Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts

Automated Question Generation System for Genesis

Automatic Question Generation systems automatically generate questions from input such as text. This study implements an Automated Question Generation system for Genesis, a program that analyzes text. The Automated Question Generation system for Genesis outputs a ranked list of questions over content Genesis does not understand. It does this using a Question Generation Module and Question Ranking module. The Question Generation Module determines what content Genesis does not understand and generates questions using rules. The Question Ranking Module ranks the questions by relevance. This Automated Question Generation system was evaluated on a story read by Genesis. The average question relevance among the top 10 generated questions was 2.41 on a scale of 1-3, with 3 being most relevant. 53.8% of subjects ranked questions in the same order as the Question Ranking Module. The results suggest that the Automated Question generation system produces an optimally ranked list of relevant questions for Genesis

Improving Academic Natural Language Processing Infrastructures Utilizing Cluster Computation

In light of widespread digitization endeavors and ever-growing textual data generation, developing efficient academic Natural Language Processing (NLP) infrastructures, which can deal with large amounts of data, is of particular importance. Novel computation technologies allow tools that support big data and heavy computation while performing timely and cost-effective data processing. This development has led researchers to demand that knowledge be extracted from ever-increasing textual data before it is outdated. Cluster computation is a modern technology for handling big data efficiently. It provides distribution of computing and data over a number of machines in a cluster, as well as efficient use of resources, which are key requirements to process big data in a timely manner. It also assures applications’ high availability and fault tolerance, which are fundamental concerns when dealing with vast amounts of data. In addition, it provides load balancing of data during the execution of tasks, which results in optimal use of resources and enhances efficiency. Data-oriented parallelization is an effective solution to enable the currently available academic NLP infrastructures to process big data. This approach offers a solution to parallelize the NLP tools which comprise identical non-complicated tasks without the expense of changing NLP algorithms. This thesis presents the adaption of cluster computation technology to academic NLP infrastructures to address the notable features that are essential to process vast quantities of text materials efficiently, in terms of both resources and time. Apache Spark on top of Apache Hadoop and its ecosystem have been utilized to develop a set of NLP tools that provide a distributed environment to execute the NLP tasks. Many experiments were conducted to assess the functionality of the designated strategy. This thesis shows that using cluster computation technology and data-oriented parallelization enables academic NLP infrastructures to execute large amounts of textual data in a timely manner while improving the performance of the NLP tools. Moreover, these experiments provide information that brings a more realistic and transparent estimation of workflows’ costs (required hardware resources) and execution time, along with the fastest, optimum, or feasible resource configuration for each individual workflow. This knowledge can be employed by users to trade-off between run-time, size of data, and hardware, and it enables them to design a strategy for data storage, duration of data retention, and delivery time. This has the potential to enhance researchers’ satisfaction when using academic NLP infrastructures. The thesis also shows that a cluster computation approach provides the capacity to adapt NLP services with JIT delivery systems. The proposed strategy assures the reliability and predictability of the services, which are the main characteristics of the services in JIT delivery systems. Defining the relevant parameters, recording the behavior of the services, and analyzing the generated data resulted in the provision of knowledge that can be utilized to create a service catalog—a fundamental requirement for the services in JIT delivery systems—for each service offered. This knowledge also helps to generate the performance profiles for each item mentioned in the service catalog and to update them continuously to cover new experiments and improve service quality

Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks across Languages

We test the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Controlling the size factor, we investigate this hypothesis for a number of 25 subject areas. Since Wikipedia is a central part of the web-based information landscape, this indicates a language-related, linguistic bias. The article therefore deals with the question of whether Wikipedia exhibits this kind of linguistic relativity or not. From the perspective of educational science, the article develops a computational model of the information landscape from which multiple texts are drawn as typical input of web-based reading. For this purpose, it develops a hybrid model of intra- and intertextual similarity of different parts of the information landscape and tests this model on the example of 35 languages and corresponding Wikipedias. In this way the article builds a bridge between reading research, educational science, Wikipedia research and computational linguistics.Comment: 40 pages, 13 figures, 5 table

The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing: Extended Survey

Graph processing is becoming increasingly prevalent across many application domains. In spite of this prevalence, there is little research about how graphs are actually used in practice. We performed an extensive study that consisted of an online survey of 89 users, a review of the mailing lists, source repositories, and whitepapers of a large suite of graph software products, and in-person interviews with 6 users and 2 developers of these products. Our online survey aimed at understanding: (i) the types of graphs users have; (ii) the graph computations users run; (iii) the types of graph software users use; and (iv) the major challenges users face when processing their graphs. We describe the participants' responses to our questions highlighting common patterns and challenges. Based on our interviews and survey of the rest of our sources, we were able to answer some new questions that were raised by participants' responses to our online survey and understand the specific applications that use graph data and software. Our study revealed surprising facts about graph processing in practice. In particular, real-world graphs represent a very diverse range of entities and are often very large, scalability and visualization are undeniably the most pressing challenges faced by participants, and data integration, recommendations, and fraud detection are very popular applications supported by existing graph software. We hope these findings can guide future research

Requirements Analysis for an Open Research Knowledge Graph

Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get an overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work. Recently, several initiatives have proposed knowledge graphs (KGs) for organising scientific information as a solution to many of the current issues. The focus of these proposals is, however, usually restricted to very specific use cases. In this paper, we aim to transcend this limited perspective by presenting a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting daily core tasks of a scientist, (b) establishing their consequential requirements for a KG-based system, (c) identifying overlaps and specificities, and their coverage in current solutions. As a result, we map necessary and desirable requirements for successful KG-based science communication, derive implications and outline possible solutions.Comment: Accepted for publishing in 24th International Conference on Theory and Practice of Digital Libraries, TPDL 202

Linking Textual Resources to Support Information Discovery

A vast amount of information is today stored in the form of textual documents, many of which are available online. These documents come from different sources and are of different types. They include newspaper articles, books, corporate reports, encyclopedia entries and research papers. At a semantic level, these documents contain knowledge, which was created by explicitly connecting information and expressing it in the form of a natural language. However, a significant amount of knowledge is not explicitly stated in a single document, yet can be derived or discovered by researching, i.e. accessing, comparing, contrasting and analysing, information from multiple documents. Carrying out this work using traditional search interfaces is tedious due to information overload and the difficulty of formulating queries that would help us to discover information we are not aware of. In order to support this exploratory process, we need to be able to effectively navigate between related pieces of information across documents. While information can be connected using manually curated cross-document links, this approach not only does not scale, but cannot systematically assist us in the discovery of sometimes non-obvious (hidden) relationships. Consequently, there is a need for automatic approaches to link discovery. This work studies how people link content, investigates the properties of different link types, presents new methods for automatic link discovery and designs a system in which link discovery is applied on a collection of millions of documents to improve access to public knowledge

Requirements Analysis for an Open Research Knowledge Graph

Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get a full overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work. Recently, several initiatives have proposed knowledge graphs (KGs) for organising scientific information as a solution to many of the current issues. The focus of these proposals is, however, usually restricted to very specific use cases. In this paper, we aim to transcend this limited perspective by presenting a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting daily core tasks of a scientist, (b) establishing their consequential requirements for a KG-based system, (c) identifying overlaps and specificities, and their coverage in current solutions. As a result, we map necessary and desirable requirements for successful KG-based science communication, derive implications and outline possible solutions