Abstract

The Text Analysis Conference (TAC) 2009 competition featured a new textual entailment search task, which extends the 2008 textual entailment task. The goal is to find information in a set of documents that are entailed from a given statement. Rather than designing a system specifically for this task, we investigated the adaptation of an existing artificial believer system to solve this task. The results show that this is indeed possible, and furthermore allows to recast the existing, divergent tasks of textual entailment and automatic summarization under a common umbrella.

With the rapidly growing amount of information available, employees spend an ever-increasing proportion of their time searching for the right information. Information overload has become a serious threat to productivity. We address this challenge with a service-oriented architecture that integrates semantic natural language processing services into desktop applications.

Abstract

Software development and maintenance are highly distributed processes that involve a multitude of supporting tools and resources. Knowledge relevant to these resources is typically dispersed over a wide range of artifacts, representation formats, and abstraction levels. In order to stay competitive, organizations are often required to assess and provide evidence that their software meets the expected requirements. In our research, we focus on assessing non-functional quality requirements, specifically evolvability, through semantic modeling of relevant software artifacts. We introduce our SE-Advisor that supports the integration of knowledge resources typically found in software ecosystems by providing a unified ontological representation. We further illustrate how our SE-Advisor takes advantage of this unified representation to support the analysis and assessment of different types of quality attributes related to the evolvability of software ecosystems.

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

This paper gives a brief overview of the International Workshop on Semantic Technologies in System Maintenance. It describes a number of semantic technologies (e.g., ontologies, text mining, and knowledge integration techniques) and identifies diverse tasks in software maintenance where the use of semantic technologies can be beneficial, such as traceability, system comprehension, software artifact analysis, and information integration.

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

Nowadays, software development and maintenance are highly distributed processes that involve a multitude of supporting tools and resources. Knowledge relevant for a particular software maintenance task is typically dispersed over a wide range of artifacts in different representational formats and at different abstraction levels, resulting in isolated 'information silos'. An increasing number of task-specific software tools aim to support developers, but this often results in additional challenges, as not every project member can be familiar with every tool and its applicability for a given problem. Furthermore, historical knowledge about successfully performed modifications is lost, since only the result is recorded in versioning systems, but not how a developer arrived at the solution. In this research, we introduce conceptual models for the software domain that go beyond existing program and tool models, by including maintenance processes and their constituents. The models are supported by a pro-active, ambient, knowledge-based environment that integrates users, tasks, tools, and resources, as well as processes and history-specific information. Given this ambient environment, we demonstrate how maintainers can be supported with contextual guidance during typical maintenance tasks through the use of ontology queries and reasoning services.

Abstract

An Automatically Generated Summary
ERSS 2008 attempted to rectify certain issues of ERSS 2007. The improvements to readability, however, do not reflect in significant score increases, and in fact the system fell in overall ranking. While we have not concluded our analysis, we present some preliminary observations here.

The SE-ADVISOR tool presents a novel approach to support software evolution, by integrating maintenance relevant knowledge resources, processes, and their constituents. We demonstrate how our SE-ADVISOR environment can provide contextual guidance during typical maintenance tasks through the use of ontological queries and reasoning services.