Survey on Community Question Answering Systems

Transcription

1 World Journal of Technology, Engineering and Research, Volume 3, Issue 1 (2018) Contents available at WJTER World Journal of Technology, Engineering and Research Journal Homepage: Survey on Community Question Answering Systems Sonmale Monika a* a P.G. Student, Department of Computer Engineering, VPKBIET, Baramati, Maharashtra, India. Keywords Community question answering Information retrieval Question answering A B S T R A C T Community Question Answering (CQA) is a new research area in the field of Information Retrieval (IR). CQA systems are forums where people share their views, opinions etc. from all over the globe, various questions can be found along with their answers so that user can find his/her problem's solutions. Current Question Answering (QA) websites such as Stack Overflow 1, Quora 2 is gaining popularity, because of the flexibility of these websites to provide information to a user. Early Question Answering Systems (QASs) were domain restricted and had limited capabilities. Early QASs were restricted to domains and have limited capabilities. Current Q&ASs focuses on types of questions generally asked by users, characteristics of data sources consulted, and forms of correct answers generated. In 1960, research in the area of QAs began and since then, a large number of QASs have been developed. Survey on QASs arises naturally to identify the future scope of research in the QAS. This survey gives an outline of current QASs, its system structure and suggests the future scope of the research WJTER All rights reserved. 114

2 1. INTRODUCTION The QASs are concerned with providing relevant answers in response to questions proposed in natural language. Earlier QASs provided answers to the user by matching keywords and concept, but because of that sometimes the user didn t get proper answers to the questions. To solve this challenge, CQA system was developed. In CQA, systems are growing now-a-days to gain greater additional category of knowledge. For example, current CQA system works on too many techniques like automatically answering temporal and geospatial questions, questions of definition and terminology, multilingual questions. Now more and more people are using CQA websites like Stack Overflow 1, Quora 2 etc. to get answers to their questions. These websites allow individuals to post their questions online, and multiple experts across the globe answer them. 1.1 Question Answering (QA) vs Community Question Answering (CQA) A. Question Answering (QA) As shown in Fig. 1, QA system is IR system in which people post questions to the QA system and it gives closest/correct answers. In short, QA system is designed to give automatic answers to the user s queries. The answer to a question may come from background data collection of some form and question answering system might require going through the different steps of scientific technology to assemble a probable answer before returning the same to the user. QA system uses unstructured collection of natural language documents to construct its answers B. Community Question Answering (CQA) Fig. 1. Question Answering (QA) [2]. As shown in Fig. 2 working of current CQA include following steps. i. The querier posts a question. Moderator checks this question for inappropriate content and duplication. After moderator checking, this question is visible to other community user for answering. 115

3 ii. iii. The other users interact in three ways: Users posts relevant or irrelevant answers of the question based on their opinions By upvoting or downvoting answers by other users, based on the validity, significance and content of the responses. By simply commenting on the question. Finally, if the asker is satisfied, she/he may mark the best answer. 2. RELATED WORK Fig. 2. Community Question Answering (CQA) [4]. Recently, research in social-based Q&A systems increased rapidly [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]. Most of social-based Q&A systems use distributed approach to identify credible answerer. For example, Guoxin Liu [1] developed a distributed QA system called iask[1], which include both social community intelligence and global collective intelligence to identify good answerer from the user community. iask[1] used a Neural Network (NN) which helps to consider multiple factors in evaluating the answers of the user's friends. Ze Li and Haiying Shen [2] proposed a distributed Social-based mobile Q&A System (SOS), which enables mobile users to send questions to potential answers in their friend lists in a decentralized manner for a number of hops and then restore to the server. It depends on lightweight knowledge engineering techniques to accurately find out friends who are able to and willing to answer questions, thus reducing the search and computation costs of mobile nodes. To identify the credible users and relevant answers, GunWoo [3] proposed a ranking algorithm called InfluenceRank. It works as the base for analyzing the relationship between user's activities and their mutual understanding. InfluenceRank combines Social Network Analysis (SNA) to judge user authority. By applying SNA, it computes Activity and Trust in Q&A network. Sumanth Patil[4] presented an analysis of experts and non-experts behaviors on Quora and identified four types of features such as user activity features, quality of answer features, linguistic features based on LIWC and temporal features. Social networks have been used for efficient and cooperative file sharing and distribution in peer-to-peer (P2P) Networks [5], [6]. Cheng et al. [5] proposed NetTube, a video forwarding framework that looks for the clustering in social networks for short video sharing. NetTube solved a long queue of key-design issues, which included bi-layer overlay, an effective indexing method, and a pre-fetching methodology using social networks. Wang et 116

4 al. [9] studied the propagation patterns of social video contents in social networks, and based on these patterns, they proposed a social-aware content dissemination method with a hybrid edge-cloud and a peer-assisted architecture to facilitate the video sharing. The works in [6] focus on locating experts and authoritative users as potential answers for Q&A systems. To recognize reliable users and content in social media, Jiang Bain [6] developed a semi-supervised coupled common boosting framework. This framework concurrently calculates elements quality and user position. This framework needed relatively less marked examples to start the training process of the system. The process of information gathering and interaction has increased due to social websites. On basis of this, Evans et al. [6] focused on the social interactions and demonstrated that social interactions can help improve the search results. Social networks also have been leveraged for search engines in recent years [8], [10], [11]. Amitay et al. [8] used enhanced faceted search engine to query social entities (including friends and social bookmarks), the scores of which are evaluated based on the relevance of their documents to the query. David et al. [10] re-ranked the search results by calculating their relevance with individuals in the requester s social network. S. Bao [11] optimizes a web search by using social annotations from the following two aspects: similarity ranking and Static ranking. TABLE I LITERATURE SURVEY IN SUCCINCT. Sr. No. 1 Literature Survey In Succinct Name of paper Technique / Mechanism used Advantages Limitations iask: A Distributed Q&A Guoxin Liu [1] works on to System Incorporating provide with best answer to asker Social Community and Global in CQA system. Collective Intelligence This work helps user to get best answers with the help of experts which are available in user's community. If user do not get best answer then they have to get support from forum sites 2 3 SOS: A Distributed Mobile ZeLi,Haiying[2] works on non- Q&A System Based on Social factual questions posted by users Networks. by searching friends close to them and interested. Credible User Identification using Social Network Analysis in a Q&A Site. P. GunWoo [3] uses ranking algorithm for CQA users where they can be used for answering questions of other users. This system works on basis of mobile connection as mobile is connected and it can help us to get answers in less time. In this system user can answer and answer will go to asker but there is need to study QA from users to find out behavior of user. This system works on This system can find finding relevant user to user who can answer but answer the question in forum they lack in finding sites and find out relations truthfulness and trust of between users and their users and that can help to mutual interests. get best user for answers. 117

5 Detecting experts on Quora: by their activity, quality of answers, linguistic characteristics and temporal behaviors. NetTube: Exploring Social Networks for Peer-to-Peer Short Video Sharing.. Learning to Recognize Reliable Users and Content in Social Media with Coupled Mutual Reinforcement. Towards Social Information Seeking and interaction on the web. This work focuses on finding experts from sites like Quora where they can act like answer system to users. This work helps to find out experts in online forums which can help users to get best answers. This system used four features to find experts on sites there is need to add more no of features to find best expert. In this research work peer to peer video forwarding system is made Data is collected from users used video forwarding data This system has indexing drawback where video so that there will be fewer loads on and then data is sent to users. sharing is not up to the servers of YouTube and there will be less cost. mark and that s why there is limitations of video content sharing. In this work, Jiang Bian [6] found out the quality of content and the rate users who answer the questions in CQA systems. The work [7] focuses on social interactions during the search process by combining a model which attempts to describe human motivation for using computers with an explorative model for social search. Based on users posted content we help to find content based on user's reputations and quality of content. This system tends to find interaction of the users on web based on their behavior and forwarding of content. This system only finds users but not the quality of expertise in domains so there is need to come out with solution for this problem. Users cannot come in contact with other users simultaneously while searching data online. Sr. No. 8 9 Literature Survey In Succinct Name of paper Technique / Mechanism used Advantages Limitations Social Search and Discovery Using a Unified Approach Propagation-Based Social- Aware Replication for Social Video Contents This unified search System returns three result lists for each query: documents, people and tags. This system addresses the challenges in the duplication of social video contents, resulting from the massive number of the videos generated by users and the close-to-uniform popularity distribution. It aims to increase the information gathering on enterprise level and their information can be dependable for information gathering. This system works on the video forwarding based on social network so that it will reduce load of downloading of video. As unified search is implemented to gather information so I won't work on large scale information searching. Videos sent by users are cached by users which seek video and not by level of user of available users. 3. CONCLUSION In this survey, it is observed that these research works cannot be directly used to locate the best cooperative potential answerers for questions because they do not consider the users expertise to answer a specific question. As per my experience, iask [1] is only Q&A system which focuses on users expertise, but it uses only fewer features to rank users and to locate the expert. In the future, I will study the fault tolerance after CQA system failure. 118

6 REFERENCES [1] G. Liu and H. Shen. (2017). iask: A Distributed Q&A System Incorporating Social Community and Global Collective Intelligence. In Proc: IEEE Transactions on Parallel and Distributed Systems. [2] Li, H. Shen, G. Liu, and J. Li. (2012). SOS: A Distributed Mobile Q&A System Based on Social Networks. In Proc: ICDCS. [3] P. GunWoo, Y. SoungWoung, L. SooJin, and L. SangHoon. (2011). Credible user identification using social network analysis in a Q&A site. [4] SumanthPatil and Kyumin Lee. (2015). Detecting experts on Quora: by their activity, quality of answers, linguistic characteristics and temporal Behaviors. [5] X. Cheng and J. Liu. (2009). NetTube: Exploring Social Networks for Peer-to-Peer Short Video Sharing. In Proc: INFOCOM. [6] J. Bian, Y. Liu, D. Zhou, E. Agichtein, and H. Zha. (2009). Learning to Recognize Reliable Users and Content in Social Media with Coupled Mutual Reinforcement. [7] Christian Wolff, Markus Heckner. (2009). Towards Social Information Seeking and interaction on the Web. [8] E. Amitay, D. Carmel, N. Har El, and S. A. Ofek-Koiman.Golbandi. (2009). Social search and discovery using a unified approach. In Proc: HT. [9] Z. Wang, L. Sun, X. Chen, W. Zhu, J. Liu, M. Chen, and S. Yang. (2012). Propagationbased social aware replication for social video contents. In Proc: ACM Multimedia. [10] D. Carmel, N. Zwerdling, I. Guy, S. Ofek-Koifman, N. Har el, I. Ronen, E. Uziel, S. Yogev, and S. Chernov. (2009). Personalized social search based on user s social network. In Proc: CIKM. [11] S. Bao, G. Xue, X. Wu, Y. Yu, B. Fei, and Z. Su. (2007). Optimizing web search using social annotations. In Proc: WWW. 119