LD Connect: About

LD Connect black and green  logo with Connect Data, Accelerate Research underneath

In collaboration with the research community, we are enriching and connecting human- and machine-readable data in more meaningful ways to contribute to an increased understanding of published research. Our datasets include, for example, metadata of journal articles and book chapters, authors, affiliations, countries, volumes, issues, series, pre-press and publication dates, ISSNs, DOIs accessibility, keywords, pages, and abstracts.

We use a custom vocabulary and web standards while describing our data in order to make that data even more discoverable, accessible, linkable, and interoperable with other datasets. Affiliations are geocoded and authors as well as affiliations are disambiguated using our co-reference resolution script. With the help of machine learning techniques, the data conversion pipeline keeps on improving as more data are added.

LD Connect, all components to access linked data (white background)

The unsiloing of data leads to improved retrieval, accessibility, reusability, and interoperability. Structured data can be searched, shared, reused, data mined, and linked to other data sources.

By enriching and fostering the interlinking of data, contextual relationships among authors, institutions, and research areas can be made visible. Geocoding and spatial information add another layer of discovery.

Tools that visualize the data for human consumption, as well as tools for deep knowledge mining, include a visual linked Data Browser, SPARQL, and our Semantic Search interface.

By freely offering our datasets in machine-readable form to third parties and semantic tools that reveal important connections, we hope to help empower the scientific research community and contribute in a meaningful way to scientific progress.


As a technologically forward-looking organization, we support continuous investment in innovation and technology, collaborating with research teams across the globe.

LD Connect was developed in collaboration with STKO Lab at UC Santa Barbara, CA, USA, and for the co-reference resolution with DaSe Lab at Wright State University in Dayton, OH, USA, and Kansas State University in Manhattan, KS, USA.

Further insights into how the LD Connect embeddings were developed by the team are available:

Combining Text Embedding and Knowledge Graph Embedding Techniques for Academic Search Engines, by Gengchen Mai, Krzysztof Janowicz, and Bo Yan, in: Proceedings of SemDeep-4 Workshop co-located with ISWC 2018, Oct. 8–12, 2018, Monterey, CA, USA; link: ceur-ws.org/Vol-2241.

Powerful and Intelligent Search Capabilities
Searching LD Connect goes beyond queries that just use exact keywords. AI-based semantic vectors, generated by embeddings, enable far more sophisticated searches. They crawl the data and automatically look for all semantically similar terms – terms that the user may not have even considered – as well as all truncations, resulting in more accurate and comprehensive results.

For example, searching for "Artificial Intelligence" would also retrieve data based on all full text and would include variations like "AI" or related terms like "Machine Learning" or "Model Based Reasoning."

    Visit LD Connect to download and connect our data or embeddings to your research output or application.

    Further details about  background of LD Connect can be found in the following news announcements:

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