by Allard Oelen and Markus Stocker

Instead of representing #research in static PDF articles @l3s_luh works on a dynamic #knowledgegraph. The Open Research KG represents ideas, approaches, methods in machine-readable form. A comparison of different genome editing methods can be created

This blog post by Steve Sarsfield provides an overview of the graph market and how to select the appropriate graph database for your particular use case.

The International Semantic Web Conference (ISWC) is the premier international forum, for the Semantic Web Community. ISWC 2018 in Monterey, CA (USA) will bring together researchers, practitioners and industry specialists to discuss, advance, and shape the future of semantic technologies

Updates on #knowledgegraphs affect services built on top of them. But not all changes are the same: some updates drastically change the result of operations based on knowledge graph content; others do not lead to any variation #research #award #iswc_conf

Towards the @BoschGlobal Materials Science Knowledge Base #Industry40 #data #knowledgemanagement #knowledgegraph use case in #iswc_conf #digitaltransformation #business #tech @metaphacts http://ceur-ws.org/Vol-2456/paper89.pdf

How to generate RDF from very large and heterogeneous data sources. Presentation on the Semantiv Big Data workshop @ SIGMOD/PODS 2019 conference.

Have you ever wondered how when you search for something or someone in Google search engine you see relevant stories, Wikipedia and images…Continue reading on AlgoAnalytics »

The workflow & cost of my current Personal Knowledge OS setup, key tools includes @RoamResearch @readwiseio @instapaper @AirrAudio @reed...

generation retrieval systems to improve retrieval performance. Knowledge graph abstracts things into entities and establishes relationships among entities, which are expressed in the form of triples. However, with the expansion of knowledge graph and the rapid increase of data volume, traditional place retrieval methods on knowledge graph have low performance. This paper designs a place retrieval method in order to improve the efficiency of place retrieval. Firstly, perform data preprocessing and problem model building in the offline stage. Meanwhile, build semantic distance index, spatial quadtree index, and spatial semantic hybrid index according to semantic and spatial information. At the same time, in the online retrieval stage, th

Rock and Roll!

06/20/19 - Knowledge graph reasoning, which aims at predicting the missing facts through reasoning with the observed facts, is critical to ma...

L3S member Prof. Dr. Sören Auer, Director of the TIB and Professor of Data Science & Digital Libraries at Leibniz Universität Hannover, is one of the world's most cited researchers in the field of artificial intelligence. Analyses of academic data from the online platform AMiner (https://www.aminer.org/ai2000/ke) have shown that Sören Auer is one of the most influential scientists in the field of knowledge engineering (knowledge modelling).  The list, which puts him in 4th place, honors the work of the researchers over the last 10 years. The "Most Influential Scholar" award is given in recognition of outstanding technical achievement with lasting impact. How can the handling of information, data and knowledge be improved and made more effective? In view of the enormous technological progress, how can knowledge and information be digitally networked so that they can be better used by machines in the future? Auer and his team at the TIB and the L3S in Hannove

See how to benchmark your system using the HOBBIT platform @hobbit_project #benchmarking #opendata #LinkedData #MachineLearning #H2020 https://t.co/BvzQwO7W8U pic.twitter.com/RW9LDCxvFB— Project HOBBIT (@hobbit_project) July 4, 2019

Are Property Graphs Training Wheels on the way to #KnowledgeGraphs? @semanticarts contention is that beyond initial #analytics use case, great body of work that already exists, has been long standardized will have to be reinvented #graphDB #datascience

Graph databases such as Neo4J are gaining in popularity. These are in many ways comparable to RDF databases (triplestores), but I will highlight three differences: The underlying datamodel in most …

scale incomplete knowledge graphs

Querying @DBPedia #LinkedData From Jupyter Notebooks – Music Genres Related to Heavy Metal and Music Venues in England. #data #tech #dataviz #knowledgegraph #datascience @psychemedia h/t @aaranged

Part 1.

Querying the first RDF Data Cube based on an existing PC-Axis/PX data cube published by @swissstatistics. Thanks to @bergi_bergos for the great converter, will be made public later pic.twitter.com/4SJ3jpKg7e— Adrian Gschwend (@linkedktk) July 10, 2019

Last month in Populating a Schema.org dataset from Wikidata I talked about pulling data out of Wikidata and using it to create Schema.org triples, and I hinted about the possibility of updating Wikidata data directly. The SPARQL fun of this is to then perform queries against Wikidata and to see your data edits reflected within a few minutes. I was pleasantly surprised at how quickly edits showed up in query results, so I thought I would demo it with a little video.

Building on the newbie’s guide to querying #Wikidata, @markhneedham learns all about the CONSTRUCT clause in SPARQL #softwareengineering #datascience #tutorial #opendata #linkeddata #knowledgegraph #GraphDB #data #tech

Natural language question answering over #knowledgegraphs enables accurate answers in intuitive manner. But bridging the gap between unstructured questions & knowledge graphs is a challenge. Novel notion: structural query pattern #research #AI #NLP

In this paper, an overview of the state of the art on knowledge graph

RDFox is a high performance knowledge graph and semantic reasoning engine developed by Oxford Semantic Technologies. This short article will help you understand the key concepts behind RDFox and when to use them in your applications.Knowledge GraphsA knowledge graph is composed of a graph database to store the data and a reasoning layer to interpret and manipulate the data.Relational databases store data in structured records whereas graph databases store data points as nodes which are connected with edges if they share some form of relationship.Data stored in a graph can be accessed with a query which will “hop” along the edges to find the requested nodes.ReasoningReasoning is the process of materialising rules which apply to the data. Materialising a rule means adding new nodes or edges to the graph when it is satisfied. These new nodes and edges match the rule’s “pattern”.A rule can be as simple as an “If… then…” statement.For example: “If a city is located

(Submitted on 18 Mar 2020) Abstract: In the active research area of employing embedding models for knowledge graph