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The paper proposes a knowledge-based framework for mobile autonomous robots. It exploits data annotation for semantic-based context description. High-level event/situation detection and action decision are performed through a semantic matchmaking approach, supporting approximate matches and relevance-based ranking. The framework was fully implemented in a prototype built with off-the-shelf components,...
Discrimination discovery from data consists of designing data mining methods for the actual discovery of discriminatory situations and practices hidden in a large amount of historical decision records. Approaches based on classification rule mining consider items at a flat concept level, with no exploitation of background knowledge on the hierarchical and inter-relational structure of domains. On...
Concept maps are resources for the representation and construction of knowledge. They allow the showing, through concepts and relationships, how knowledge about a subject is organized. Technological advances have boosted the development of technological approaches that help the automatic construction of a map, in order to facilitate and provide the benefits of that resource more broadly. Because of...
Organizations have an increasing need to adapt faster their Information Systems to technical, functional and legal changes. One way proposed in the literature is to make a deep process analysis in order to have a better comprehension of the business process (BP) and adapt it to its new context. In this paper, we propose a meta model for a BP contextualization solution. The solution links a BP with...
The main problem of rule-based information extraction technique is that the extraction rules tend to be specifically designed for specific information or document structure; hence it cannot be directly used in another without some proper modifications. Semi-structured documents like tables present another challenge to information extraction; since there are no standards on how to design it, the structure...
User annotation of images has become a popular solution for image classification and retrieval. This paper reports a machine learning approach in taxonomy induction that applies image annotations involved in different levels of meaning making from the participants. Results of the study indicate the effectiveness of the method. The study suggests that the discussed machine learning procedures can be...
In recent years homeland security is becoming increasingly sensitive to threats posed by the tactics of subversive groups or individuals with malicious intent. Networked groups and organizations leverage various means of communication, ranging from simple phone calls to more sophisticated forms of collaborations. Such data provide a rich collection of evidence from which to infer relationships of...
Medical Information Retrieval (IR) aims to extract relevant medical information from the web, patients' records, electronic books, research articles etc. However, users are generally unfamiliar with medical terms and find difficulties expressing their needs. One interesting solution is to integrate fuzzy ontologies in order to achieve semantic interoperability and offer a way to handle vague and imprecise...
This paper presents a context-aware ontological approach applied to an alarm management system. The goal is to easier the work of the plant's operators using supervisory systems and process control. Being part of a Supervisory Control and Data Acquisition (SCADA), alarm management systems produces and stores a big data set. Using proper analysis techniques in order to produce information and knowledge...
One of the biggest challenges in Big Data is to exploit value from large volumes of variable and changing data. For this, one must focus on analyzing the data in these Big Data sources and classify the data items according to a domain model (e.g. an ontology). To automatically classify unstructured text documents according to an ontology, a hierarchical multi-label classification process called Semantic...
As sensors become more affordable, sensor networks are increasingly deployed to monitor diverse environments. However, these sensor network deployments often utilize different standards for communication and data storage. As a result, it is challenging to build large-scale pervasive systems able to find, query, and analyze information across a diverse set of sensor networks. Additionally, aggregating...
Nowadays, many devices and sensors we use in everydaylife are connected to the internet. We call this the IoT (Internet ofThings). With more things being used, it is getting more difficultfor users to use them efficiently. Without overcoming thischallenge, IoT cannot be vitalized. To solve this problem, manyvoice agent systems, including Apple Siri and Amazon Alexa, areextending their service domains...
Taxonomy learning is an important task for knowledge acquisition, sharing, and classification as well as application development and utilization in various domains. To reduce human effort to build a taxonomy from scratch and improve the quality of the learned taxonomy, we propose a new taxonomy learning approach, named TaxoFinder. TaxoFinder takes three steps to automatically build a taxonomy. First,...
The research project we present in this paper concerns an ontology-based recommender for teacher training and support. An important issue in Computer-Assisted Language Learning education is the complexity of exploiting technology in order to enhance language teaching. The integration of technology into language education is a rather complex achievement, which implies the ability to understand the...
This paper builds upon the BWEC1 (Business for Women in Women of Emerging Country) research project to improve the socio-economic situation of handicraft women. In this project our principal task is to build data warehouse schema from handicraft women social network. For that, we follow a semi-supervised clustering-based methodology. In this paper, we propose the adaptation of a semi-supervised hierarchical...
The competition inherent to globalisation has led enterprises to gather in nests of specialised business providers with the purpose of building better applications and provide more complete solutions. This, added to the improvements on the Information and Communications Technologies (ICT), led to a paradigm shift from product-centrism to service-centrism and to the need to communicate and interoperate...
Process mining algorithms use event logs to learn and reason about processes by technically coupling event history data and process models. During the execution of a learning process, several events occur which are of interest and/or necessary for completing and achieving a learning goal. The work in this paper describes a Semantic Process Mining approach directed towards automated learning. The proposed...
Query Expansion is an important component for information retrieval systems. It makes possible the reformulation of the initial user query by adding new terms. In this paper, we propose a new approach for term selection in the relevance feedback process. This approach, based on Rocchio formula, is an adaptation to the XML information retrieval context. It can resolve two major problems specific to...
NELL (Never Ending Language Learning system) is the first system to practice the Never-Ending Machine Learning paradigm techniques. It has an inactive component to continually extend its KB: OntExt. Its main idea is to identify and add to the KB new relations which are frequently asserted in huge text data. Co-occurrence matrices are used to structure the normalized values of co-occurrence between...
With increasing amount of information (video, text) being available today, it has become non-trivial to develop techniques to categorize documents into contextually meaningful classes. The information as available in the documents is composed of sequence of events termed as patterns. It is evident to know the important trends as observed from patterns that are emerging over a specific time period...
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