Using Semantic Annotation for Mining Privacy and Security Requirements from European Union Directives

The increasing complexity of software systems and growing demand for regulations compliance require effective methods and tools to support requirements analysts activities. In order to facilitate alignment of software system requirements and regulations, systematic methods and tools automating regulations analysis must be developed. This work explores applicability of the semantic annotation tool Cerno to mining of rights and obligations from European privacy directives

NLP-Based Requirements Modeling: Experiments on the Quality of the models

Conceptual models are used in a variety of areas within Computer Science, including Software Engineering, Databases and AI. A major bottleneck in broadening their applicability is the time it takes to build a conceptual model for a new application. Not surprisingly, a variety of tools and techniques have been proposed for reusing conceptual models, e.g. ontologies, or for building them semi-automatically from natural language (NL) descriptions. What has been left largely unexplored is the impact of such tools on the quality of the models that are being created. This paper presents the results of three experiments designed to assess the extent to which a Natural-Language Processing (NLP) tool improves the quality of conceptual models, specifically object-oriented ones. Our main experimental hypothesis is that the quality of a domain class model is higher if its development is supported by a NLP system. The tool used for the experiment – named NL-OOPS – extracts classes and associations from a knowledge base realized by a deep semantic analysis of a sample text. Specifically, NL-OOPS produces class models at different levels of detail by exploiting class hierarchies in the knowledge base of a NLP system and marks ambiguities in the text. In our experiments, we had groups working with and without the tool, and then compared and evaluated the final class models they produced. The results of the experiments – the first on this topic – give insights on the state of the art of linguistics-based Computer Aided Software Engineering (CASE) tools and allow identifying important guidelines to improve their performance. In particular it was possible to highlight which of the linguistic tasks are more critical to effectively support conceptual modelling

Ambiguity Identification and Measurement in Natural Language Texts

Text ambiguity is one of the most interesting phenomenon in human communication and a difficult problem in Natural Language Processing (NLP). Identification of text ambiguities is an important task for evaluating the quality of text and uncovering its vulnerable points. There exist several types of ambiguity. In the present work we review and compare different approaches to ambiguity identification task. We also propose our own approach to this problem. Moreover, we present the prototype of a tool for ambiguity identification and measurement in natural language text. The tool is intended to support the process of writing high quality documents

Annotating Accommodation Advertisements using

There has been great interest in applying Semantic Web technologies to the tourism sector ever since Tim Berners-Lee introduced his vision. Unfortunately, there is a major obstacle in realizing such applications: tourist (or other) information on the Web has to be semantically annotated, and this happens to be a very time- and resource-consuming process. In this work we present the application of a lightweight automated approach for the annotation of accommodation advertisements. The annotation tool, called Cerno, allows for annotation of text according to a predefined conceptual schema. Resulting annotations are stored in a database, allowing users to quickly find the best match to personal requirements. To evaluate our framework, we have conducted a series of experiments that support the efficacy of our proposal with respect to annotation quality and fulfilment of user information needs

ABSTRACT

Security, privacy and governance are increasingly the focus of government regulations in the U.S., Europe and elsewhere. This trend has created a “regulation compliance ” problem, whereby companies and developers are required to ensure that their software complies with relevant regulations, either through design or reengineering. We previously proposed a methodology for extracting stakeholder requirements, called rights and obligations, from regulations. In this paper, we examine the challenges of developing tool support for this process. We apply the Cerno framework for textual semantic annotation to propose a tool for semi-automatic semantic annotation of concepts that constitute sources of requirements

A Lightweight Approach to Semantic Tagging

Semantic Annotation is a challenging research direction in the area of Semantic Web. Turning the web into a Semantic Web implies widespread semantic annotation of documents. But it is still need to be investigated further in order to make annotation process more efficient, automating it as far as possible. The approach described in this paper aims at semi-automatic semantic tagging by application of linguistic lightweight methods for extraction of relevant concepts and by defining appropriate semantic models