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A Knowledge Graph Entity Disambiguation Method Based on Entity-Relationship Embedding and Graph Structure Embedding.

Computational intelligence and neuroscience
The purpose of knowledge graph entity disambiguation is to match the ambiguous entities to the corresponding entities in the knowledge graph. Current entity ambiguity elimination methods usually use the context information of the entity and its attri...

BERTtoCNN: Similarity-preserving enhanced knowledge distillation for stance detection.

PloS one
In recent years, text sentiment analysis has attracted wide attention, and promoted the rise and development of stance detection research. The purpose of stance detection is to determine the author's stance (favor or against) towards a specific targe...

Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System.

Journal of medical Internet research
BACKGROUND: Genealogical information, such as that found in family trees, is imperative for biomedical research such as disease heritability and risk prediction. Researchers have used policyholder and their dependent information in medical claims dat...

Disease ontologies for knowledge graphs.

BMC bioinformatics
BACKGROUND: Data integration to build a biomedical knowledge graph is a challenging task. There are multiple disease ontologies used in data sources and publications, each having its hierarchy. A common task is to map between ontologies, find disease...

The perceptions of medical physicists towards relevance and impact of artificial intelligence.

Physical and engineering sciences in medicine
Artificial intelligence (AI) is an innovative tool with the potential to impact medical physicists' clinical practices, research, and the profession. The relevance of AI and its impact on the clinical practice and routine of professionals in medical ...

HiAM: A Hierarchical Attention based Model for knowledge graph multi-hop reasoning.

Neural networks : the official journal of the International Neural Network Society
Learning to reason in large-scale knowledge graphs has attracted much attention from research communities recently. This paper targets a practical task of multi-hop reasoning in knowledge graphs, which can be applied in various downstream tasks such ...

Design publicity of black box algorithms: a support to the epistemic and ethical justifications of medical AI systems.

Journal of medical ethics
In their article 'Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI', Durán and Jongsma discuss the epistemic and ethical challenges raised by black box algorithms in medical practice. The opacity ...

The role of foreign technologies and R&D in innovation processes within catching-up CEE countries.

PloS one
Prior research showed that there is a growing consensus among researchers, which point out a key role of external knowledge sources such as external R&D and technologies in enhancing firms´ innovation. However, firms´ from catching-up Central and Eas...

The man and the machine: Do children learn from and transmit tool-use knowledge acquired from a robot in ways that are comparable to a human model?

Journal of experimental child psychology
Robots are an increasingly prevalent presence in children's lives. However, little is known about the ways in which children learn from robots and whether they do so in the same way as they learn from humans. To investigate this, we adapted a previou...

Enhancement of Target-Oriented Opinion Words Extraction with Multiview-Trained Machine Reading Comprehension Model.

Computational intelligence and neuroscience
Target-oriented opinion words extraction (TOWE) seeks to identify opinion expressions oriented to a specific target, and it is a crucial step toward fine-grained opinion mining. Recent neural networks have achieved significant success in this task by...