Personal Knowledge Graph generation is no longer a cumbersome technical endeavor. PROV4ITDaTa is an MIT open source platform to provide a smooth user experience for generating knowledge graphs from your online web services, such as Google, Flickr, and Imgur, into your personal data space. This br...

Semantic MediaWiki (SMW), which was introduced as early as in 2006, has since gone on to establish a vital community and is currently one of the few semantic wiki solutions still in existence. SMW is an extension of MediaWiki, the software used for Wikipedia and many other projects, resulting in ...

This presentation will feature and demonstrate "VocBench", an semantic web collaborative development platform for ontologies, thesauri and lexicons.
VocBench has initially been developed for maintenance of the thesaurus "Agrovoc". It has become then a generic tool for thesaurus management and now...

Python offers excellent libraries for working with graphs: semantic technologies, graph queries, interactive visualizations, graph algorithms, probabilistic graph inference, as well as embedding and other integrations with deep learning. However, most of these approaches share little common groun...

Knowledge graphs are powerful tools used by organizations to integrate their siloed data into useful, machine readable information for a wide range of purposes. The OriginTrail Decentralized Knowledge Graph (DKG) extends this approach to enable trusted knowledge exchange between multiple organiza...

Show me your schemas, and I will show you a graph! Although graph databases have become very popular in the enterprise, deep expertise in graphs is still in short supply (see "Building an Enterprise Knowledge Graph @Uber: Lessons from Reality" from KGC 2019). Developers often think of graphs as a...

Machine Learning (ML), as one of the key drivers of Artificial Intelligence, has demonstrated disruptive results in numerous industries. However one of the most fundamental problems of applying ML, and particularly Artificial Neural Network models, in critical systems is its inability to provide ...

OpenRefine is a data wrangling tool which celebrated its 10th birthday this year. Cleaning and importing data in knowledge graphs is its core use case, since it was originally designed to help populate Freebase. In this talk I want to give a broad overview of the latest developments in the tool a...

Knowledge Graphs can be considered as fulfilling an early vision in Computer Science of creating intelligent systems that integrate knowledge and data at large scale. Stemming from scientific advancements in research areas of Semantic Web, Databases, Knowledge representation, NLP, Machine Learnin...

Knowledge Graphs are increasingly being developed and leveraged in academia and industry to tackle complex biomedical challenges, such as drug discovery and safety, medical literature search, clinical decision support, and disease monitoring and management. In this talk, we will present the resea...

In this presentation we will show how current general-purpose CPU hardware fails to deliver high performance graph analytics. We show that by doing a detailed analysis of the actual hardware functionally needed by graph queries (pointer jumping), we can redesign hardware that is optimized for fas...

SPOKE is a biomedical knowledge graph containing factual data on various biomedical fields including genetics, molecular biology, physiology, metabolomics, pharmacology and clinical medicine. SPOKE can be used to repurpose medications, predict patient outcomes, and accelerate drug development, am...

Semantic systems provide tremendous opportunities to interoperate our data, facilitate shared vocabularies, and power enterprise knowledge graphs, but these increasingly distributed data ecosystems also introduce new instabilities and concerns. What happens when data we rely on is changing in une...

When knowledge graphs in your company get larger and larger, a scalable graph search is in high demand. In the current graph search solutions, scalability is still a big issue. Furthermore, with the fast development of deep learning on graphs, many companies rely on deep learning methods to mine ...

The lack of a convenient way to capture annotations and statements about individual RDF triples has been a long standing issue for RDF. Such annotations are a native feature in other contemporary graph data models (e.g., edge properties in the Property Graph model). In recent years, the RDF* app...

The Property Graph Schema Working Group (PGSWG) is an informal working group that was set up in 2018 under the umbrella of LDBC, the Linked Data Benchmark Council, to support the formal working group that works on the SQL/PGQ and GQL, the upcoming ISO/IEC standards for managing property graphs. T...

Content Enrichment: Development and deployment of a 5-stage taxonomy. Applying the taxonomy to tag regulations and classify them for improved discovery & work assignment.
Smart Authoring: Leveraging advanced NLP and ML techniques to learn from the past content authoring for identification of key ...

The increasing reliance on distributed epidemiological data sources for time sensitive analysis defines an emergent computational commons space: API ecosystems supporting epidemiology data commons. This space has been forced to evolve significantly to meet the real-time requirements of COVID-19 i...

Customer adoption of graph databases is growing rapidly and has attracted many vendors and products. Graphs, as an abstraction, are a simple and intuitive way to model information about the world. Despite this, the learning curve for building a graph-based application remains steep and daunting, ...

We’ll explore how unstructured and structured data can come together to tell a complete story through the lens of a pharma clinical trial and subsequent events in the field. We’ll weave through the patient history and other documents using an ontology and several NLP tools, then use natural langu...

The enterprise data landscape is increasingly hybrid, varied, and changing. The emergence of IoT, rise in unstructured data volume, increasing relevance of external data sources, and trend towards hybrid multi-cloud environments are obstacles to satisfying each new data request. The old data stra...

Bristol-Myers Squibb (BMS) is a global pharmaceutical company with drug discovery and development programs in several therapeutic areas. As part of its enterprise information governance, there is an ongoing effort to unlock research data from siloed systems, by building a knowledge graph that tra...