In this paper we introduce DARE, a methodology for learning Japanese kanji characters, the study of which is an integral part of acquiring the Japanese language. Our methodology covers aspects of Dissecting, Assembling, Retrieving, and Exploring (DARE) to acquire four complementary skills, i.e. to decompose a kanji into its components, put together a kanji from basic building blocks, visually identify a kanji, and understand its usage in context. We have developed a Web-based learning environment to implement this methodology using augmented browsing to train these skills in the context of Wikipedia pages. We have created a workflow which enables language students to choose a specific topic of interest. This is done by selecting keywords which start a crawl process to gather a corpus of Wikipedia pages for this domain. This collection of text is the basis for the dynamically created learning experience.

HCI and NLP traditionally focus on different evaluation methods. While HCI involves a small number of people directly and deeply, NLP traditionally relies on standardized benchmark evaluations that involve a larger number of people indirectly. We present five methodological proposals at the intersection of HCI and NLP and situate them in the context of ML-based NLP models. Our goal is to foster interdisciplinary collaboration and progress in both fields by emphasizing what the fields can learn from each other.

We present a Web-based language learning environment for Japanese, in which we use augmented browsing to create content-related games for kanji acquisition. The games can be applied to various learning scenarios, ranging from autonomous learning to classroom settings. We introduce our generic system architecture that has been used to develop a suite of kanji games focusing on different methodological dimensions of kanji acquisition. As one showcase for incidental kanji training, we offer a brief overview of KANTAN, a strategic card game, which exposes the educational content to the students during engaging game playing activity.

We present a Web-based language learning environment, which uses augmented browsing to display linguistic and semantic information for sentences in Japanese Web pages. For this purpose, we rely on two formalisms that have gained momentum in natural language processing, namely Universal Dependencies (UD) and Abstract Meaning Representation (AMR). We have extended an existing dependency parser to display universal part-of-speech tags, features, and dependency relations, and have aligned word tokens with AMR concepts as well as syntactic relations with AMR roles.

The Integrated Intelligent Computer-Assisted Language Learning (iiCALL) environment offers options to learn natural languages with the use of common working environments like Web browsers or e-mail clients. Therefore, we designed a generic data model and developed a software framework handling language learning processes and information exchanges. A corresponding prototype, implemented as Firefox plug-in, shows the applicability of the generic data model to a specific learning scenario within an iiCALL environment. For developers, the paper proves extensibility by describing the framework and the way of extending iiCALL to add new learning scenarios and functionalities.

The Integrated Intelligent Computer-Assisted Language Learning (iiCALL) environment enables language learning within common working environment like Web browsers or e-mail clients. Based on a generic data model, we developed a software framework for generic and transparent information exchange within those environments. The applicability of both is proven empirically by implementation of a corresponding prototype. The software framework constitutes a major improvement compared to current research. It enables a decoupled realization of individual systems within an iiCALL environment, while still ensuring interoperability during runtime.

We present a Web-based environment for incidental learning of Japanese. As the two key enabling technologies we use augmented browsing at the client and logic programming at the server. This way we can create a learning experience which blends foreign language acquisition with the user's everyday browsing activities. Main features presented in this paper are an intuitive visual presentation of sentence structures, comprehensive support at the word level by integrating several lexical resources including DBpedia, and multiple customization options.

Abstract Meaning Representation (AMR) has emerged recently as one of the most influential formalisms for the semantic representation of natural language. In this paper, we present a Web-based environment for the annotation of Japanese Web pages using augmented browsing and logic programming as the two key enabling technologies. We have developed a visual AMR editor strongly integrated into our Web-based Japanese language learning environment, which hides most of the complexities of AMR syntax from the user and extends it by grounding concepts through links to WordNet synsets and DBpedia resources. The resulting tool can be used meaningfully for several language engineering tasks, in particular, language learning, corpus annotation, and translation.

In this paper, we present a social networking platform for semantic time series processing which enables expert users and time series analysts to improve their data collection and collaborate on common data in order to initiate an automated, dynamic process of assignment of right data to the right user. Our approach is the combination of the research areas of Semantic Web, Time Series Processing, and Community Building as a basis for an interactive and intelligent Web portal for expert users. The basis of our portal is a bridge ontology which enables the integration of specific domain ontologies and thus prepares the stage for the application of ontology mapping and reasoning methods. Furthermore, we present our prototype implementation and provide the validation of our concepts based on two domain ontologies from an international research project.

Based on the idea of Health Level Seven International (HL7) version 3 and its initial architecture and database design, a generic data model for the Integrated Intelligent Computer-Assisted Language Learning (iiCALL) environment has been developed. Open technologies for communication and a knowledge-based and service-oriented architecture (SOA) are key factors of iiCALL. The development process of iiCALL concentrates on object-oriented analysis and object-oriented design. According to the development process of HL7, use-case driven development yields to a message information model which is explained in detail for exemplary use cases.

After introducing the new field of Semantic Time Series Processing, we take our findings one step further and investigate ontology mapping and reasoning in semantic time series processing. This paper shows how ontologies for time series from different areas can be mapped together and how additional meta-information can be generated by using reasoning methods. For ontology mapping we use the bridging concept and present a general time series ontology, which allows the integration of new domain ontologies. In reasoning a popular open source reasoner is used to show generation of new time series meta-information. Additionally, we discuss our results together with a description of the validation for all previously introduced methods.

Size of retrieved data versus crawling time formulate a well-known dilemma in the structured Web crawling community. The real challenge within this dilemma is to optimize the settings of a given wrapper to obtain maximum available data in shortest possible time. In this paper, we try to tune these settings, by introducing a threaded algorithm that guarantees accessing all available detail pages within crawling scope; and using this algorithm, we try to reduce the time consumed by the crawler, via simple adjustments of sleeping time after each detail page visit.

The Integrated Intelligent Computer-Assisted Language Learning (iiCALL) environment is a Web-based e-learning platform that enables language learning in common working environments like Web browsers or email clients. Business logic and language issues are mainly implemented at the iiCALL server. To meet the challenges for providing high quality and context-related language learning, the iiCALL system is assumed to be semantically interoperable, scalable and flexible. Additionally, existing and well approved technologies and standards have to be taken into consideration. Open technologies for communication are as important as a knowledge-based and service-oriented architecture (SOA). Following an idea coming from the healthcare sector, in particular the development of Health Level Seven International (HL7) version 3, the iiCALL development process concentrates on object-oriented analysis and object-oriented design to achieve a generic data model. Based on the initial architecture and database design, the development process for iiCALL applications is redeveloped, which by abstraction yields to the iiCALL generic data model.

In this paper we present an extensible framework for Web-based language learning. At the client side we make extensive use of augmented browsing technology to accommodate various language education approaches with an emphasis on incidental language learning. Though our system architecture is designed in a generic and language-independent way, we built our first use cases for Japanese due to the inherent challenges in studying this complex language. Based on first valuable feedback from students and instructors we have concentrated our recent development efforts on three important extensions: contextual Japanese character training, visualization of Japanese sentence structures, and personal vocabulary lists.

Collaborative research, preservation of data, common access to information and recently an increasingly growing amount of data have been an issue in disciplines in which the studied material is located in many places. This is especially the case in the humanities. Digital libraries are a step towards turning hard copies into digitally available material. The need to manage, share, and preserve this information gave birth to a new term, the Digital Humanities. In this paper we present the current state, goals and future outlook of the European Digital Humanities initiative.

In this paper, we present a new approach of time series enrichment with semantics, and the usage of this technology for building various kinds of communities interested in time series data. The paper shows the problem of assigning time series data to the right party of interest and why this problem could not be solved so far. We demonstrate a new way of processing semantic time series and the consequential ability of addressing and creating target communities. The combination of time series processing and Semantic Web technologies leads us to a new powerful method of data processing and data generation, which offers completely new opportunities to the expert user.

The Integrated Intelligent Computer-Assisted Language Learning (iiCALL) environment is a Web-based e-learning platform that enables language learning in common working environments like Web browsers or email clients. Based on learning theories, it offers context-related learning scenarios for different learning types and different levels of learners. So far, the prototype implements the server architecture which runs inside an Apache Tomcat using Hibernate and MySQL for persistence purposes and which uses the General Architecture for Text Engineering (GATE) framework for Natural Language Processing (NLP) tasks. The prototype also implements a client as a Mozilla Firefox extension by using the XML User Interface Language (XUL) and JavaScript. It exemplarily shows a context-related vocabulary trainer, a learning scenario for language learners on a beginner's level. The paper explains technology of the prototype and points out open issues and future work.

The problem of extracting data that resides in the deep Web has become the center of many research efforts in the recent few years. The challenges in this research area are spanning from online databases discovery and forms extraction from query interfaces, to receiving structured queries from the user, submitting them automatically and retrieving accurate results back to the user. Therefore, the main task is to build an integrated system that connects this variety of missions. In this paper we give an overview of this area of research. We start by surveying previous deep Web systems. After that we define the basic components of a typical deep Web integrated system. Finally, we highlight the current challenges along with possible future research directions.

This paper introduces an approach to build a geographical ontology of countries at the global scale. Our approach is based on the free online encyclopedia Wikipedia to extract lists of places, then rebuild a hierarchy and relationships among extracted places.

The Intelligent Integrated Computer-Assisted Language Learning (iiCALL) environment is a Web-based platform that provides language learning being integrated in common working environments like Web browsers, Email clients, or mobile devices. It applies Natural Language Processing (NLP) technologies, and it uses Semantic Web technologies for context-related language learning. Individual learners prefer individual learning methods based on their stage of language skills and their personal learning types. The paper describes the work in progress of the iiCALL environment. It shows the needs from the perspective of learner types, it gives a technological overview, and it points out the current system architecture of the iiCALL environment.

So far, Computer-Assisted Language Learning (CALL) comes in many different flavors. Our research work focuses on developing an integrated e-learning environment that allows improving language skills in specific contexts. Integrated e-learning environment means that it is a Web-based solution that performs language learning tasks using common working environments like, for instance, Web browsers or Email clients. It should be accessible on different platforms, even on mobile devices. Natural Language Processing (NLP) forms the technological basis for developing such a learning framework. The paper gives an overview of the state-of-the-art in this area. Therefore, on the one hand, it explains creation processes for NLP resources and gives an overview of corpora. On the other hand, it describes existing NLP standards. Based on our requirements, the paper gives special attention to the evaluation and comparison of toolkits that can suitably support the planned implementation. An outlook at the end points out necessary developments in e-learning to keep in mind.

In this paper we present a new approach for the automatic acquisition of linguistic knowledge for machine translation based on parallel corpora and bilingual lexica. We have implemented a first prototype of a Web-based Japanese-English translation system called JETCAT and built a Fire-fox extension to analyze Japanese Web pages and translate sentences via Ajax. In addition, we visualize lexical and translation knowledge to offer a useful tool for Web-based language learning. Finally, the user can simply correct translation results and update the knowledge base resulting in a fully customizable personal translation assistant.