1,009 research outputs found
An Extended Semantic Interoperability Model for Distributed Electronic Health Record Based on Fuzzy Ontology Semantics
Semantic interoperability of distributed electronic health record (EHR) systems is a crucial problem for querying EHR and machine learning projects. The main contribution of this paper is to propose and implement a fuzzy ontology-based semantic interoperability framework for distributed EHR systems. First, a separate standard ontology is created for each input source. Second, a unified ontology is created that merges the previously created ontologies. However, this crisp ontology is not able to answer vague or uncertain queries. We thirdly extend the integrated crisp ontology into a fuzzy ontology by using a standard methodology and fuzzy logic to handle this limitation. The used dataset includes identified data of 100 patients. The resulting fuzzy ontology includes 27 class, 58 properties, 43 fuzzy data types, 451 instances, 8376 axioms, 5232 logical axioms, 1216 declarative axioms, 113 annotation axioms, and 3204 data property assertions. The resulting ontology is tested using real data from the MIMIC-III intensive care unit dataset and real archetypes from openEHR. This fuzzy ontology-based system helps physicians accurately query any required data about patients from distributed locations using near-natural language queries. Domain specialists validated the accuracy and correctness of the obtained resultsThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2021R1A2B5B02002599)S
Information Extraction based on Named Entity for Tourism Corpus
Tourism information is scattered around nowadays. To search for the
information, it is usually time consuming to browse through the results from
search engine, select and view the details of each accommodation. In this
paper, we present a methodology to extract particular information from full
text returned from the search engine to facilitate the users. Then, the users
can specifically look to the desired relevant information. The approach can be
used for the same task in other domains. The main steps are 1) building
training data and 2) building recognition model. First, the tourism data is
gathered and the vocabularies are built. The raw corpus is used to train for
creating vocabulary embedding. Also, it is used for creating annotated data.
The process of creating named entity annotation is presented. Then, the
recognition model of a given entity type can be built. From the experiments,
given hotel description, the model can extract the desired entity,i.e, name,
location, facility. The extracted data can further be stored as a structured
information, e.g., in the ontology format, for future querying and inference.
The model for automatic named entity identification, based on machine learning,
yields the error ranging 8%-25%.Comment: 6 pages, 9 figure
Discourse-Aware Graph Networks for Textual Logical Reasoning
Textual logical reasoning, especially question-answering (QA) tasks with
logical reasoning, requires awareness of particular logical structures. The
passage-level logical relations represent entailment or contradiction between
propositional units (e.g., a concluding sentence). However, such structures are
unexplored as current QA systems focus on entity-based relations. In this work,
we propose logic structural-constraint modeling to solve the logical reasoning
QA and introduce discourse-aware graph networks (DAGNs). The networks first
construct logic graphs leveraging in-line discourse connectives and generic
logic theories, then learn logic representations by end-to-end evolving the
logic relations with an edge-reasoning mechanism and updating the graph
features. This pipeline is applied to a general encoder, whose fundamental
features are joined with the high-level logic features for answer prediction.
Experiments on three textual logical reasoning datasets demonstrate the
reasonability of the logical structures built in DAGNs and the effectiveness of
the learned logic features. Moreover, zero-shot transfer results show the
features' generality to unseen logical texts
Extracting Temporal and Causal Relations between Events
Structured information resulting from temporal information processing is
crucial for a variety of natural language processing tasks, for instance to
generate timeline summarization of events from news documents, or to answer
temporal/causal-related questions about some events. In this thesis we present
a framework for an integrated temporal and causal relation extraction system.
We first develop a robust extraction component for each type of relations, i.e.
temporal order and causality. We then combine the two extraction components
into an integrated relation extraction system, CATENA---CAusal and Temporal
relation Extraction from NAtural language texts---, by utilizing the
presumption about event precedence in causality, that causing events must
happened BEFORE resulting events. Several resources and techniques to improve
our relation extraction systems are also discussed, including word embeddings
and training data expansion. Finally, we report our adaptation efforts of
temporal information processing for languages other than English, namely
Italian and Indonesian.Comment: PhD Thesi
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge
graphs that represent structural relations between entities have become an
increasingly popular research direction towards cognition and human-level
intelligence. In this survey, we provide a comprehensive review of knowledge
graph covering overall research topics about 1) knowledge graph representation
learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph,
and 4) knowledge-aware applications, and summarize recent breakthroughs and
perspective directions to facilitate future research. We propose a full-view
categorization and new taxonomies on these topics. Knowledge graph embedding is
organized from four aspects of representation space, scoring function, encoding
models, and auxiliary information. For knowledge acquisition, especially
knowledge graph completion, embedding methods, path inference, and logical rule
reasoning, are reviewed. We further explore several emerging topics, including
meta relational learning, commonsense reasoning, and temporal knowledge graphs.
To facilitate future research on knowledge graphs, we also provide a curated
collection of datasets and open-source libraries on different tasks. In the
end, we have a thorough outlook on several promising research directions
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
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