| Session
|
A look across the industry, Part 2
|
| Duration
|
1 hour
|
| Date/Time
|
25 Oct 2023 16:00 GMT
|
| 9:00am PDT/12:00pm EDT
|
| 4:00pm GMT/5:00pm CST
|
| Convener
|
Andrea Westerinen and Mike Bennett
|
Agenda
- Evren Sirin, Stardog CTO and lead for their new Voicebox offering
- Title: How Stardog Uses AI and How AI Uses Stardog
- Abstract: Stardog’s AI strategy can be summarized as hybrid, applied, in-house, and user-focused. "Hybrid" derives from understanding that data management systems should provide crisp, provably correct, trusted answers to questions, but also benefits from considering fuzzy, not-terribly-wrong answers. "Applied and in-house" is focused on using foundational LLMs, NLP, or AI infrastructures to address the challenges of data modeling, data mapping, query generation, rule creation and more. "User-focused" pivots around capabilities such as question answering without any need to write queries, using ordinary language to manage a data lifecycle, and semi-supervised integration of an enterprise's structured, semi-structured, and unstructured data. The overall goal is universal self-service analytics. A significant step towards the goal is Stardog's Voicebox which leverages LLM to build, manage, and query knowledge graphs using ordinary language.
- Yuan He, Key contributor to DeepOnto, a package for ontology engineering with deep learning
- Title: DeepOnto: A Python Package for Ontology Engineering with Deep Learning and Language Models
- Abstract: Integrating deep learning techniques, particularly language models (LMs), with knowledge representations like ontologies has raised widespread attention, urging the need for a platform that supports both paradigms. However, deep learning frameworks like PyTorch and Tensorflow are predominantly developed for Python programming, while widely-used ontology APIs, such as the OWL API and Jena, are primarily Java-based. To facilitate seamless integration of these frameworks and APIs, we present DeepOnto, a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognized and reliable OWL API, encapsulating its fundamental features in a more “Pythonic” manner and extending its capabilities to incorporate other essential components including reasoning, verbalization, normalization, projection, taxonomy, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs.
Conference Call Information
- Date: Wednesday, 25 October 2023
- Start Time: 9:00am PDT / 12:00pm EDT / 6:00pm CEST / 5:00pm BST / 1600 UTC
- Expected Call Duration: 1 hour
- Video Conference URL: https://bit.ly/48lM0Ik
- Conference ID: 876 3045 3240
- Passcode: 464312
The unabbreviated URL is:
https://us02web.zoom.us/j/87630453240?pwd=YVYvZHRpelVqSkM5QlJ4aGJrbmZzQT09
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