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Found 9 papers, 3 papers with code
Knowledge Graph Question Answering for Materials Science (KGQA4MAT): Developing Natural Language Interface for Metal-Organic Frameworks Knowledge Graph (MOF-KG)
To evaluate the benchmark, we have developed a systematic approach for utilizing ChatGPT to translate natural language questions into formal KG queries.
Repurposing Knowledge Graph Embeddings for Triple Representation via Weak Supervision
The majority of knowledge graph embedding techniques treat entities and predicates as separate embedding matrices, using aggregation functions to build a representation of the input triple.
Exploring Wasserstein Distance across Concept Embeddings for Ontology Matching
Measuring the distance between ontological elements is fundamental for ontology matching.
Building Open Knowledge Graph for Metal-Organic Frameworks (MOF-KG): Challenges and Case Studies
Metal-Organic Frameworks (MOFs) are a class of modular, porous crystalline materials that have great potential to revolutionize applications such as gas storage, molecular separations, chemical sensing, catalysis, and drug delivery.
Clustering and Network Analysis for the Embedding Spaces of Sentences and Sub-Sentences
Sentence embedding methods offer a powerful approach for working with short textual constructs or sequences of words.
Price change prediction of ultra high frequency financial data based on temporal convolutional network
Through in-depth analysis of ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are constructed in this paper.
A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs
Neural embedding approaches have become a staple in the fields of computer vision, natural language processing, and more recently, graph analytics.
A Comparative Study on Structural and Semantic Properties of Sentence Embeddings
Although a number of techniques have been proposed in the literature for embedding both sentences and knowledge graphs, little is known about the structural and semantic properties of these embedding spaces in terms of relation extraction.
Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge
This paper presents a novel approach to the task of automatically inferring the most probable diagnosis from a given clinical narrative.
