A large community exists “that sees Linked Data, let alone the full Semantic Web, as an unnecessarily complicated technology”, Phil Archer. However early adopters are always known for zealotry rather than pragmatism: mainframe to mini, mini-to-pc, pc-to-web and many more are examples where the zealots often threw the baby out with the bath-water.
Rather than abandoning the semantic web perhaps we should take a pragmatic view and identify its strengths, together with some soul-searching to be honest about its weaknesses:
SPARQL is unlikely ever to be end-user friendly:
However one can say exactly the same of SQL (which, believe it or not, used to be called User-Friendly-Interface, or UFI for short). SQL is now hidden behind much more developer-friendly facades so that they can deliver user-friendly experiences such as Hibernate/JPA, C#+LINQ, Odata, and many more. Even so SQL remains the language of choice for manipulating and querying RDBMS.
So we still need SPARQL for manipulating semantic data, but we need developer- and user-friendly facades that make the semantic information more accessible. Some initiatives are JPA for RDF, LINQtoRDF, Odata-SPARQL but the effort is fragmented with few standard initiatives.
RDF/OWL is unlikely to supplant Entity-Relational, JSON-Object databases:
However we have all learnt the lesson that the data model is often at the core of any application. Furthermore any changes to that data model can be costly, especially in the later stages of the development lifecycle. Who has not created an object-attribute data model deployed in an RDBMS to retain design flexibility without the need to change the underlying database schema? Are we not reinventing the semantic data model?
So the principles and flexibility of semantic (RDF, RDFS, and OWL) data modeling of data could be adopted as a data modeling paradigm representing the evolution that started with Entity-Relationship (ER), went through Object-Role Modeling (ORM or NIAM), to Semantic Data Modeling (SDM). Used in combination with dynamic REST/JSON, such as Odata, one could truly respond to the Dynamic Business Application Imperative (Forrester).
Triple-stores are unlikely to supplant Big-Data:
However, of volume, velocity, and variety, many big data solutions are weaker when handling variety, especially when the variety of data is changing over time as it does with evolving user needs. Most of the time the variety at the data source will be solved by Extract-Transform-Load (ETL) into a big-data store. Is this any different than data warehousing which has matured over the last 20 years, except for the use of different data storage technology? Like any goods in transit, data gets damaged and contaminated when it is moved. It is far better to use the data in-situ if at all possible, but this has been the unachievable Holy Grail of data integration for many years.
So the normalization of any and all data into triples might not be the best way to store data but can be the way to mediate variable information from a variety of data-sources: Ontology Based Data Access (OBDA). The ontology acts a semantic layer between the user and the data. The semantics of the ontology are used to enrich the information on the sources and/or cope with incomplete information in them.
In summary …
Semantic technologies will see more success if it pursues the more pragmatic approach of the solving those problems that are not satisfactorily solved elsewhere such as querying and reasoning, dynamic data models, and data source mediation; instead of resolving the already solved.
Phil Archer http://semanticweb.com/tag/phil-archer
Dynamic Business Imperative: http://www.forrester.com/The+Dynamic+Business+Applications+Imperative/fulltext/-/E-RES41397?objectid=RES41397
Ontology Based Data Access: http://obda.inf.unibz.it/