Ricardo Gacitua

A flexible framework to experiment with ontology learning techniques

Ontology learning refers to extracting conceptual knowledge from several sources and building an ontology from scratch, enriching, or adapting an existing ontology. It uses methods from a diverse spectrum of fields such as natural language processing, artificial intelligence and machine learning. However, a crucial challenging issue is to quantitatively evaluate the usefulness and accuracy of both techniques and combinations of techniques, when applied to ontology learning. It is an interesting problem because there are no published comparative studies. We are developing a flexible framework for ontology learning from text which provides a cyclical process that involves the successive application of various NLP techniques and learning algorithms for concept extraction and ontology modelling. The framework provides support to evaluate the usefulness and accuracy of different techniques and possible combinations of techniques into specific processes, to deal with the above challenge. We show our frameworks efficacy as a workbench for testing and evaluating concept identification. Our initial experiment supports our assumption about the usefulness of our approach.

The case for dumb requirements engineering tools

[Context and Motivation] This paper notes the advanced state of the natural language (NL) processing art and considers four broad categories of tools for processing NL requirements documents. These tools are used in a variety of scenarios. The strength of a tool for a NL processing task is measured by its recall and precision. [Question/Problem] In some scenarios, for some tasks, any tool with less than 100% recall is not helpful and the user may be better off doing the task entirely manually. [Principal Ideas/Results] The paper suggests that perhaps a dumb tool doing an identifiable part of such a task may be better than an intelligent tool trying but failing in unidentifiable ways to do the entire task. [Contribution] Perhaps a new direction is needed in research for RE tools.

On the Effectiveness of Abstraction Identification in Requirements Engineering

The identification of abstractions, i.e. terms that have a particular significance in a given domain, and such that they can indirectly characterize the most salient features of the document in which they appear, has often been recognized as a useful tool in the analysis of domain descriptions and requirements documents in software development. In this paper we propose a new technique for the identification of single- and multi-word abstractions named Relevance driven abstraction identification (RAI) and a corresponding tool implementation, present an experiment comparing the effectiveness of our technique with human judgement and with a different technique proposed in the literature, and discuss a number of ways in which the abstractions so identified can be used to good profit in requirements engineering.

Making Tacit Requirements Explicit

The importance of tacit knowledge in Requirements Engineering (RE) is widely acknowledged. While valuable work has developed techniques to expose sources of tacit knowledge during requirements elicitation, such techniques are not universally applied and tacit knowledge, continues to negatively affect the quality of the requirements. In this position paper we present a brief review and interpretation of the literature on tacit knowledge that, we believe, is useful for RE. We describe a number of techniques that offer analysts the means to reason about the effect of tacit knowledge and improve the quality of requirements and their management.

Relevance-based abstraction identification: technique and evaluation

When first approaching an unfamiliar domain or requirements document, it is often useful to get a quick grasp of what the essential concepts and entities in the domain are. This process is called abstraction identification, where the word abstraction refers to an entity or concept that has a particular significance in the domain. Abstraction identification has been proposed and evaluated as a useful technique in requirements engineering (RE). In this paper, we propose a new technique for automated abstraction identification called relevance-based abstraction identification (RAI), and evaluate its performance—in multiple configurations and through two refinements—compared to other tools and techniques proposed in the literature, where we find that RAI significantly outperforms previous techniques. We present an experiment measuring the effectiveness of RAI compared to human judgement, and discuss how RAI could be used to good effect in requirements engineering.

Unpacking Tacit Knowledge for Requirements Engineering

The use of tacit knowledge is a common feature in everyday communication. It allows people to communicate effectively without forcing them to make everything tediously and painstakingly explicit, provided they all share a common understanding of whatever is not made explicit. If this latter criterion does not hold, confusion and misunderstanding will ensue. Tacit knowledge is also commonplace in requirements where it also affords economy of expression. However, the use of tacit knowledge also suffers from the same risk of misunder-standing, with the associated problems of anticipating where it has the potential for confusion, and of unraveling where it has played an actual role in misunder-standing. Thus the effective communication of requirements knowledge (whether verbally, through a document or some other medium) requires an understanding of what knowledge is and isn’t (necessarily) held in common. This is very hard to get right as people from different professional and cultural backgrounds are typically involved. At its worst, tacit requirements knowledge may lead to software that fails to satisfy the customer’s requirements. In this chapter we review the diverse views of tacit knowledge discussed in the literature from a wide range of disci-plines, reflect on their commonalities and differences, and propose a conceptual framework for requirements engineering that characterizes the different facets of tacit knowledge that distinguish the different views. We then identify methodolog-ical and technical challenges for future research on the role of tacit knowledge in requirements engineering.

Ensemble Methods for Ontology Learning - An Empirical Experiment to Evaluate Combinations of Concept Acquisition Techniques

Most approaches to ontology learning combine techniques from different areas (hybrid approaches) to increase the efficiency of the ontology learning process. However, the results from the ontology learning process do not fully satisfy the users at present. An important problem is that there is a lack of quantitative and comparative data about the efficiency of techniques and technique combinations applied to ontology learning. In this paper we present a quantitative comparison of technique combinations for concept extraction and a software system (OntoLancs) to support the evaluation of techniques. By applying OntoLancs, users are able to assist the process of building ontologies by semi- automatically acquiring concepts from large-scale domain document collections and experiment with different combinations of knowledge acquisition techniques to refine and organize domain concepts into a taxonomy. Quantitative and comparative studies about the performance of several techniques and user experiences indicate the applicability and usefulness of our approach.

Concept mapping as a means of requirements tracing

Requirements documents often describe the system on different abstraction levels. This results in the fact that the same issues may be described in different documents and with different vocabulary. For analysts who are new to the application domain, this poses a major orientation problem, as they cannot link different concepts or documents with each other. In the presented paper, we propose an approach to map concepts extracted from different documents to each other. This, in turn, allows us to find related passages in different documents, even though the documents represent different levels of abstraction. Practical applicability of the approach was proven in a case study with real-world requirements documents.

A Flexible Framework To Experiment With Ontology Learning Techniques

Ontology learning refers to extracting conceptual knowledge from several sources and building an ontology from scratch, enriching, or adapting an existing ontology. It uses methods from a diverse spectrum of fields such as Natural Language Processing, Artificial Intelligence and Machine learning. However, a crucial challenging issue is to quantitatively evaluate the usefulness and accuracy of both techniques and combinations of techniques, when applied to ontology learning. It is an interesting problem because there are no published comparative studies. We are developing a flexible framework for ontology learning from text which provides a cyclical process that involves the successive application of various NLP techniques and learning algorithms for concept extraction and ontology modelling. The framework provides support to evaluate the usefulness and accuracy of different techniques and possible combinations of techniques into specific processes, to deal with the above challenge. We show our framework’s efficacy as a workbench for testing and evaluating concept identification. Our initial experiment supports our assumption about the usefulness of our approach.

A collaborative workflow for building ontologies: A case study in the biomedical field

Much medical knowledge is contained within available literature, such as clinical guidelines and protocols. Recently, an interest has been developed in automatic content extraction to construct ontologies of this knowledge to make it more widely available. With groups of domain experts distributed geographically, and the growing amount of medical literature, an important challenge is to develop collaborative workflows to support ways for domain experts to contribute in the ontology learning process. This paper presents a collaborative workflow for ontology learning based on coupling an Ontology Learning Tool (OntoLancs) with and Ontology engineer (Protégé) to provide semi-automatic support for text mining and a collaborative tool to model formal ontologies. The work presented in this paper was evaluated with a case study on a Clinical Practice Guideline of Diabetic Retinopathy. The major benefits of coupling OntoLancs with Protégé are: a) a higher level of automation in the creation of domain ontologies and models, and b) strengthened communication and information exchange among domain experts that are physically distributed. Validations of user experiences indicate the applicability of our approach.