RDF (Star) and SPARQL (Star)

RDF* & SPARQL*

Types of RDF Storage

Native

• Main Memory-based
• Disk-based

Non-native

• RDBMS
• Schema-based
• Vertical partitioning
• Hierarchical property table
• Property table
• Schema-free
• Triple table

• NoSQL
• Key-value
• Column Family
• Document store
• Graph database

SHACL

SHACL

Spreadsheet Transformations to Metadata

Spreadsheet Transformations to Metadata

Industry Classification

Industry Classification

Relational to Semantic Mappings

R2RML
RML

The Accidental Taxonomist

The Accidental Taxonomist

Smart Data Lakes

Anzo

Franz

Theasuri Editors

TopBraid
PoolParty
OpenSKOS
Protege SKOS Editor
WebProtege
VocBench
SKOS Play
TemaTres
ThManager
Semantic Media Wiki
Ontostudio

Linked Data Patterns

Linked Data Patterns

Semantic Web Meetup Course

semantic web london
metadataconsulting

• Introduction to Semantic Web standards and Linked data technologies 
• Resource Description Framework 
• Graph-based data model representation and core concepts 
• Terse RDF Triple Language 
• Advanced RDF features 
• Best practices on publishing RDF data 
• RDF Schema (RDFS) 
• Discussion of the added value of a schema driven by examples 
• Syntax of the core features: classes, properties and their characteristics 
• Relationships between RDFS vocabulary elements 
• Computing answers to typical queries over RDFS datasets 
• Using Protege for modeling and querying RDFS datasets 
• Limitations of RDFS 
• Querying Semantic Web with SPARQL 
• Core concepts 
• Basic graph patterns 
• Querying datasets with the SPARQL engine StarDog 
• Filters and SPARQL expressions 
• Property path expressions 
• Complex graph patterns with advanced features such as optional parts, aggregation and ordering Other query types 
• Updating with SPARQL 
• OWL Web Ontology Language  
• Core concepts and differences to RDFS 
• Overview of OWL modeling constructs
• Modeling and assessing the benefits of alternative models in a particular application context Substitutability of modeling constructs 
• Discussion of the trade-off between the expressivity of modeling languages and the computational efficiency of querying 
• OWL profiles 
• Limitations of the expressive power of OWL 
• Applications of Semantic Technologies in Practice

Alternatives:

futurelearn - linked data

hpi - semantic web technologies

mooc list - semantic-web

cambridge semantics - semantic university

Outstanding Ontologies

There are different types of ontologies ranging from knowledge representation ontologies, domain ontologies, linguistic ontologies, and top-level ontologies. A selection of a few examples from different types are provided below.

Knowledge Representation Ontologies:

Frame Ontology

OKCB

Top-Level Ontologies: 

Cyc

SOWA

Standard Upper Ontology

Linguistic Ontologies: 

Wordnet

Generalized Upper Model

Sensus

Eurowordnet

Mikrokosmos

Ecommerce Ontologies (Domain Ontology): 

United Nations Standards Products and Services Codes

North American Industry Classification System

Standard Classification of Transported Goods

E-Cl@ss

RosettaNet

Medical Ontologies (Domain Ontology):

GALEN

UMLS

ON9

Engineering Ontologies (Domain Ontology):

EngMath

PhysSys

Enterprise Ontologies (Domain Ontology):

Enterprise Ontology

TOVE

Chemistry Ontologies (Domain Ontology):

Chemicals

Ions

Environmental Pollutants

Knowledge Mgmt Ontologies (Domain Ontology):

KA Ontology - Project, Organization, Person, Publication, Event, Research-Topic, Research-Product

Nature.com Subjects Ontologies

Blazegraph

Blazegraph - Semantic Graph Database / Triplestore
Technology To Watch For 2016

SKOS

SKOS is a very common data model for representing knowledge in form of thesauri or controlled vocabularies which can provide for interlinked knowledge graphs as a form of linked data. SKOS is a lightweight and flexible OWL ontology representation format available in various RDF syntax. OWL on the other hand is an ontology language. It is possible to convert from SKOS to OWL and even to combine them. The below links provide some related tools and libraries for working with SKOS models. 

JSKOS
SKOSAPI
OWLAPI
SKOSEd
OpenSKOS
TemTres
THManager
PoolParty
TopBraid
Thesaurus Master
Lexaurus
Fluent Editor
Intelligent Topic Manager
SKOS2OWL
Protege
SKOSIFY
Poolparty Consistency Checker
KEA
SKOSMOS
SILK

W3C SKOS
SKOS: A Guide for Information Professionals
SKOS Taxonomy
The Accidental Taxonomist
Knowledge Engineering with Semantic Web Technologies
LinkedData Engineering
PoolParty Academy
Gate
Ontotext
Knowledge Extraction
Taxonomy Warehouse
Synaptica

Graph Comparison

Analytical
Type	Backend	Supported Frameworks	Context of Use
Giraph	Hadoop/HDFS	Spark/Hadoop	Data Processing for Analytics
GraphX	Titan, Neo4J, HDFS	Spark	Data Processing for Analytics (in-memory)
GraphLab	Hadoop/HDFS	Spark/Hadoop	Data Processing for Analytics, using PowerGraph and GAS models

Operational
Type	Backend	Supported Frameworks	Context of Use
Cayley	MongoDB or LevelDB	Custom Implementation in Go	Knowledge Graph
Titan	Cassandra, HBase, HDFS	Tinkerpop & RDF SPARQL	Massive Knowledge Graphs OLAP/OLTP (now part of Datastax)
Neo4J	Custom	Tinkerpop	Data Visualization, Web Browsing, Portfolio Analytics, Gene Sequencing, Mobile Social Application
OrientDB	Custom	Tinkerpop & RDF SPARQL	Embedded and Standalone, Knowledge Graph, Multimodel (Document + Graph)

Semantic
Type	Backend	Supported Frameworks	Context of Use
Blazegraph and MapGraph	Custom	Sesame RDF SPARQL Tinkerpop	Massive Knowledge Graphs on GPU, includes support for Semantic Web Standards of W3C (used by Wikidata, a Wikimedia project)
Stardog	Custom	RDF SPARQL	In cloud the semantic data use case (third-party)
OntoText GraphDB	Custom	Sesame Jena RDF SPARQL	Optimized as a Semantic Graph Database based on Semantic Web Standards of W3C (used by BBC, Euromoney, FinancialTimes, etc)
Virtuoso	Custom/Hybrid	Sesame Jena RDF SPARQL	Optimized as a Semantic Graph Database based on Semantic Web Standards of W3C (used by DBPedia)
Allegrograph	Custom	Sesame RDF SPARQL	Optimized as a Semantic Graph Database based on Semantic Web Standards of W3C
OpenCog	Custom	Semantic Knowledge	Massive Artificial General Intelligence Graph Knowledge Base

wikidata graph comparison

OLTP/Graph Databases
OLTP/Analytical Databases
Graph Database as a Service
Native Semantic Graph Databases
Graph Query / Interfaces

Semantic Representation

Representation of semantic data is a computationally expensive process with a lot of embedded metadata for building semantically contextual graphs. However, such representation also comes at a storage and processing cost. XML standard has always been a more complete representation option on basis of which other standards have been developed. However, the introduction of JSON-LD provides further options for flexibility. Unfortunately, flexibility of semantic data processing also comes at a cost from loss in fidelity. Representing JSON-LD maybe a plausible option. But, storing the raw form of RDF in XML compatible native form is always favorable. This loss in fidelity may arise during content negotiation and during conversion. But, RDF is quite a memory intensive representation format which requires a separate processing requirements. Even viewing RDF from property graph perspective may not be sufficient. And, utilizing triple stores and even quad stores have always been the best option even of today, while such options still provide issues with vendor lock-in at times. Although, RDF and semantic web have come along way, there is still a lot that can be done both in terms of standardization as well as better distributed semantic graph storage. Semantic integration is again a core aspect of Linked Data requirements which is another aspect that requires more standardization and advancement. JSON-LD appears to be a useful option for front-end client processing in a lightweight integration. It also has some fundamental limitations in comparison to RDF. A question arises as to why the W3C gave up on the idea of RDF/JSON standardization. However, this is a case of what is more important in the semantic web community and for an application context, whether the representation should be in computer readable or human readable form. Nonetheless, the core representation format of semantic web for storage, in most domain contexts, should really be maintained in the native form of RDF/XML and associated derivatives for obvious reasons.

Metadata Standards

Library and Book Publishing metadata standards have come a long way and they are still in a state of flux and evolution as cataloging and publishing take on emerging new forms for further standardization and universal interpretation. Data science and Data Mining are also providing new ways of harnessing information and knowledge about classification of both data and content. However, metadata are still the epitome of differentiating and exposing data in all its transformations. XML is often seen as the mainstream format for most metadata standards. However, JSON and RDF have also emerged to break into a strong hold in developing more flexible and universal standard formats. Metadata is categorized in fundamentally three different types: administrative, descriptive, and structural. The following handbook provides further details on the book publishing structures and evolving metadata trends.

the metadata handbook

BISG
BIC
BNC

Semantic Certifications

Getting certified in a particular technology is a debatable topic. For some employers, it could be a plus point for achievement. For others, it holds very little value. And, yet certifications can also get outdated very quickly. Perhaps, getting certified with the concepts is more important than the technology certification that is specific to a version. Semantic Web technologies are slow moving as they go through an extensive specification driven process. They are also rarely taught so formally at university and even for certifications. However, Semantic Web is growing in popularity as industry sees remarkable benefits in contextualizing data and information on the web as well as a wide variety of use cases. Semsphere Certifications is one unique starting point that provides solid grounding in the area with a rigorous exam. The certifications are primarily at two levels which may be of interest to most developers: Specialist and a Professional. The third level is primarily for trainers. The two levels cover broadly the core areas of interest and technologies in Semantic Web:

HTTP

RDF

SPARQL

RDFS

OWL

SKOS

VoID

RDFa

eRDF

GRDDL

SAWSDL

HREST

SA-REST

WSMT

iServe
Protege
Pellet

Semsphere Specialist

Semsphere Professional

Semsphere Academy Trainer

EAV vs SPO

Knowledge Representation has been around for a long time, within database architecture, but also within abstractions of domain logic especially in area of analytics. There have been various formalisms defined for representation of such knowledge in order to derive a more structured logic for learning and reasoning from data. Databases have historically provided the persistence layer for many applications and usually hold the key to unlocking a lot of the data today. However, good data representation allows for versatility, performance, and unlocking hidden knowledge. Entity-Attribute-Value (EAV) and Subject-Predicate-Object (SPO) are similar in modelling approaches. In both cases, one is working with relationship by 3-tuples. However, EAV is a subset of SPO. Often, people who are not familiar with SPO and are more comfortable with the relational model design end up utilizing EAV, as an extension. However, in most cases, EAV is seen as an anti-pattern leading to higher development times, poor utilization of data, and undesirably more complex queries. In SPO, everything is treated as a resource and extended in a graph representation. It also allows for better reasoning capabilities for inference. SPO also lends very well to the architecture of the web, utilizing URI schemas, in a linked context, as an extension to the RESTful approach of using standard HTTP methods. On one hand, the EAV tries to build richer metadata semantics through a schema taxonomy, as an extension to the database relational model. While, in SPO the approach is more synonymous to ontologies in form of extensible linked data schema for a domain context, which is not only web friendly, but also machine readable. SPO is also linguistically inspired from natural language.

eav and spo
eav/cr model
rdf
SPO
considerations for eav
considerations for modelling eav for biomedical databases
magento eav
eav talk
understanding linked data via eav model

Open Annotations

Open Annotations Community is an interesting collaborative group defining the standards for specifications on interoperable and extendable annotations which can be enabled for sharing across multiple application, device, and service domains. The open approach here looks towards maximizing on accessibility with unfettered access and even for the addition of new techniques. At same time, there is compatibility with use of standard approaches of publish/subscribe models. Although, it does not define a specific protocol for such interactions. The seamless effort is designed to work with the simplicity of the displaced architecture of the web. As a semantic web standard the approach towards an annotation is taken from the viewpoint of an RDF graph serialization. The design stipulates for body with one or more targets which can be defined as URI resources.  Although, some annotations may not utilize a body. Each resource then has a very distinctive metadata and provenance information with any relevant media type that can be dereferenced. Additional representations can also be defined or resolved as changes arise to resources via content negotiation. There are extensive use cases available for open annotations. The open annotations community is also very active and has draft specification in place for data model.