From engineering models to knowledge graph : delivering new insights into models

Essential information on the early stages of a mission design is contained in Engineering Models. Yet, these models are often uneasy to visualise, query, let alone compare. This study demonstrates how Knowledge Graphs can overcome these data silos, interconnect information, provide a big-picture perspective, and infer new knowledge that would have remained hidden otherwise. Following the migration of CubeSats Engineering Models to a Knowledge Graph, two case studies are explored. The first case study illustrates how graph inference can derive implicit knowledge from existing explicit concepts. In the second case study, a Natural Language Processing layer is adjoined to the Knowledge Graph to enhances the analysis of textual content. The Natural Language Processing layer relies on the document embedding method doc2v

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

Ontología y Procesamiento de Lenguaje Natural

At present, the convergence of several areas of knowledge has led to the design and implementation of ICT systems that support the integration of heterogeneous tools, such as artificial intelligence (AI), statistics and databases (BD), among others. Ontologies in computing are included in the world of AI and refer to formal representations of an area of knowledge or domain. The discipline that is in charge of the study and construction of tools to accelerate the process of creation of ontologies from the natural language is the ontological engineering. In this paper, we propose a knowledge management model based on the clinical histories of patients (HC) in Panama, based on information extraction (EI), natural language processing (PLN) and the development of a domain ontology.Keywords: Knowledge, information extraction, ontology, automatic population of ontologies, natural language processing

From Text to Knowledge with Graphs: modelling, querying and exploiting textual content

This paper highlights the challenges, current trends, and open issues related to the representation, querying and analytics of content extracted from texts. The internet contains vast text-based information on various subjects, including commercial documents, medical records, scientific experiments, engineering tests, and events that impact urban and natural environments. Extracting knowledge from this text involves understanding the nuances of natural language and accurately representing the content without losing information. This allows knowledge to be accessed, inferred, or discovered. To achieve this, combining results from various fields, such as linguistics, natural language processing, knowledge representation, data storage, querying, and analytics, is necessary. The vision in this paper is that graphs can be a well-suited text content representation once annotated and the right querying and analytics techniques are applied. This paper discusses this hypothesis from the perspective of linguistics, natural language processing, graph models and databases and artificial intelligence provided by the panellists of the DOING session in the MADICS Symposium 2022

Empirical study of automated dictionary construction for information extraction in three domains

ManuscriptA primary goal of natural language processing researchers is to develop a knowledge-based natural language processing (NLP) system that is portable across domains. However, most knowledge-based NLP systems rely on a domain-specific dictionary of concepts, which represents a substantial knowledge-engineering bottleneck. We have developed a system called AutoSlog that addresses the knowledge-engineering bottleneck for a task called information extraction. AutoSlog automatically creates domain-specific dictionaries for information extraction, given an appropriate training corpus. We have used AutoSlog to create a dictionary of extraction patterns for terrorism, which achieved 98% of the performance of a handcrafted dictionary that required approximately 1500 person-hours to build. In this paper, we describe experiments with AutoSlog in two additional domains: joint ventures and microelectronics. We compare the performance of AutoSlog across the three domains, discuss the lessons learned about the generality of this approach, and present results from two experiments which demonstrate that novice users can generate effective dictionaries using AutoSlog

Natural language processing methods for knowledge management - Applying document clustering for fast search and grouping of engineering documents

Product development companies collect data in form of Engineering Change Requests for logged design issues, tests, and product iterations. These documents are rich in unstructured data (e.g. free text). Previous research affirms that product developers find that current IT systems lack capabilities to accurately retrieve relevant documents with unstructured data. In this research, we demonstrate a method using Natural Language Processing and document clustering algorithms to find structurally or contextually related documents from databases containing Engineering Change Request documents. The aim is to radically decrease the time needed to effectively search for related engineering documents, organize search results, and create labeled clusters from these documents by utilizing Natural Language Processing algorithms. A domain knowledge expert at the case company evaluated the results and confirmed that the algorithms we applied managed to find relevant document clusters given the queries tested

Mobile Phone Text Processing and Question-Answering

Mobile phone text messaging between mobile users and information services is a growing area of Information Systems. Users may require the service to provide an answer to queries, or may, in wikistyle, want to contribute to the service by texting in some information within the service’s domain of discourse. Given the volume of such messaging it is essential to do the processing through an automated service. Further, in the case of repeated use of the service, the quality of such a response has the potential to benefit from a dynamic user profile that the service can build up from previous texts of the same user. This project will investigate the potential for creating such intelligent mobile phone services and aims to produce a computational model to enable their efficient implementation. To make the project feasible, the scope of the automated service is considered to lie within a limited domain of, for example, information about entertainment within a specific town centre. The project will assume the existence of a model of objects within the domain of discourse, hence allowing the analysis of texts within the context of a user model and a domain model. Hence, the project will involve the subject areas of natural language processing, language engineering, machine learning, knowledge extraction, and ontological engineering