Today, at 6.30 pm, Valentina Presutti is giving a webinar about “Automation of Knowledge Extraction and Ontology Learning”.
Context: Building & maintaining knowledge bases & ontologies is hard work and could use some automated help.
Perception: Various parts of AI, such as NLP and machine learning are developing rapidly and could offer help.
Motivation: bring together various researchers to discuss the issues and state of the art.
Agenda and Speakers
- Introduction: Gary Berg-Cross
- Estevam Hruschka (Associate Professor at Federal University of Sao Carlos DC-UFSCar & adjunct Professor at Carnegie Mellon University) will speak on work growing out of Never-Ending Language Learning (NELL).
Abstract: Never-Ending Learning approach for Populating and Extending an Ontology
Abstract: NELL (Never-Ending Language Learner) is a computer system that runs 24/7, forever, learning to read the web and, as a result, populating and extending its own ontology. NELL has two main tasks to be performed each day: i) extract (read) more facts from the web, and integrate these into its growing ontology; and ii) learn to read better than yesterday, enabling it to go back to the text it read yesterday, and today extract more facts, more accurately. This system has been running 24 hours/day for over seven years now. The result so far is an ontology having +100 million interconnected instances (e.g., servedWith(coffee, applePie), isA(applePie, bakedGood)), that NELL is considering at different levels of confidence, along with hundreds of thousands of learned phrasings, morphological features, and web page structures that NELL uses to extract beliefs from the web. The main motivation for building NELL is based on the belief that we will never really understand machine learning until we can build machines that learn many different things, over years, and become better learners over time.
- Valentina Presutti, (Semantic Technology Laboratory of the Institute of Italian National Research Council (CNR)), Semantic Web machine reading with FRED
Abstract: A machine reader is a tool able to transform natural language text to formal structured knowledge so as the latter can be interpreted by machines, according to a shared semantics. FRED is a machine reader for the semantic web: its output is a RDF/OWL graph, whose design is based on frame semantics. Nevertheless, FRED’s graph are domain and task independent making the tool suitable to be used as a semantic middleware for domain- or task- specific applications. To serve this purpose, it is available both as REST service and as Python library. In this talk I will give an overview of the method and principles behind FRED’s implementation.
- Alessandro Oltramari (Research Scientist at Bosch) “From machines that learn to machines that know: the role of ontologies in machine intelligence”
Abstract Deep learning networks are getting better and better at recognizing visual and acoustic information, from pictures of cats to human faces, from gunshots to voices. But to make sense of a movie scene, or to interpret speech, machines need world models, namely semantic structures capable of connecting the dots from perception to knowledge. In my presentation I will talk about the state of the art of the art in the integration between machine learning algorithms and ontologies. I will also briefly illustrate how this integration is nowadays a key requirement for enabling intelligent services in the Internet of Things.
Virtual Meeting Details
Date: Wed., 8-March-2017 Start Time: 9:30am PST / 12:30pm EST / 6:30pm CEST / 5:30pm BST / 1630 UTC
Expected Call Duration: ~90 minutes