Presentations
Ariadne, an innovative approach to scalable semantic embedding (video)
The OCLC Research project Ariadne Semantic Embedding is a demonstration of a practical solution to support libraries in this field. Watch this webinar to see the potential of this scalable semantic embedding method for other applications such as entity disambiguation, citation recommendation, clustering and collection exploration.
Topics: Data Science, Semantic Embedding, Works in Progress
OCLC Research Mini-symposium Part 3 - Deep-interactions with Open Collections (video)
As the volume of digitized heritage collections continues to grow, memory institutions are challenged to making this open content discoverable and usable across repositories. At this mini-symposium in Leiden, guests learn about research & development work done in the area of digital image interoperability (IIIF), corpus-building and deep interactions with open collections by OCLC, and latest developments at Europeana and the Global Digitised Dataset Network
Topics: Open Access, Semantic Embedding
Fast and Discriminative Semantic Embedding
Gothenburg, Sweden
We present a novel, effective and efficient method for term and document embedding method. Our experiments show it outperforms state-of-the-art methods in terms of the STS benchmark and subject prediction when trained on the same datasets, while at the same time being computationally cheaper by orders of magnitude.
Topics: Semantic Embedding
An Innovative Approach to Scalable Semantic Embedding
Pittsburgh, Pennsylvania, USA
Semantic search, in addition to keyword based search, is a desirable feature for many digital library systems. Even in the largely structured library data world, there is still a lot of tacit information locked in the free-text fields. Embedding words and texts in compact, semantically meaningful vector spaces allows for computable semantic similarity/relatedness which would make search more intelligent.
Topics: Semantic Embedding
Subject Prediction Using Semantic Embedding
The Hague (Netherlands)
Koopman and Wang describe semantic embedding and their work on the Ariadne random projection algorithm that attempts to predict the right mix of subject headings.
Topics: Semantic Embedding