Nicola Zeni

Requirements for tools for ambiguity identification and measurement in natural language requirements specifications

This paper proposes a two-step approach to identifying ambiguities in natural language (NL) requirements specifications (RSs). In the first step, a tool would apply a set of ambiguity measures to a RS in order to identify potentially ambiguous sentences in the RS. In the second step, another tool would show what specifically is potentially ambiguous about each potentially ambiguous sentence. The final decision of ambiguity remains with the human users of the tools. The paper describes several requirements-identification experiments with several small NL RSs using four prototypes of the first tool based on linguistic instruments and resources of different complexity and a manual mock-up of the second tool.

GaiusT: supporting the extraction of rights and obligations for regulatory compliance

Abstract Ensuring compliance of software systems with government regulations, policies, and laws is a complex problem. Generally speaking, solutions to the problem first identify rights and obligations defined in the law and then treat these as requirements for the system under design. This work examines the challenge of developing tool support for extracting such requirements from legal documents. To address this challenge, we have developed a tool called GaiusT. The tool is founded on a framework for textual semantic annotation. It semiautomatically generates elements of requirements models, including actors, rights, and obligations. We present the complexities of annotating prescriptive text, the architecture of GaiusT, and the process by which annotation is accomplished. We also present experimental results from two case studies to illustrate the application of the tool and its effectiveness relative to manual efforts. The first case study is based on the US Health Insurance Portability and Accountability Act, while the second analyzes the Italian accessibility law for information technology instruments.

Text mining through semi automatic semantic annotation

The Web is the greatest information source in human history. Unfortunately, mining knowledge out of this source is a laborious and error-prone task. Many researchers believe that a solution to the problem can be founded on semantic annotations that need to be inserted in web-based documents and guide information extraction and knowledge mining. In this paper, we further elaborate a tool-supported process for semantic annotation of documents based on techniques and technologies traditionally used in software analysis and reverse engineering for large-scale legacy code bases. The outcomes of the paper include an experimental evaluation framework and empirical results based on two case studies adopted from the Tourism sector. The conclusions suggest that our approach can facilitate the semi-automatic annotation of large document bases.

Applying software analysis technology to lightweight semantic markup of document text

Software analysis techniques, and in particular software “design recovery”, have been highly successful at both technical and businesslevel semantic markup of large scale software systems written in a wide variety of programming languages, and in particular have proven e.cient and scalable in assisting the resolution of the “year 2000” problem for billions of lines of legacy source code. In this work we describe a first experiment in applying the same technical solutions and tools that have proven so successful in software markup to the more general problem of semantic markup of text documents. In this early report we describe our adaptation of the software analysis techniques, propose a general domain-independent architecture for semantic markup using them, and demonstrate its feasibility in a limited but realistic domain of application by comparison with both raw and tool-assisted human semantic markers.

Annotating Accommodation Advertisements Using CERNO

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.

A lightweight approach to semantic annotation of research papers

This paper presents a novel application of a semantic annotation system, named Cerno, to analyze research publications in electronic format. Specifically, we address the problem of providing automatic support for authors who need to deal with large volumes of research documents. To this end, we have developed Biblio, a user-friendly tool based on Cerno. The tool directs the users attention to the most important elements of the papers and provides assistance by generating automatically a list of references and an annotated bibliography given a collection of published research articles. The tool performance has been evaluated on a set of papers and preliminary evaluation results are promising. The backend of Biblio uses a standard relational database to store the results.

Experimenting with Linguistic Tools for Conceptual Modelling: Quality of the Models and Critical Features

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. 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 give insights on the state of the art of NL-based Computer Aided Software Engineering (CASE) tools and allow identifying important guidelines to improve their performance, highlighting which of the linguistic tasks are more critical to effectively support conceptual modelling.

RFID-Based Action Tracking for Measuring the Impact of Cultural Events on Tourism

Organization of cultural events in a tourist destination often demands large investments. For this reason, authorities, tourism managers and operators need data to support their decisions, define their strategies and, in general terms, they are interested in measuring the economic impact of such events on tourism. In this paper, we propose a lightweight framework that can be applied to gather data on the places tourists visit during an event. The framework is based on an action tracking system that exploits RFID technologies on an existing city card circuit. The main choices necessary to design a system that allows to satisfy technical, organizational and budget constraints are described referring to its application to two important recent festivals — Trento FilmFestival and the Festival of Economics — characterized by very different content, history, and targets.

Annotating Regulations Using Cerno: An Application to Italian Documents - Extended Abstract

The increasing complexity of software systems and growing demand for regulations compliance require effective methods and tools to support requirements analysts activities. Internationalization of information systems due to both economics and Web based architectures call for the application of regulations written in different languages. Thus far existing approaches for extracting rights and obligations have concentrated on English documents. In this paper, we describe the results of the application of Cerno, a lightweight framework for semantic annotation, to legal documents written in Italian. In addition, we investigate critical issues for semantic annotation tools in a different cultural and environmental context. Results obtained, while preliminary, allow us to quantify the effort needed to port tools based on Cerno and give some insight on directions of future development of a multilingual system to support semantic annotation of regulations not only in different domains, but also written in different languages.

Building Large Models of Law with NómosT

Laws and regulations impact the design of software systems, as they introduce new requirements and constrain existing ones. The analysis of a software system and the degree to which it complies with applicable laws can be greatly facilitated by models of applicable laws. However, laws are inherently voluminous, often consisting of hundreds of pages of text, and so are their models, consisting of thousands of concepts and relationships. This paper studies the possibility of building models of law semi-automatically by using the NomosT tool. Specifically, we present the NomosT architecture and the process by which a user constructs a model of law semi-automatically, by first annotating the text of a law and then generating from it a model. We then evaluate the performance of the tool relative to building a model of a piece of law manually. In addition, we offer statistics on the quality of the final output that suggest that tool supported generation of models of law reduces substantially human effort without affecting the quality of the output.