What’s new for KGs in Graph ML?

Your guide to the KG-related research in NLP, December edition

NeurIPS is a major venue covering a wide range of ML & AI topics. Of course, there is something interesting for Graph ML aficionados and knowledge graph connoisseurs 🧐. Tune in to find out!

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This year, NeurIPS had 1900 accepted papers 😳 and 100+ among them are on graphs. Plus take into account several prominent workshops like KR2ML, DiffGeo4ML, and LMCA. Be sure to check their proceedings as such workshop papers are likely to appear at future venues like ICLR, ICML, or ACL. …

What’s new for KGs in NLP?

Your guide to the KG-related research in NLP, November edition.

Knowledge Graphs continue to drive NLP forward 🏎! You can’t miss the event as major as EMNLP 2020, so let’s dive in and see what’s new in our ocean 🌊.

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I had the luck to attend EMNLP in its online form which was well organized: Zoom Q&A sessions, poster sessions and socials in gather.town, paper-specific and general channels in Rocket.Chat. This year, the EMNLP program consists of 754 EMNLP papers, plus 520 Findings of EMNLP papers are accessible in the proceedings. …

GNNs Beyond Triples

Hyper-relational KGs encode much more knowledge than triple KGs. We adopt recent advances in Graph ML and propose a GNN encoder for such graphs along with a new benchmark.

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Image by Author.

Knowledge graphs (KGs) are a cornerstone of modern NLP and AI applications — recent works include Question Answering, Entity & Relation Linking, Language Modeling, Information Extraction, and even playing text RPGs with Reinforcement Learning. Furthermore, KGs are already widely adopted in the industry, e.g., a line-up of works from the recent Knowledge Graph Conference (KGC)

Triples vs The World

Traditionally, KGs are encoded as <subject, predicate, object> (RDF) triples, and many publicly available KGs like DBpedia and YAGO initially followed this paradigm backed by expressive logical formalisms (remember the times when DL referred to Description Logics ? 👵) and standards like RDF and OWL.

State of the Art Mid 2020

This post commemorates the first anniversary of the series where we examine advancements in NLP and Graph ML powered by knowledge graphs! 🎂 1️⃣
The feedback of the audience drives me to continue, so fasten your seatbelts (and maybe brew some ☕️): in this episode, we are looking at the KG-related ACL 2020 proceedings!

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ACL 2020 went fully virtual this year and I can’t imagine how hard was it for the chairs to organize such an enormous event online catering for multiple time zones and 700+ accepted papers. …

👋 Hello, I hope you are all doing well during the lockdown.
ICLR 2020 went fully virtual, and here is a fully virtual article (well, unless you print it) about knowledge graph-related papers at the conference 🚀

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This time our agenda includes:

  1. Neural Reasoning for Complex QA with KGs
  2. KG-augmented Language Models
  3. KG Embeddings: Temporal and Inductive Inference
  4. Entity Matching with GNNs
  5. Bonus: KGs in Text RPGs!
  6. Conclusions

Neural Reasoning for Complex QA with KGs

It’s great to see more research and more datasets on complex QA and reasoning tasks. Whereas last year we saw a surge of multi-hop reading comprehension datasets (e.g.,

The first major AI event of 2020 is already here! Hope you had a nice holiday break 🎄, or happy New Year if your scientific calendar starts with a conference (which means NY comes from NYC). AAAI 2020 brought us a new line-up of Knowledge Graph-related papers, in other words, AAA-class papers from AAAI 😉 Okay, enough feeble jokes, let’s get started!

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This year AAAI got 1591 accepted papers among which about 140 are graph-related 👀. Additionally, there was a strong Workshop and Tutorial presence:

If you still had any doubts — it’s time to admit. Machine Learning on Graphs becomes a first-class citizen at AI conferences while being not that mysterious as you might have imagined 🧙. Let’s check out the goodies brought by NeurIPS 2019 and co-located events!

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Graphs were well represented at the conference. The main venue alone had more than 100 graph-related publications, and even more were available at three workshops: Graph Representation Learning (about 100 more papers), Knowledge Representation & Reasoning Meets Machine Learning (KR2ML) (about 50 papers), Conversational AI. Nice 👀 So we’ll consider all events jointly.

In this…

Here is the second part of the review of knowledge graph related papers from EMNLP 2019. In this part, we’ll talk about Question Answering over Knowledge Graphs, NLG from KGs, Commonsense reasoning with KGs, and some old school Named Entity/Relation Recognition & Linking. Let’s start 🚀

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Part I

  1. Augmented Language Models
  2. Dialogue Systems and Conversational AI
  3. Building Knowledge Graphs from Text (Open KGs)
  4. Knowledge Graph Embeddings
  5. Conclusions

Part II (👈 you are here)

  1. Question Answering over Knowledge Graphs
  2. Natural Language Generation from KGs
  3. Commonsense Reasoning with KGs
  4. Named Entity Recognition and Relation Linking

Question Answering over Knowledge Graphs

Question Answering (QA) enjoys a growing traction from the NLP communinty. Machine Reading Comprehension (MRC)…

Hey there! 👋 The review post of the papers from ACL 2019 on knowledge graphs (KGs) in NLP was well-received so I thought maybe it would be beneficial for the community to look through the proceedings of EMNLP 2019 for the latest state of the art in applying knowledge graphs in NLP. Let’s start!

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In the past projects aimed at building infotainment goal-oriented dialog systems we observed a series of unexpectedly funny responses 🍄. Having enjoyed a pun, our investigation led to more serious conclusions. In this article, I’d like to share our findings and discuss some recently delivered goodies from IJCAI 2019 which took place August 10–16th in Macao.
(and if you are a mushroom lover — look no further, I have some!)

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A typical case of the Mushroom Effect

The Mushroom Effect

A bit of a background: our goal was to build a conversational platform able to communicate with a user on some selected points of interest in Berlin, e.g., Berlin Hauptbahnhof…

Michael Galkin

Research Scientist @ TU Dresden & Fraunhofer IAIS in Dresden, working on merging Knowledge Graphs with Conversational AI

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