User-interactive Question Answering Research Articles

A π-Calculus-Based Complaint Model for a User-Interactive Question Answering System

User-interactive question answering systems are catching more and more attention, however users complaints arise within it from such problems as unfair compensation. This paper focuses on a π-calculus-based formal model for the complaint process of a user-interactive question answering system. The formal model is verified by employing MWB tools so as to demonstrate the deadlock-free and other properties. The research results are of special significance to the comprehensive formal analysis of the user-interactive question answering systems.

Semantic Link Analysis for Finding Answer Experts

Recommending unanswered questions to answer experts is an important mechanism in User-Interactive Question Answering (UIQA) services and is helpful to reduce asker's waiting time and obtain high-quality answers. In this paper, we address the task of identifying answer experts in UIQA services with semantic information extracted from user interaction behaviors. We first construct the user question-answer interaction graph through direct semantic links and latent links extracted from the records of question sessions and user profiles. After that, two expert-finding approaches are developed by employing the semantic information in the so-called propagation link analysis method and in the language model, respectively. Experimental results on Yahoo! Answers dataset show that the extracted semantic information indeed improves the performance of both propagation and language model for the task of answer experts finding.

User interest modeling and its application for question recommendation in user-interactive question answering systems

In this paper, we propose a generative model, the Topic-based User Interest (TUI) model, to capture the user interest in the User-Interactive Question Answering (UIQA) systems. Specifically, our method aims to model the user interest in the UIQA systems with latent topic method, and extract interests for users by mining the questions they asked, the categories they participated in and relevant answer providers. We apply the TUI model to the application of question recommendation, which automatically recommends to certain user appropriate questions he might be interested in. Data collection from Yahoo! Answers is used to evaluate the performance of the proposed model in question recommendation, and the experimental results show the effectiveness of our proposed model.

Toward Automatic Answers in User-Interactive Question Answering Systems

A strategy of automatic answer retrieval for repeated or similar questions in user-interactive systems by employing semantic question patterns is proposed in this paper. The used semantic question pattern is a generalized representation of a group of questions with both similar structure and relevant semantics. Specifically, it consists of semantic annotations (or constraints) for the variable components in the pattern and hence enhances the semantic representation and greatly reduces the ambiguity of a question instance when asked by a user using such pattern. The proposed method consists of four major steps: structure processing, similar pattern matching and filtering, automatic pattern generation, question similarity evaluation and answer retrieval. Preliminary experiments in a real question answering system show a precision of more than 90% of the method.

Term Weighting Schemes for Question Categorization

Term weighting has proven to be an effective way to improve the performance of text categorization. Very recently, with the development of user-interactive question answering or community question answering, there has emerged a need to accurately categorize questions into predefined categories. However, as a question is usually a piece of short text, can the existing term-weighting methods perform consistently in question categorization as they do in text categorization? The answer is not clear, since to the best of our knowledge, we have not seen any work related to this problem despite of its significance. In this study, we investigate the popular unsupervised and supervised term-weighting methods for question categorization. At the same time, we propose three new supervised term-weighting methods, namely, qf*icf, iqf*qf*icf, and vrf. Comparisons of them with existing unsupervised and supervised term-weighting methods are made through a series of experiments on question collections of Yahoo! Answers. The experimental results show that iqf*qf*icf achieves the best performance among all term-weighting methods, while qf*icf and vrf are also competitive for question categorization. Meanwhile, tf*OR is proven to be the most significant one among existing methods. In addition, iqf*qf*icf and vrf are also effective for long document categorization.

Effective Question Recommendation Based on Multiple Features for Question Answering Communities

We propose a new method of recommending questions to answerers so as to suit the answerers’ knowledge and interests in User-Interactive Question Answering (QA) communities. A question recommender can help answerers select the questions that interest them. This increases the number of answers, which will activate QA communities. An effective question recommender should satisfy the following three requirements: First, its accuracy should be higher than the existing category-based approach; more than 50% of answerers select the questions to answer according a fixed system of categories. Second, it should be able to recommend unanswered questions because more than 2,000 questions are posted every day. Third, it should be able to support even those people who have never answered a question previously, because more than 50% of users in current QA communities have never given any answer. To achieve an effective question recommender, we use question histories as well as the answer histories of each user by combining collaborative filtering schemes and content-base filtering schemes. Experiments on real log data sets of a famous Japanese QA community, Oshiete goo, show that our recommender satisfies the three requirements.

Automatic categorization of questions for user-interactive question answering

Question categorization, which suggests one of a set of predefined categories to a user’s question according to the question’s topic or content, is a useful technique in user-interactive question answering systems. In this paper, we propose an automatic method for question categorization in a user-interactive question answering system. This method includes four steps: feature space construction, topic-wise words identification and weighting, semantic mapping, and similarity calculation. We firstly construct the feature space based on all accumulated questions and calculate the feature vector of each predefined category which contains certain accumulated questions. When a new question is posted, the semantic pattern of the question is used to identify and weigh the important words of the question. After that, the question is semantically mapped into the constructed feature space to enrich its representation. Finally, the similarity between the question and each category is calculated based on their feature vectors. The category with the highest similarity is assigned to the question. The experimental results show that our proposed method achieves good categorization precision and outperforms the traditional categorization methods on the selected test questions.

Using a User-Interactive QA System for Personalized E-Learning

A personalized e-learning framework based on a user-interactive question-answering (QA) system is proposed, in which a user-modeling approach is used to capture personal information of students and a personalized answer extraction algorithm is proposed for personalized automatic answering. In our approach, a topic ontology (or concept hierarchy) of course content defined by an instructor is used for the system to generate the corresponding structure of boards for holding relevant questions. Students can interactively post questions, and also browse, select, and answer others’ questions in their interested boards. A knowledge base is accumulated using historical question/answer (Q/A) pairs for knowledge reuse. The students’ log data are used to build an association space to compute the interest and authority of the students for each board and each topic. The personal information of students can help instructors design suitable teaching materials to enhance instruction efficiency, be used to implement the personalized automatic answering and distribute unsolved questions to relevant students to enhance the learning efficiency. The experiment results show the efficacy of our user-modeling approach.

Special Issue: The 2nd International Conference on Semantics, Knowledge and Grid

Semantics and Semantic Grid. Knowledge and Knowledge Grid. Advanced Networking Model. Systems, Tools, and Applications. The 1st conference SKG2005 was successfully inaugurated in Beijing, China 1. The conference co-chairs were H. Zhuge and D. D. Roure. The program Co-Chairs were G. C. Fox and J. Liu. D. Harel presented an invited keynote ‘What's the Semantics of Modeling Nature?’. G. C. Fox presented the theme talk ‘Services for the Semantic Grid’. H. Zhuge presented the theme talk ‘The Knowledge Grid and Its Methodology’. The proceedings of the conference was published by the IEEE Computer Society. The acceptance rate of regular papers is 12%. The special issue of SKG2005 was published in Concurrency and Computation: Practice and Experience 2. SKG2006 was held in Guilin, China 3. The conference chairs were I. Foster, J. Hendler and G. C. Fox. The program chairs were H. Zhuge and T. Ishida. X. Sun served the associate program chair. K. Aberer presented invited keynote ‘Structure and Dynamics of Emergent Semantics Systems’. G. C. Fox presented theme talk ‘Implications of Web 2.0 for the Semantic Grid’; T. Ishida, ‘Use of the Language Grid for Collaborative Work’; and H. Zhuge, ‘Completeness of Query Operations on Resource Spaces’. Submissions came from Australia, China (mainland, Hong Kong, Taiwan), France, India, Italy, Japan, Korea, U.K., and U.S.A. The acceptance rate of regular research papers was 18%. This special issue includes seven papers mainly selected from the regular papers of SKG2006. The paper titled ‘Semantic Knowledge Facilities for a Web-based Recipe Database System Supporting Personalization’ introduces a Web-based recipe database system Dish_Master, which uses Knowledge Grid technologies 4. The paper titled ‘Semantic Patterns for User-Interactive Question Answering’ introduces a user-interactive Question Answering (QA) system where a useful semantic pattern is used 5. The paper titled ‘XML Metadata Services’ introduces a hybrid information service to manage both stateless and transient metadata on services 6. The paper titled ‘Efficient Global Checkpointing Algorithms for Mobile Agents’ proposes checkpointing algorithms for mobile agent systems 7. The paper titled ‘Semantic Representation of Scientific Document for the e-Science Knowledge Grid’ proposes an approach to use a fuzzy cognitive map to represent the semantics of texts by extracting keywords from texts 8. The Resource Space Model is a semantic data model based on classification semantics. The paper titled ‘Resource Space View Centric Viewpoint Tour’ proposes a view mechanism for finding and reusing legacy resource spaces according to users' idiosyncratic resource interests 9. The Semantic Link Network is a solution to the Semantic Web. The paper titled ‘The Modeling Language and Tools for the Semantic Link Network’ presents a UML-based modeling language for the Semantic Link Network. The proposed metamodel, UML profile, and SLN builder and browser for Semantic Link Network are important parts of the Semantic Link Network model 10. SKG has significantly encouraged the development of research and practice on Knowledge Grid and Semantic Grid. We expect this special issue to play an important role in promoting relevant cross-area research.