"SYGMA: System for Generalizable Modular Question Answering Over Knowledge Bases", tested on #DBPedia and #Wikidata + a new Temporal QA benchmarkdataset based on Wikidata.(Neelam et al, 2021)data: https://t.co/0tkY9sjA9Zpaper: https://t.co/rZDw4bW56Q pic.twitter.com/XqFwp2def2— WikiResearch (@WikiResearch) October 6, 2021

ArangoDB, the commercial open source company behind the graph database system of the same name, has raised $27.8 million.

Memgraph 2.0 is here! We are finally source available and ready to tame your streams.

Our paper titled "A Survey of #RDF Stores & #SPARQL Engines for Querying Knowledge Graphs" has been accepted to #VLDB Journal. A survey of over 120 RDF stores and #KnowledgeGraphs. https://t.co/SsFroOOBI5 @aidhog @NgongaAxel @akswgroup @DiceResearch pic.twitter.com/o4fiwG1VJq— Muhammad Saleem (@saleem_muhamad) October 3, 2021

“Take a crash course on GraphQL, a tool for building APIs that's seeing rapid adoption. And Dgraph Cloud is the complete GraphQL platform to get you going."

Program Transfer and Ontology Awareness for Semantic Parsing in KBQA [Knowledge Base Question Answering] https://t.co/4UHsmhhkRz pic.twitter.com/iTyYwse9Jf— Aaron Bradley (@aaranged) October 13, 2021

We present principled approaches to train and deploy dyadic neural embedding models at the billion scale, focusing our investigation on the application of semantic product search. When training a...

This particular article is a discussion about a recommendation to a given standard, that of SPARQL 1.1. None of this has been implemented yet, and as such rep…

"Detecting Cross-Language Plagiarism using Open Knowledge Graphs" a new multilingual retrieval model for cross-language plagiarism, representing documents as entity vectors obtained from #Wikidata.(Stegmüller et al, #JCDL2022)https://t.co/5Uyalf4o7o pic.twitter.com/FppQzfVd97— WikiResearch (@WikiResearch) October 13, 2021

Check out how David Meza from NASA combined knowledge graph and graph algorithms to find hidden skills within the organization.

"No Need to Know Everything! Efficiently Augmenting Language Models With External Knowledge" Instead of packing all knowledge in the model, the system provides external Wiki knowledge and trains the model to use that source.(Kaur et al 2021)https://t.co/U3VcZn0XyY@sbhatia_ pic.twitter.com/XNPZj5Neys— WikiResearch (@WikiResearch) October 15, 2021

Knowledge Graph-enhanced Sampling for Conversational Recommender System https://t.co/LfD8ixZT7K pic.twitter.com/H5HFPYsAXN— Aaron Bradley (@aaranged) October 14, 2021

User profiling has long been an important problem that investigates user interests in many real applications. Some recent works regard users and their interacted objects as entities of a graph and...

Graph neural networks (GNN) are a type of machine learning algorithm that can extract important information from graphs and make useful predictions.

In this paper, we present a pre-trained language model (PLM) based framework called RID for conversational recommender system (CRS). RID finetunes the large-scale PLMs such as DialoGPT, together...

Global datasphere is increasing fast, and it is expected to reach 175 Zettabytes by 20251 . However, most of the content is unstructured and is not understandable by machines. Structuring this...

The supply chain traceability graph solution implemented by Infosys uses the Azure Cosmos DB Gremlin API and other Azure services. It provides global supply chain track and trace capability for finished goods.

Here's the recording of my session "Better Project Management with Knowledge Graphs" at the PMI Fair Benelux 2021.#projectmanagement #knowledgegraph #PKGhttps://t.co/ldG671YDpr— Ivo Velitchkov (@kvistgaard) October 18, 2021

Beer Recommendation Engine with Graph Data

We are proud to introduce WebQA, a dataset for multi-hop, multi-modal open-domain question answering challenge, to be hosted at NeurIPS 2021 Competition Track. Designed to simulate the heterogeneous information landscape one might expect when performing web search, WebQA contains 46K knowledge-seeking queries whose answers are to be found in either images or text snippets, where a system must…

GraphDB 9.10 features smart updates via SPARQL templates and Kafka as well as graph-path search optimizations

Marketers and media owners are looking for ways to get the most out of consumer data amid changes to privacy laws and growing restraints on online audience tracking. They can un...

Software development organizations are seeking to add quality to their products. Testing processes are strategic elements to manage projects and product quality. However, advances in technology and the emergence of increasingly critical applications make testing a complex task and large volumes of information are generated. In fact, software testing is a knowledge intensive process. Because of this, these organizations have shown a growing interest in Knowledge Management (KM) programs, which in turn support the improvement of testing procedures. KM emerges as a means to manage testing knowledge, and, consequently, to improve software quality. However, there are only a few KM solutions supporting software testing. This paper reports experiences from the development of an approach, called Ontology-based Testing Knowledge Management (OntoT-KM), that aims to assist in launching KM initiatives in the software testing domain with the support of Knowledge Management Systems (KMSs).  OntoT-KM provides a process guiding how to start applying KM in software testing. OntoT-KM is based on the findings of a systematic mapping on KM in software testing and the results of a survey with testing practitioners. Moreover, OntoT-KM considers the conceptualization established by a Reference Ontology on Software Testing (ROoST). As a proof of concept, OntoT-KM was applied to develop a KMS called Testing KM Portal (TKMP), which was evaluated in terms of usefulness, usability, and functional correctness. Results show that the developed KMS from OntoT-KM is a potential system for managing knowledge in software testing, so, the approach can guide KM initiatives in software testing.

Boosting Graph Embedding on a Single GPU https://t.co/ozJ60Kz5Yi pic.twitter.com/XJqPDRNDOK— Aaron Bradley (@aaranged) October 20, 2021

"Our knowledge graph form of NOAA climate data facilitates the supply of semantic climate information to researchers and offers a variety of semantic applications that can be built on top of it." https://t.co/2C2ZAUGSJy pic.twitter.com/Z459afns5S— Aaron Bradley (@aaranged) October 20, 2021

"Language Models As or For Knowledge Bases" a position paper about strengths, limitations and complementarity of language model and knowledge bases.(Razniewski et al, 2021)https://t.co/TZisPy3hHH@maxplanckpress @andrewyates pic.twitter.com/252pQFxwau— WikiResearch (@WikiResearch) October 21, 2021