Knowledge extraction

About: Knowledge extraction is a research topic. Over the lifetime, 20251 publications have been published within this topic receiving 413401 citations.

Named entity recognition in biomedical texts using an HMM model

Abstract: Although there exists a huge number of biomedical texts online, there is a lack of tools good enough to help people get information or knowledge from them. Named entity Recognition (NER) becomes very important for further processing like information retrieval, information extraction and knowledge discovery. We introduce a Hidden Markov Model (HMM) for NER, with a word similarity-based smoothing. Our experiment shows that the word similarity-based smoothing can improve the performance by using huge unlabeled data. While many systems have laboriously hand-coded rules for all kinds of word features, we show that word similarity is a potential method to automatically get word formation, prefix, suffix and abbreviation information automatically from biomedical texts, as well as useful word distribution information.

Text Mining, knowledge extraction from unstructured textual data

Abstract: In the general context of Knowledge Discovery, specific techniques, called Text Mining techniques, are necessary to extract information from unstructured textual data. The extracted information can then be used for the classification of the content of large textual bases. In this paper, we present two examples of information that can be automatically extracted from text collections: probabilistic associations of key-words and prototypical document instances. The Natural Language Processing (NLP) tools necessary for such extractions are also presented.

An Incremental Approach for Inducing Knowledge from Dynamic Information Systems

Abstract: Knowledge in an information system evolves with its dynamical environment. A new concept of interesting knowledge based on both accuracy and coverage is defined in this paper for dynamic information systems. An incremental model and approach as well as its algorithm for inducing interesting knowledge are proposed when the object set varies over time. A case study validates the feasibility of the proposed method.

A knowledge management approach for managing uncertainty in manufacturing

Abstract: Purpose – This paper proposes a knowledge management approach for managing uncertainty in manufacturing enterprises.Design/methodology/approach – The knowledge management approach consists of a knowledge‐enriched manufacturing system, which is modelled using SIMAN simulation language and programmed using Visual Basic applications. A knowledge‐based planning module and an execution platform are simulated so that signals could be transferred, and configuration to the planned parameters could be made, in order to minimise variations due to uncertainties. A reference architecture and intelligent agent are created to store tacit knowledge and create explicit knowledge, respectively.Findings – Manufacturing enterprises should use both tacit knowledge about uncertainties and buffering and dampening techniques, simultaneously with the explicit knowledge that is generated by the intelligent agent, for managing uncertainty. The design of the knowledge management approach enables easy integration with material requi...

Adaptive knowledge management system for vehicle trend monitoring, health management and preventive maintenance

Abstract: A method and an Adaptive Knowledge Management is provided. The Adaptive Knowledge Management System is used for assisting a user with decision making by providing real-time, on-line automated recommendations for actions in a monitored vehicle troubleshooting, performance trend monitoring, health management and preemptive maintenance domain diagnostics and prognostics. The system creates a Structured Knowledge Repository, constructed from models, historical data, and heuristics for organizing a model domain knowledge. It uses a plurality of Analytical and Machine Learning tools for capturing knowledge from data sources and populating cells of the Structured Knowledge Repository. A Mixed-Initiative Planning module is used for interpreting operation goals for the monitored vehicle and utilizing the Structure Knowledge Repository for developing recommendations for user decision making. A plurality of Mixed-initiative Decision Support tools use the feedback from the Mixed-Initiative Planning module and query the Structured Knowledge Repository for incorporating the extracted knowledge and information into outputs dealing with current issues and contingencies.