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Doc2KG: Transforming Document Repositories to Knowledge Graphs

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TLDR
In this article , the authors introduce Doc2KG (Document-to-Knowledge-Graph), an intelligent framework that handles both creation and real-time updating of a knowledge graph, while also exploiting domain-specific ontology standards.
Abstract
Document Management Systems (DMS) are used for decades to store large amounts of information in textual form. Their technology paradigm is based on storing vast quantities of textual information enriched with metadata to support searchability. However, this exhibits limitations as it treats textual information as black box and is based exclusively on user-created metadata, a process that suffers from quality and completeness shortcomings. The use of knowledge graphs in DMS can substantially improve searchability, providing the ability to link data and enabling semantic searching. Recent approaches focus on either creating knowledge graphs from document collections or updating existing ones. In this paper, we introduce Doc2KG (Document-to-Knowledge-Graph), an intelligent framework that handles both creation and real-time updating of a knowledge graph, while also exploiting domain-specific ontology standards. We use DIAVGEIA (clarity), an award winning Greek open government portal, as our case-study and discuss new capabilities for the portal by implementing Doc2KG.

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Citations
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Recommendation of Healthcare Services Based on an Embedded User Profile Model

TL;DR: The experiment results show that the PCE-CF framework proposed in this paper can improve the recommendation algorithm's efficiency and have higher feasibility in personalized service recommendations.
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Integration and Open Access System Based on Semantic Technologies

TL;DR: The aim of this work is the development of an information system that makes it possible to publish and share with society the scientific production generated in the university environment, promoting its dissemination and thus contributing to the knowledge society.
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A Semantically Enhanced Knowledge Discovery Method for Knowledge Graph Based on Adjacency Fuzzy Predicates Reasoning

TL;DR: In this article , a new interpretable reasoning schema is proposed by introducing fuzzy theory, which focuses on analyzing the fuzzy semantic between related entities in a knowledge graph, by annotating fuzzy semantic features of adjacency predicates, a novel semantic reasoning model is designed to realize the fuzzy semantics extension over knowledge graph.
References
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TL;DR: This article proposed BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora.
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Entity Linking meets Word Sense Disambiguation: A Unified Approach

TL;DR: Babelfy is presented, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations.
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Quality assessment for Linked Data: A Survey

TL;DR: A systematic review of approaches for assessing the quality of Linked Data, which unify and formalize commonly used terminologies across papers related to data quality and provides a comprehensive list of 18 quality dimensions and 69 metrics.
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A Survey on Deep Learning for Named Entity Recognition

TL;DR: A comprehensive review on existing deep learning techniques for NER is provided in this paper, where the authors systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder.
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Open data policies, their implementation and impact: A framework for comparison

TL;DR: A framework for comparing open data policies is developed and it is suggested that current policies are rather inward looking and can be improved by collaborating with other organizations, focusing on the impact of the policy, stimulating the use of open data and looking at the need to create a culture in which publicizing data is incorporated in daily working processes.
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