FarsBase-KBP: A knowledge base population system for the Persian Knowledge Graph

While most of the knowledge bases already support the English language, there is only one knowledge base for the Persian language, known as FarsBase, which is automatically created via semi-structured web information. Unlike English knowledge bases such as Wikidata, which have tremendous community support, the population of a knowledge base like FarsBase must rely on automatically extracted knowledge. Knowledge base population can let FarsBase keep growing in size, as the system continues working. In this paper, we present a knowledge base population system for the Persian language, which extracts knowledge from unlabelled raw text, crawled from the Web. The proposed system consists of a set of state-of-the-art modules such as an entity linking module as well as information and relation extraction modules designed for FarsBase. Moreover, a canonicalization system is introduced to link extracted relations to FarsBase properties. Then, the system uses knowledge fusion techniques with minimal intervention of human experts to integrate and filter the proper knowledge instances, extracted by each module. To evaluate the performance of the presented knowledge base population system, we present the first gold dataset for benchmarking knowledge base population in the Persian language, which consisting of 22015 FarsBase triples and verified by human experts. The evaluation results demonstrate the efficiency of the proposed system.     

The effects of animated pedagogical agents in an English-as-a-foreign-language learning environment

Animated pedagogical agents (APAs) have frequently been used as a powerful addition to learning environments, since APAs have been known to facilitate learning. APAs can present various features, such as voice, movements, gestures and pointing, and researchers have sought to verify specifically which features of agents effectively contribute to learning. Previous studies have studied these features by comparing different degrees of agent embodiment in the evaluation of the image effect (i.e., students learn more when learning systems have visual APAs), the embodied agent effect (i.e., fully embodied agents that deliver instruction aurally and use gestures to improve learning outcomes in text-only learning systems), the modality effect (i.e., oral instruction contributes to the learning process), and the expressiveness effect (i.e., fully embodied agents promote more effective learning than static ones). Some of these studies have investigated the image, embodied agent and modality effects in the same learning environment, but they were not the same studies that investigated the expressiveness effect. The expressiveness effect allows us to separate the movements of the agent from its other features, such as the agent's image, so investigating this effect is as important as investigating the other effects. We are not aware of any studies that investigated all of these four effects within the same learning system, nor that evaluated any of these effects in language learning environments. Accordingly, this paper describes the design, implementation, and analysis of an APA designed to evaluate the abovementioned effects. The APA was integrated into a computer-assisted language learning (CALL) system to teach English as a foreign language to Brazilian students. A total of 72 Brazilian undergraduate students were divided into four groups, each of which used a different version of the APA in the same CALL system: no agent, a voice-only agent, a static agent, or a fully embodied agent. We compared students’ gain scores (i.e., difference between pre- and posttest scores) across groups to evaluate each of the four effects. Though the outcomes of our study supported the presence of the embodied agent and modality effects, we were not able to demonstrate the image or expressiveness effects in the experiment. Our results indicate that the voice of the agent might contribute more positively to learning than movements, gestures and pointing.     

A knowledge construction methodology to automate case-based learning using clinical documents

The case-based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer-based domain knowledge to medical students for solving real-world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine-readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM-CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM-CD methodology, an interactive case-based learning system(iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.     

基于70年报刊语料的现代汉语历时稳态词抽取与考察

该文基于70年跨度的历时报刊语料库,使用九种统计方法计算了词语历年的使用情况,并通过对稳定性、覆盖度和时间区分性能的考察筛选获得了规模为3 013词的历时稳态词候选词集。该词集中动词与名词各占约三分之一(其余为形容词、副词与虚词),平均词长约1.7字,前密后疏地分布于历时语料库总频序表的前7 609位,覆盖了总语料的近九成。该部分词语中包含大量构造句子结构的核心词语。它们塑造了稳态词在词长和词类上的特性。稳态词的提取可以加深对语言生活底层与基础词汇的认识,对汉语教学、中文信息处理和语言规划都具有重要意义。     

Mímir: An open-source semantic search framework for interactive information seeking and discovery

Semantic search is gradually establishing itself as the next generation search paradigm, which meets better a wider range of information needs, as compared to traditional full-text search. At the same time, however, expanding search towards document structure and external, formal knowledge sources (e.g. LOD resources) remains challenging, especially with respect to efficiency, usability, and scalability.This paper introduces Mímir—an open-source framework for integrated semantic search over text, document structure, linguistic annotations, and formal semantic knowledge. Mímir supports complex structural queries, as well as basic keyword search.Exploratory search and sense-making are supported through information visualisation interfaces, such as co-occurrence matrices and term clouds. There is also an interactive retrieval interface, where users can save, refine, and analyse the results of a semantic search over time. The more well-studied precision-oriented information seeking searches are also well supported.The generic and extensible nature of the Mímir platform is demonstrated through three different, real-world applications, one of which required indexing and search over tens of millions of documents and fifty to hundred times as many semantic annotations. Scaling up to over 150 million documents was also accomplished, via index federation and cloud-based deployment.     

GoBIS: An integrated framework to analyse the goal and business process perspectives in information systems

ContextOrganisational reengineering, continuous process improvement, alignment among complementary analysis perspectives, and information traceability are some current motivations to promote investment and scientific effort for integrating goal and business process perspectives. Providing support to integrate information systems analysis becomes a challenge in this complex setting.ObjectiveThe GoBIS framework integrates two goal and business process modelling approaches: i^⁎ (a goal-oriented modelling method) and Communication Analysis (a communication-oriented business process modelling method).MethodIn this paper, we describe the methodological integration of both methods with the aim of fulfilling several criteria: i) to rely on appropriate theories; ii) to provide abstract and concrete syntaxes; iii) to provide scenarios of application; iv) to develop tool support; v) to provide demonstrable benefits to potential adopters.ResultsWe provide guidelines for using the two modelling methods in a top-down analysis scenario. The guidelines are validated by means of a comparative experiment and a focus-group session with students.ConclusionsFrom a practitioner viewpoint (modeller and/or analyst), the guidelines facilitate the traceability between goal and business process models, the experimental results highlight the benefits of GoBIS in performance and usability perceptions, and demonstrate an improvement on the completeness of the latter having an impact on efficiency. From a researcher perspective, the validation has produced useful feedback for future research.     

文本摘要常用数据集和方法研究综述

文本摘要成为人们从互联网上海量文本信息中便捷获取知识的重要手段。现有方法都是在特定数据集上进行训练和效果评价,包括一些公用数据集和作者自建数据集。已有综述文献对现有方法进行全面细致的总结,但大多都是对方法进行总结,而缺少对数据集的详细描述。该文从调研数据集的角度出发,对文本摘要常用数据集及在该数据集上的经典和最新方法进行综述。对公用数据集的综述包括数据来源、语言及获取方式等,对自建数据集的总结包括数据规模、获取和标注方式等。对于每一种公用数据集,给出了文本摘要问题的形式化定义。同时,对经典和最新方法在特定数据集上的实验效果进行了分析。最后,总结了已有常用数据集和方法的现状,并指出存在的一些问题。     

大规模连续中国手语数据集的创建与分析

绝大多数健听人不懂手语导致听障人在找工作、就医、法律咨询等各生活、工作领域中遇到了极大的沟通障碍，而手语翻译员需要提前预约，成本也非常高，所以很多科研工作者都开始利用机器学习来开发手语自动翻译器，但其中的大部分研究都因为受到了数据集规模和质量的影响而效果不佳。为解决上述矛盾和问题，创建了目前全球最大的中国连续手语数据集，并使用了考虑身体关节的位置、面部表情及手指关节的端到端的深度学习模型进行有效训练。结论突显了现代深度学习技术在识别复杂手语方面的巨大优势，针对较小子集的BLEU-4已达到30.8。