Acquiring Legal Ontologies from Domain-specific Texts

The paper reports on methodology and preliminary results of a case study in automatically extracting ontological knowledge from Italian legislative texts in the environmental domain. We use a fully-implemented ontology learning system (T2K) that includes a battery of tools for Natural Language Processing (NLP), statistical text analysis and machine language learning. Tools are dynamically integrated to provide an incremental representation of the content of vast repositories of unstructured documents. Evaluated results, however preliminary, are very encouraging, showing the great potential of NLP-powered incremental systems like T2K for accurate large-scale semi?automatic extraction of legal ontologies

Ontology learning from Italian legal texts

The paper reports on the methodology and preliminary results of a case study in automatically extracting ontological knowledge from Italian legislative texts. We use a fully-implemented ontology learning system (T2K) that includes a battery of tools for Natural Language Processing (NLP), statistical text analysis and machine language learning. Tools are dynamically integrated to provide an incremental representation of the content of vast repositories of unstructured documents. Evaluated results, however preliminary, show the great potential of NLP-powered incremental systems like T2K for accurate large-scale semi-automatic extraction of legal ontologies

Natural Language Requirements Processing: A 4D Vision

The future evolution of the application of natural language processing technologies in requirements engineering can be viewed from four dimensions: discipline, dynamism, domain knowledge, and datasets

A Terminological Survey on the Titles of the Seventh Framework Programme (FP7)

This paper focuses on the automatic extraction of domain-specific knowledge from the European Commission projects of the 7th Framework Programme, hereinafter referred as FP7. The study is divided in three parts: the first part introduces the work starting from the building up of a corpus containing the titles of European Projects of the whole FP7 in order to obtain a relevant terminological sample for the different domains; the second describes software and methods while the third part focuses on the evaluation of results. Finally, we conclude by suggesting possible directions for further development of a comparison between terminological extraction from FP7 and FP5/FP6

Functional technology foresight. A novel methodology to identify emerging technologies

The speed and complexity of the technology evolution faced by modern societies need new approaches to the analysis and understanding of the world. Indeed, an exclusive focus on technological goals can miss to recognize all the stakeholders of a technology and address real user needs; moreover, on the one hand low signals are becoming more and more important in fast evolving markets, on the other hand the excess of hype, fashions, or vested interests sometimes deeply alter indicators. However, the so called Big Data promise to be a huge low cost set of valuable information, available and affordable to all (SMEs included). But, analyzing them is not trivial especially if we deal with academic papers and patents. To tackle these issues, the present paper proposes to apply a powerful methodological tool called Functional Analysis to the Technology Foresight process. Actually the rigorous study of the functions, that an artefact should perform to satisfy the user needs, provides a universal and thus unifying point of view, which is able to correlate the user perspective on the product with its technical features. Functional reasoning has been applied to (i) detect possible patterns of development, spotting missing elements and highlighting strengths as well as potential sources of failure; (ii) to enhance traditional bibliometric tools such as the analysis of S-curves and (iii), integrated with a natural language processing analysis toolchain, tailored for patent documents, to identify emerging technologies. The paper describes the functional approach to technology foresight activity, presents how to integrate it with text mining algorithms and experts’ domain knowledge, and finally discusses its benefits in the context of Technology Foresight also from an economic point of view, showing that oresight is affordable also for Small and Medium Enterprises

Proceedings of LOAIT '07 : II Workshop on Legal Ontologies and Artificial Intelligence Techniques

Proceedings of the 2nd Workshop on Legal Ontologies and Artificial Intelligence Techniques June 4th, 2007, Stanford Universit

Assessing ICD-9-CM and ICPC-2 Use in Primary Care. An Italian Case Study

Controlled vocabularies and standardized coding systems play a fundamental role in the healthcare domain. The International Classification of Diseases (ICD) is one of the most widely used classification systems for clinical problems and procedures. In Italy the 9th revision of the standard is used and recommended in primary care for encoding prescription documents. This paper describes a statistical and terminological study to assess ICD-9-CM use in primary care and its comparison to the International Classification of Primary Care (ICPC), specifically designed for primary care. The study has been conducted by analyzing the clinical records of about 199,000 patients provided by a set of 166 General Practitioners (GPs) in different Italian areas. The analysis has been based on several techniques for detecting coding practice and errors, like natural language processing and text-similarity comparison. Results showed that the selected GPs do not fully exploit the diseases and procedures descriptive capabilities of ICD-9-CM due to its complexity. Furthermore, compared to ICPC-2, it resulted less feasible in the primary care setting, particularly for the high granularity of the structure and for the lack of reasons for encounters

Antennas and Electromagnetics Research via Natural Language Processing.

Advanced techniques for performing natural language processing (NLP) are being utilised to devise a pioneering methodology for collecting and analysing data derived from scientific literature. Despite significant advancements in automated database generation and analysis within the domains of material chemistry and physics, the implementation of NLP techniques in the realms of metamaterial discovery, antenna design, and wireless communications remains at its early stages. This thesis proposes several novel approaches to advance research in material science. Firstly, an NLP method has been developed to automatically extract keywords from large-scale unstructured texts in the area of metamaterial research. This enables the uncovering of trends and relationships between keywords, facilitating the establishment of future research directions. Additionally, a trained neural network model based on the encoder-decoder Long Short-Term Memory (LSTM) architecture has been developed to predict future research directions and provide insights into the influence of metamaterials research. This model lays the groundwork for developing a research roadmap of metamaterials. Furthermore, a novel weighting system has been designed to evaluate article attributes in antenna and propagation research, enabling more accurate assessments of impact of each scientific publication. This approach goes beyond conventional numeric metrics to produce more meaningful predictions. Secondly, a framework has been proposed to leverage text summarisation, one of the primary NLP tasks, to enhance the quality of scientific reviews. It has been applied to review recent development of antennas and propagation for body-centric wireless communications, and the validation has been made available for comparison with well-referenced datasets for text summarisation. Lastly, the effectiveness of automated database building in the domain of tunable materials and their properties has been presented. The collected database will use as an input for training a surrogate machine learning model in an iterative active learning cycle. This model will be utilised to facilitate high-throughput material processing, with the ultimate goal of discovering novel materials exhibiting high tunability. The approaches proposed in this thesis will help to accelerate the discovery of new materials and enhance their applications in antennas, which has the potential to transform electromagnetic material research