Open Knowledge Accessing Method in IoT-based Hospital Information System for Medical Record Enrichment

For a medical treatment with IoT-based facilities, physicians always have to pay much more attentions to the raw medical records of target patients instead of directly making medical advice, conclusions or diagnosis from their experiences. Because the medical records in IoT-based Hospital Information System (HIS) are dispersedly obtained from distributed devices such as tablet computer, personal digital assistant, automated analyzer and other medical devices, they are raw, simple, weak-content and massive. Such medical records cannot be used for further analyzing and decision supporting due to that they are collected in a weak-semantic manner. In this paper, we propose a novel approach to enrich IoT-based medical records by linking them with the knowledge in Linked Open Data (LOD). A case study is conducted on a real-world IoT-based HIS system in association with our approach, the experimental results show that medical records in the local HIS system are significantly enriched and useful for healthcare analysis and decision making, and further demonstrate the feasibility and effectiveness of our approach for knowledge accessing

Interlinking RDF data in different languages

lesnikova2014bInternational audienceno abstrac

Entity Linking in Low-Annotation Data Settings

Recent advances in natural language processing have focused on applying and adapting large pretrained language models to specific tasks. These models, such as BERT (Devlin et al., 2019) and BART (Lewis et al., 2020a), are pretrained on massive amounts of unlabeled text across a variety of domains. The impact of these pretrained models is visible in the task of entity linking, where a mention of an entity in unstructured text is matched to the relevant entry in a knowledge base. State-of-the-art linkers, such as Wu et al. (2020) and De Cao et al. (2021), leverage pretrained models as a foundation for their systems. However, these models are also trained on large amounts of annotated data, which is crucial to their performance. Often these large datasets consist of domains that are easily annotated, such as Wikipedia or newswire text. However, tailoring NLP tools to a narrow variety of textual domains severely restricts their use in the real world. Many other domains, such as medicine or law, do not have large amounts of entity linking annotations available. Entity linking, which serves to bridge the gap between massive unstructured amounts of text and structured repositories of knowledge, is equally crucial in these domains. Yet tools trained on newswire or Wikipedia annotations are unlikely to be well-suited for identifying medical conditions mentioned in clinical notes. As most annotation efforts focus on English, similar challenges can be noted in building systems for non-English text. There is often a relatively small amount of annotated data in these domains. With this being the case, looking to other types of domain-specific data, such as unannotated text or highly-curated structured knowledge bases, is often required. In these settings, it is crucial to translate lessons taken from tools tailored for high-annotation domains into algorithms that are suited for low-annotation domains. This requires both leveraging broader types of data and understanding the unique challenges present in each domain

Interlinking English and Chinese RDF data sets using machine translation

lesnikova2014aInternational audienceData interlinking is a difficult task particularly in a multilingual environment like the Web. In this paper, we evaluate the suitability of a Machine Translation approach to interlink RDF resources described in English and Chinese languages. We represent resources as text documents, and a similarity between documents is taken for similarity between resources. Documents are represented as vectors using two weighting schemes, then cosine similarity is computed. The experiment demonstrates that TF*IDF with a minimum amount of preprocessing steps can bring high results