Knowledge Graphs and Natural Language Processing. The Year of the…
Pinterest gets with the knowledge graph program. Facebook releases a new dataset for conversational Reasoning over Knowledge Graphs. Connected Data London announces its own program, rich in leaders and innovators.
And as always, new knowledge graph and graph database releases, research, use cases, and definitions. A double bill summertime newsletter edition, making your knowledge graph living easy.
Pinterest adopted Semantic Web technologies to create a knowledge graph that aims to represent the vast amount of content and users, to help both content recommendation and ads targeting. A mixed team from Stanford and Pinterest present the engineering of an OWL ontology—the Pinterest Taxonomy—that forms the core of Pinterest’s knowledge graph, the Pinterest Taste Graph. Facebook AI researchers study a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph to introduce engaging and contextually diverse entities and attributes in conversational agents. As part of this, they created a corpus called OpenDialKG.
OpenDialKG was created using the Freebase knowledge graphs and 15K conversations between human agents. The conversations were recorded, referenced Freebase entities were identified, and the paths corresponding to the reference of entities in the flow of the conversation added to OpenDialKG.
The DialKG Walker model was developed, that learns the symbolic transitions of dialog contexts as structured traversals over KG, and predicts natural entities to introduce given previous dialog contexts via a novel domain-agnostic, attention-based graph path decoder.
The OpenDialKG research was published in ACL 2019, the annual meeting of the Association for Computational Linguistics. ACL 2019 was enormous — 2900 submissions, 660 accepted papers, more than 3000 registered attendees, and four workshops with about 400 attendees. About 30 papers out of 660, or 5%, involved Knowledge Graphs.
Michael Galkin from Fraunhofer IAIS outlined some major areas where KGs were most represented and described some very promising papers. Galkin notes research in Dialogue Systems over Knowledge Graphs. Natural Language Generation of Knowledge Graph facts. Complex Question Answering over Knowledge Graphs. Named Entity Recognition and Relation Linking over KGs. KG Embeddings & Graph Representations.
Bonus track: natural language interfaces for databases, including both relational databases and RDF graph databases
Connected Data London, the leading event for those who use the relationships, meaning and context in Data to achieve great things, has announced its lineup for 2019. The biggest and most visionary event to feature the rich array of technologies which make up the Connected Data landscape is taking place in London on October 3 and 4, 2019.
Building on a tradition dating back long before analysts like Gartner proclaimed knowledge graphs a key technology of the 2020s, the conference focuses on Knowledge Graphs and Graph Databases, AI and Machine Learning, Linked Data and Semantic Technology.
What is a knowledge graph? Mike Bergman from Cognonto Corp. embarks on a journey through various knowledge graph definitions found in literature. Bergman notes that if one looks up what is a knowledge graph, one finds there are some 99 references on Google, plus 22 academic papers. Bergman presents and compares various definitions of knowledge graph. Alan Morrison from PwC goes one step further, elaborating on the difference between knowledge graphs and a graph databases. You would think this should be clear, but we’ve seen a good deal of confusion, ranging from users to “influencers”. The clarity Morrison offers is much needed.
