Graph documents (GDs) can express the content of any nonartistic text documents (TDs). Experiments have shown that the collaborative GD composition is more efficient than collaborative TD composition. Another experiment demonstrated that regular high school classrooms can not only seamlessly integrate collaborative GD composition without incurring additional costs, but also improve students' CT skills. Opting for GDs over TDs hence promises an enhanced efficiency not only in collaborative document composition but also in intellectual works in general.

It is ideal to provide medical services as patient-oriented. The medical staff members share the final goals to recover patients. Toward the goals, each staff has practical knowledge to achieve patient-oriented medical services. But each medical staff has his/her own sense of value that comes from his/her expertness. Therefore the practical knowledge sometimes conflicts. The aim of this research is to develop an intelligent system to support externalizing practical knowledge, and sharing it among medical staff members. In this paper, the author propose a method to model the sense of value of each medical staff as his/her understanding about medical service workflow, and to obtain the practical knowledge using the models. The method was experimented by an implementation of knowledge-sharing system base on the method and by its trial use in Miyazaki University Hospital.

The contribution of this paper is twofold: 1) we provide a public corpus for Human-Agent Interaction (where the agent is controlled by a Wizard of Oz) and 2) we show a study on verbal alignment in Human-Agent Interaction, to exemplify the corpusu0027 use. In our recordings for the Human-Agent Interaction Alice-corpus (HAI Alice-corpus), participants talked to a wizarded agent, who provided them with information about the book Alice in Wonderland and its author. The wizard had immediate and almost full control over the agentu0027s verbal and nonverbal behavior, as the wizard provided the agentu0027s speech through his own voice and his facial expressions were directly copied onto the agent. The agentu0027s hand gestures were controlled through a button interface. Data was collected to create a corpus with unexpected situations, such as misunderstandings, (accidental) false information, and interruptions. The HAI Alice-corpus consists of transcribed audio-video recordings of 15 conversations (more than 900 utterances) between users and the wizarded agent. As a use-case example, we measured the verbal alignment between the user and the agent. The paper contains information about the setup of the data collection, the unexpected situations and a description of our verbal alignment study.

Search and recommendation systems help users find their favorites among an abundant amount of songs available on distributors such as iTunes. Nevertheless, it is still hard for us to efficiently retrieve the right music. This paper proposes an interactive music exploration system to help them reach their favorite songs by means of interactive feedbacks submitted by the user. The recommendation component of our system is based on the feedback component accepts the user's feedbacks in terms of impression features such as "more ballad-like" and "less pop-like". The proposed concepts have been implemented as an online demonstration system using a real world dataset.

A potential work item (PWI) for ISO standard (MAP) about linguistic annotation concerning syntax-semantics mapping is discussed. MAP is a framework for graphical linguistic annotation to specify a mapping (set of combinations) between possible syntactic and semantic structures of the annotated linguistic data. Just like a UML diagram, a MAP diagram is formal, in the sense that it accurately specifies such a mapping. MAP provides a diagrammatic sort of concrete syntax for linguistic annotation far easier to understand than textual concrete syntax such as in XML, so that it could better facilitate collaborations among people involved in research, standardization, and practical use of linguistic data. MAP deals with syntactic structures including dependencies, coordinations, ellipses, transsentential constructions, and so on. Semantic structures treated by MAP are argument structures, scopes, coreferences, anaphora, discourse relations, dialogue acts, and so forth. In order to simplify explicit annotations, MAP allows partial descriptions, and assumes a few general rules on correspondence between syntactic and semantic compositions.

The survey paper explains about the extraction and retrieval of personal name alias using various techniques from the web with the help of web crawls. The existing methods help to improve the depth of knowledge relevant to alias extraction and retrieval process. It also describes about how the aliases are ranked, then page counts on the web, word co-occurrence using anchor text and techniques like term frequency (tf), inverse document frequency (idf), log likelihood ratio. Chi-squared tests etc.., are used for measuring the association and similarities between words. The existing method consists of pattern extraction algorithm or string matching algorithm for extracting patterns from snippets instead of using these algorithms. The survey helps to discover a proposed method as graph mining to extract personal name aliases from the web.

Information technologies penetrate virtually every division in contemporary organizations. Organizations deploy a spectrum of information technologies with aim of alleviating operating efficiency. Knowledge workers increasingly depend on deployed information systems to accomplish their tasks. Well-deployed and managed information systems have a potential to increase effectiveness and efficiency in organizations; whereas poorly deployed and managed systems may have significant negative impact. Strategic deployment and management of information systems play the key roles in attaining beneficial impacts for organizations. Conventionally, information technology managers have relied primarily on tacit knowledge. Such knowledge and experiences have been accumulated over a number of years. However, information technologies progress at a rapid pace and early adopters gain considerable strategic advantages. Information technology managers cannot afford spending years accumulating tacit knowledge. Viable solution to this problem is to adopt an analytics-based management. We explore pertinent aspects of analytics-based management of information systems in organizations.

De-identifying textual data is an important task for publishing and sharing the data among researchers while protecting privacy of individuals referenced therein. While supervised learning approaches are successfully applied to the task in the clinical domain, existing methods are hard to transfer to different domains and languages because they require a considerable cost and time for preparation of linguistic resources. This paper presents an efficient unsupervised algorithm to detect all substrings occurring less than k times in the input string, based on the assumption that such rare sequences are likely to contain sensitive information such as names of people and rare diseases that may identify individuals. The proposed algorithm works in asymptotically and empirically linear time against the input size when k is a constant. Empirical evaluation on the i2b2 (Informatics for Integrating Biology and Bedside) dataset shows the effectiveness of the algorithm in comparison to baselines that use simple word frequencies.

This paper presents a novel attempt to generate annotated corpora by making use of grammar based autocompletion. The idea is to automatically generate corpora on the fly while a user is working on his own stuff. In the medical domain, this user would be a physician. While he uses an authoring tool to write a pathology report or enters text in an Electronic Healthcare Record (EHR) system, grammar and ontology-based autocompletion is built into such systems such that user input is limited to text parseable by the grammar used for autocompletion. As soon as the user finishes his work, the grammar used for autocompletion would be used for assigning syntactic structures and semantic representations to his input automatically. This way corpora can be generated by limiting annotation and grammar writing by a linguist to help building grammar and ontology-based autocompletion into a EHR system or an authoring tool of pathology reports. After autocompletion starts working hand-in-hand with these applications, new input from users does not need further annotation by human Users are not supposed to be paid for using an EHR system or an authoring tool with built-in autocompletion that helps them to do their job.

Data mining of clinical data that are stored continually in the course of daily medical practice will contribute to the advancement of healthcare. However, real-world clinical data are characteristically noisy, sparse, and irregular, which makes it difficult to perform data mining. This study assesses an exploratory approach to ascertain how physicians tackle the worsening of a patient's condition using clinical data from a hospital. It yielded reasonable results.

This standard is the first part of the series of ISO standards that are targeted at controlled natural language (CNL) in written languages. It focuses on the basic concepts and general principles of CNL that apply to languages in general. It will cover properties of CNL and CNL classification scheme. The subsequent parts will, however, focus on the issues specific to particular viewpoint and/or applications, such as particular CNLs, CNL interfaces, implementation of CNLs, and evaluation techniques for CNL.

This paper describes a machine-learning based approach to recognizing diagnosed disease names and corresponding temporal expressions. Using CRFs (conditional random fields) to learn and predict tags, the systems described in this paper are characterized by a character-level formulation and heuristic features extracted from medical terminologies. Experimental results on the NTCIR-11 MedNLP-2 datasets suggest that the approach effectively exploit terminological resources and combine them with other NLP (natural language processing) resources including morphological analyzers.

In today's fast-paced global economy, changes in the environment result in new opportunities for wealth creation that decision makers use in strategy formulation and implementation. Organizations have grown global by not only expanding businesses and setting up branches overseas, but they have grown global in a different kind of way-leveraging on knowledge tapped globally rather than merely growing outward from a domestic base. In organizations, information from various departments and functions, as well as formal and informal sources of information, are boughtIntroduction ........................................................................................................1 The Need for Data Governance ........................................................................2 Place for Data Governance in Organizational Strategy .................................3 Data Governance across Intrafirm Networks .................................................4 Data Governance Characteristics of Organizations through Their Life Cycle .............................................................................................................5 Data Governance Program ................................................................................7 Benefits from Data Governance .......................................................................9 Data Quality and Data Governance Process .................................................10 Data Governance and Master Data Management ........................................13 Managing Risk with Data Governance ..........................................................13 Information Governance and Cloud Computing ........................................14 Conclusion ........................................................................................................15 References ..........................................................................................................16 Further Readings ..............................................................................................16together to detect and interpret problem areas, identify opportunities, and implement strategic objectives. Strategies define the purpose of organizations, the competitive domain of firms, and the resource commitment these organizations make to achieve and sustain competitive advantage. Hence, developing an appropriate data strategy that fits the marketplace is one necessary ingredient for business success. Effective data governance reduces uncertainty and helps improve an organization's performance. An organization's ability to collect pertinent information and act on signals that others miss provides it a strategic advantage.

In agile software development, it is imperative for stakeholders such as the users and developers of an information system to collaborate in designing and developing the information system, by sharing their knowledge. Especially in development of a large-scale information system, such collaboration among stakeholders is important, but difficult to achieve. This chapter introduces a modeling method of business processes for requirements analysis and a development framework based on Web-process architectures. The modeling method makes it easier for stakeholders to agree upon requirements. It also employs a formal method to allow business process models to satisfy both understandability and accuracy. On the other hand, the development framework above enables rapid spiral development of short-term cycles through the collaboration of developers and users. This chapter also introduces an example that compares the workloads of two requirement analyses of large-scale system developments for a government service and a financial accounting service, in order to evaluate the advantages of the proposed modeling method.