Answer Platform Research Articles

인공지능을 활용한 전력계통 운영관련 자동 법령 질의응답 플랫폼 구축 연구

Electricity related laws are directly related to the property and life of the people, and they contain the preventive and practical work contents, reporting methods and procedures. In order to solve the problems such as interpretation of the law, it is best to answer directly by a person with expert knowledge, but real-time answers are difficult due to limited manpower and working hours. Therefore, in this study, we developed an AI question and answer platform that utilizes big data that allows the general public to easily ask and answer laws related questions. We want to build a system.

A Generative Model for Discovering Action-Based Roles and Community Role Compositions on Community Question Answering Platforms

This paper proposes a generative model for discovering user roles and community role compositions in Community Question Answering (CQA) platforms. While past research shows that participants play different roles in online communities, automatically discovering these roles and providing a summary of user behavior that is readily interpretable remains an important challenge. Furthermore, there has been relatively little insight into the distribution of these roles between communities. Does a community’s composition over user roles vary as a function of topic? How does it relate to the health of the underlying community? Does role composition evolve over time? The generative model proposed in this paper, the mixture of Dirichlet-multinomial mixtures (MDMM) behavior model can (1) automatically discover interpetable user roles (as probability distributions over atomic actions) directly from log data, and (2) uncover community-level role compositions to facilitate such cross-community studies.
 A comprehensive experiment on all 161 non-meta communities on the StackExchange CQA platform demonstrates that our model can be useful for a wide variety of behavioral studies, and we highlight three empirical insights. First, we show interesting distinctions in question-asking behavior on StackExchange (where two distinct types of askers can be identified) and answering behavior (where two distinct roles surrounding answers emerge). Second, we find statistically significant differences in behavior compositions across topical groups of communities on StackExchange, and that those groups that have statistically significant differences in health metrics also have statistically significant differences in behavior compositions, suggesting a relationship between behavior composition and health. Finally, we show that the MDMM behavior model can be used to demonstrate similar but distinct evolutionary patterns between topical groups.

Progressive Web Application for Question and Answer (Q&A) Platform

Progressive Web Application for Question and Answer (Q&A) Platform

Gender differences in participation and reward on Stack Overflow

Programming is a valuable skill in the labor market, making the underrepresentation of women in computing an increasingly important issue. Online question and answer platforms serve a dual purpose in this field: they form a body of knowledge useful as a reference and learning tool, and they provide opportunities for individuals to demonstrate credible, verifiable expertise. Issues, such as male-oriented site design or overrepresentation of men among the site’s elite may therefore compound the issue of women’s underrepresentation in IT. In this paper we audit the differences in behavior and outcomes between men and women on Stack Overflow, the most popular of these Q&A sites. We observe significant differences in how men and women participate in the platform and how successful they are. For example, the average woman has roughly half of the reputation points, the primary measure of success on the site, of the average man. Using an Oaxaca-Blinder decomposition, an econometric technique commonly applied to analyze differences in wages between groups, we find that most of the gap in success between men and women can be explained by differences in their activity on the site and differences in how these activities are rewarded. Specifically, 1) men give more answers than women and 2) are rewarded more for their answers on average, even when controlling for possible confounders such as tenure or buy-in to the site. Women ask more questions and gain more reward per question. We conclude with a hypothetical redesign of the site’s scoring system based on these behavioral differences, cutting the reputation gap in half.

Listening to the user's voice: A temporal analysis of autism‐related questions on Quora

ABSTRACTSocial question and answer (Q&A) platforms offer a new way for identifying information needs of people with certain diseases. Taking Quora as an example, we examine which health topics are of interest to autistic people and how these topics evolve over time. Experimental results reveal increasingly heavy and diverse attention to the condition, from diagnosis and treatment of autism itself to extended issues like social challenges, parenting, and education issues. We find that users tend to post clinical concerns about autism on Quora although traditionally such social Q&A platforms encourage more social and awareness‐level questions. New concerns have appeared recently about autism's relations to other diseases like attention deficit hyperactivity disorder (ADHD) and obsessive–compulsive disorder (OCD). This study is beneficial for tracking and responding to autistic patients' and caregivers' information needs.

Big Data and Dysmenorrhea: What Questions Do Women and Men Ask About Menstrual Pain?

Menstrual pain is highly prevalent among women of reproductive age. As the general public increasingly obtains health information online, Big Data from online platforms provide novel sources to understand the public's perspectives and information needs about menstrual pain. The study's purpose was to describe salient queries about dysmenorrhea using Big Data from a question and answer platform. We performed text-mining of 1.9 billion queries from ChaCha, a United States-based question and answer platform. Dysmenorrhea-related queries were identified by using keyword searching. Each relevant query was split into token words (i.e., meaningful words or phrases) and stop words (i.e., not meaningful functional words). Word Adjacency Graph (WAG) modeling was used to detect clusters of queries and visualize the range of dysmenorrhea-related topics. We constructed two WAG models respectively from queries by women of reproductive age and bymen. Salient themes were identified through inspecting clusters of WAG models. We identified two subsets of queries: Subset 1 contained 507,327 queries from women aged 13-50 years. Subset 2 contained 113,888 queries from men aged 13 or above. WAG modeling revealed topic clusters for each subset. Between female and male subsets, topic clusters overlapped on dysmenorrhea symptoms and management. Among female queries, there were distinctive topics on approaching menstrual pain at school and menstrual pain-related conditions; while among male queries, there was a distinctive cluster of queries on menstrual pain from male's perspectives. Big Data mining of the ChaCha® question and answer service revealed a series of information needs among women and men on menstrual pain. Findings may be useful in structuring the content and informing the delivery platform for educational interventions.

Supporting Knowledge Transfer From Secondary Studies to Software Engineering Practice

Researchers have been arguing that there is a lack of connection between Secondary Studies (SSs) and Software Engi-neering (SE) practice. The medical eld has faced the same prob-lem, and recently introduced the concept of brie ngs/summaries, and Rapid Reviews as alternatives to transfer knowledge to prac-tice.Goal: The overarching goal of this research is to investigate, pro-pose, and evaluate strategies to support researchers to transfer knowledge from SSs to SE practice.Method: First, we investigated how SSs in SE cover practition-ers» issues reported in StackExchange, a leading Question & An-swer platform. Second, we generated Evidence Brie ngs based on those SSs in order to propose a medium to transfer knowledge to practice. Third, we are planning to conduct an action research, with close collaboration with practitioners, in order explore and evaluate the applicability of Rapid Reviews in SE practice. Preliminary Results: Among 424 practitioners» issues on Stack Exchange, that were considered as related to a set o selected SSs, the SSs could successfully cover 14.1% (60) of them. Based on a qualitative techniques, we identi ed 45 recurrent issues spread in many SE topics. Additionally, both practitioners and researchers positively evaluated the content and format of 12 Evidence Brief-ings that we created based on SSs.Conclusions: Our results until now corroborate with claims that SSs lack connection with practice. On the other side, the good reception of the Evidence Brie ngs shows a possible route toward an e ective knowledge transfer from SSs to practice.

Question retrieval for community-based question answering via heterogeneous social influential network

Abstract Community-based question answering platforms have attracted substantial users to share knowledge and learn from each other. As the rapid enlargement of community-based question answering (CQA) platforms, quantities of overlapped questions emerge, which makes users confounded to select a proper reference. It is urgent for us to take effective automated algorithms to reuse historical questions with corresponding answers. In this paper, we focus on the problem with question retrieval, which aims to match historical questions that are relevant or semantically equivalent to resolve one’s query directly. The challenges in this task are the lexical gaps between questions for the word ambiguity and word mismatch problem. Furthermore, limited words in queried sentences cause sparsity of word features. To alleviate these challenges, we propose a novel framework named HSIN which encodes not only the question contents but also the asker’s social interactions to enhance the question embedding performance. More specifically, we apply random walk based learning method with recurrent neural network to match the similarities between asker’s question and historical questions proposed by other users. Extensive experiments on a large-scale dataset from a real world CQA site Quora show that employing the heterogeneous social network information outperforms the other state-of-the-art solutions in this task.

Context-aware semantic classification of search queries for browsing community question–answering archives

Community question answering (cQA) platforms, like Yahoo! Answers, provide standard search APIs to browse past questions and answers. Since, the largest cQA services maintain massive sets of resolved questions, the necessity for effective methods to revitalize the information contained in their archives is getting more and more important to serve the needs of their members as promptly and as reliably as possible.In this paper, we present a novel strategy for effectively browsing cQA archives. The core idea is to induce the semantic classes of question-like search queries (e.g., “rib pain after ovulation” and “iron oxide household”) by means of the contextual information set up or represented by inferred views of their respective search sessions, namely views modeling previous queries entered by the same user.When searching cQA archives, members do not associate semantic classes to their queries, so we are considering the cQA as a knowledge base that defines a taxonomy of semantic classes for their posted questions, which provides an explicit mapping between search queries and these questions. Following a supervised learning approach, we investigate and analyze the most salient features that are necessary to automatically exploit this relevant cQA mapping.We carried out a large number of experiments using a rich set of attributes extracted from an automatically acquired big dataset from Yahoo! Answers. Our results confirm what was often only intuitively assumed, namely, that larger contexts actually help to detect semantic relations implicitly expressed in sequences of queries submitted by users during their search sessions. In particular, we discover that Explicit Semantic Analysis is extremely helpful for inferring discriminative semantic cues that reduce, and thus determine, the semantic range of question-like search queries. Conversely, constructing traditional bag-of-words models on top of prior queries in the session was detrimental.

Evolving Philosophy of OBE in Engineering Physics Course-A Case Study

Engineering Physics Course is offered to all the freshmen students entering our Institution. They are introduced to a question and answer platform, [1] and [2] as a flipped classroom in our course. Their adaptability to the new teaching learning process was put to test after a month into the term. The effectiveness of our teaching and evaluation methodology was tested through an opinion poll, taken voluntarily by the students. The success of the flipped classroom, analysis of the students' survey, the enormous scope it provides for further improvement and some useful inferences that can be included in our pedagogical process in future are compiled in this paper.

STUDYROOM: AN APPLICATION FOR STUDENT COLLABORATION

The StudyRoom application is an incentivized knowledge-sharing platform to engage student-to-student interaction. It is designed to allow students to discuss course content with each other by posting questions and answers. In addition, students can also award points to helpful content and earn badges for displaying unique study habits or reaching various achievements. Coupled with the question and answer platform, the application also provides tools for professors to assess their student’s engagement as well as current academic struggles in a quantifiable manner.