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The proliferation of Web 2.0 technologies and the increasing use of computer-mediated communication resulted in a new form of written text, termed microtext. This poses new challenges to natural language processing tools which are usually designed for well-written text. This paper proposes a phonetic-based framework for normalizing microtext to plain English and, hence, improve the classification...
Natural Language Processing and Machine Learning techniques can be used to automatically identify, extract and manipulate textual clinical data. Many of these methods are strongly dependent on annotated corpora that are very difficult to find in the clinical domain, especially for the Brazilian Portuguese language. The annotation task is expensive and time-consuming; hence, it is important to provide...
In this article we address the problem of expanding the set of papers that researchers encounter when conducting bibliographic research on their scientific work. Using classical search engines or recommender systems in digital libraries, some interesting and relevant articles could be missed if they do not contain the same search key-phrases that the researcher is aware of. We propose a novel model...
Traditional Chinese Medicine (TCM) is a discipline typically characterized as complicated information science [1], which represents the distinctive thought (e.g. theories drawn from clinic should be applied in clinic), compared with the modern medicine. Symptoms are basic clinical concepts in TCM electronic health record, which are plain text language. Nevertheless, TCM concepts are inherently characterized...
The Unified Medical Language System (UMLS) is an important terminological system. By the policy of its curators, each concept of the UMLS should be assigned the most specific Semantic Types (STs) in the UMLS Semantic Network (SN). Hence, the Semantic Types of most UMLS concepts are assigned at or near the bottom (leaves) of the UMLS Semantic Network. While most ST assignments are correct, some errors...
External knowledge sources are commonly used in processing large amounts of data. Large external knowledge sources, such as ontologies, often contain hundreds of thousands of concepts and relationships, making comprehension and navigation difficult. Abstraction networks enhance the usability and comprehensibility of these resources by providing a higher level of abstraction. In this paper, we develop...
The importance of functional status information (FSI) has become increasingly evident in recent years [1, 2]. However, implementation, application, and normalization of FSI in health care and Electronic Health Records (EHRs) have been largely underexplored. The World Health Organization's International Classification of Functioning, Disability and Health (ICF) [3] is considered to be the international...
According to the concept system of acupuncture and moxibustion subject, the semantic types of acupoint and acupuncture method are supplemented and adjusted, and their semantic relationships are discussed about and studied, so as to realize the networking of related concepts and terminologies of acupoint and acupuncture methods. We design the classification framework, and construct acupuncture terminology...
In open software development environment, a large number of feature requests with mixed quality are often posted by stakeholders and usually managed in issue tracking systems. Thoroughly understanding and analyzing the real intents that feature requests imply is a labor-intensive and challenging task. In this paper, we introduce an approach to understand feature requests automatically. We generate...
Natural Language Processing (NLP) is one of the principal areas of artificial intelligence. It can be argued that the use of ontologies increases the efficiency of natural language processing. However, most ontologies are built manually and require a lot of work. Thus, the problem of automated ontology replenishment is very relevant. One approach is to develop methods for replenishing ontologies using...
Health and social care professionals are under increasing pressure to assimilate the ever-growing volume of data from case notes and electronic medical records. In this paper, we propose and evaluate with domain experts a cognitive system for patient-centric care that leverages and combines natural language processing, semantics, and learning from users over time to support care professionals making...
We investigate multi-agent epistemic modal logic with common knowledge modalities for groups of agents and obtain van Benthem style model-theoretic characterisations, in terms of bisimulation invariance of classical first-order logic over the non-elementary classes of (finite or arbitrary) common knowledge Kripke frames. The fixpoint character of common knowledge modalities and the rôle that reachability...
Software artifacts, such as requirements, design, source code, documentation, and safety-related artifacts are typically expressed using domain-specific terminology. Automated tools which attempt to analyze software artifacts in order to perform tasks such as trace retrieval and maintenance, domain analysis, program comprehension, or to service natural language queries, need to understand the vocabulary...
Domain terminology recognition and extraction is the primary work for construction of domain knowledge graph. Traditional method is tedious, and time-consuming, as well as low accuracy. This paper presents an automatic domain feature extraction method based on the Domain Feature Vectors (DFVs). Experimental results demonstrate that our approach is effectiveness and accuracy.
Replaced by ISO/IEEE 11073-10101-2004. Within the context of the ISO/IEEE 11073 family of standards for point-of-care (POC) medical device communication (MCD), this standard provides the nomenclature that supports both the domain information model and service model components of the standards family, as well as the semantic content exchanged with medical devices. The nomenclature is specialized for...
In recent days healthcare domains need a efficient storage and retrieval systems to provide a effective medical services to the health seekers. But there is a vocabulary gap in understanding the medical terminologies due to ambiguity. So, the existing systems need a intelligent medical storage using some natural language processing. Users post their queries in free text so it will result in complexity...
Classification of diseases and their related terms are important data resources for basic medical research. However, disease terms in different terminological databases are largely developed independent of each other and the mapping relationships between them are not complete. The purpose of this paper is to propose similarity-based disease terminology mapping methods to map disease terms with same...
Current e-learning systems are database-driven; this makes it hard for users to find information in a direct way using the domain terminology they are familiar with from daily use. In order to alleviate this problem we propose the use of a learning ontology that uses an intuitive naming convention and combines learning concepts with user profiles and learning object metadata (LOM) for the easy access...
In knowledge society organizations as well as individuals publish relevant data about themselves in the Web. However, still most Web content is only suitable for human consumption. It is not machine-understandable. In this paper, we have restricted ourselves on publishing machine-understandable competence-oriented data in the Web. Such data allows the development of new and more strengthen solutions...
Patent retrieval is important for technology survey and knowledge protection. Its aim is to search as many patent documents relevant to the patent document query as possible, which is considered as a recall-oriented task. However, existing methods suffer from the term mismatch problem caused by the frequent use of many non-standard technical terminologies in patents. To address the issue, we present...
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