Abstract

We present a lightweight, user-centred approach for document navigation and analysis that is based on an ontology of text mining results. This allows us to bring the result of existing text mining pipelines directly to end users. Our approach is domain-independent and relies on existing NLP analysis tasks such as automatic multi-document summarization, clustering, question-answering, and opinion mining. Users can interactively trigger semantic processing services for tasks such as analyzing product reviews, daily news, or other document sets.

Abstract

Real-time access to complex knowledge is a business driver in the contact centre environment. In this paper we outline for the domain of telecom technical product support a knowledge sharing paradigm in which a desktop client annotates named entities in technical documents with canonical names, class names or relevant class axioms, derived from an ontology by means of a web services framework. We described the system and its core components; OWL-DL telecom hardware ontology, ontological-natural language processing pipeline, an ontology axiom‐extractor; and the Semantic Assistants framework.

Abstract

Semantic Assistants Workflow OverviewToday's knowledge workers have to spend a large amount of time and manual effort on creating, analyzing, and modifying textual content. While more advanced semantically-oriented analysis techniques have been developed in recent years, they have not yet found their way into commonly used desktop clients, be they generic (e.g., word processors, email clients) or domain-specific (e.g., software IDEs, biological tools). Instead of forcing the user to leave his current context and use an external application, we propose a ``Semantic Assistants'' approach, where semantic analysis services relevant for the user's current task are offered directly within a desktop application. Our approach relies on an OWL ontology model for context and service information and integrates external natural language processing (NLP) pipelines through W3C Web services.

Abstract

Despite impressive advances in the development of generic NLP frameworks, content-specific text mining algorithms, and NLP services, little progress has been made in enhancing existing end-user clients with text analysis capabilities. To overcome this software engineering gap between desktop environments and text analysis frameworks, we developed an open service-oriented architecture, based on Semantic Web ontologies and W3C Web services, which makes it possible to easily integrate any NLP service into client applications.

Abstract

Today's knowledger workers are often overwhelmed by the vast amount of readily available natural language documents that are potentially relevant for a given task. Natural language processing (NLP) and text mining techniques can deliver automated analysis support, but they are often not integrated into commonly used desktop clients, such as word processors. We present a plug-in for the OpenOffice.org word processor Writer that allows to access any kind of NLP analysis service mediated through a service-oriented architecture. Semantic Assistants can now provide services such as information extraction, question-answering, index generation, or automatic summarization directly within an end user's application.

Abstract

We investigate the integration of Wiki systems with automated natural language processing (NLP) techniques. The vision is that of a "self-aware" Wiki system reading, understanding, transforming, and writing its own content, as well as supporting its users in information analysis and content development. We provide a number of practical application examples, including index generation, question answering, and automatic summarization, which demonstrate the practicability and usefulness of this idea. A system architecture providing the integration is presented, as well as first results from an initial implementation based on the GATE framework for NLP and the MediaWiki system.

General Terms: Design, Human Factors, Languages
Keywords: Self-aware Wiki System, Wiki/NLP Integration