the performance of Q&A systems. We have proposed SocialQ&A, a social network based Q&A system that identifies and notifies the users who are most likely to answer a question. SocialQ&A incorporates three major components: User Interest Analyzer, Question Categorizer, and Question- User Mapper. The User Interest Analyzer associates each user with a vector of interest categories. The Question Categorizer algorithm associates a vector of interest categories to each question. Then, based on user interest and user social connectedness, the Question-User Mapper identifies a list of potential answer providers for each question. We have also implemented a real-world prototype for SocialQ&A and analyzed the data from questions/answers obtained from the prototype. Results suggest that social networks can be leveraged to improve the quality of answers and reduce the wait time for answers. Thus, this research provides a promising direction to improve the performance of Q&A systems