Knowledgebase Meta Guide


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According to the above definitions, the difference between a database and a knowledgebase is that the data in a kowledgebase has been processed by rules, which represent the fruits of experience.

Knowledgebase

Notes:

A knowledgebase is a special kind of database for knowledge management. Knowledge may be defined as the theoretical or practical understanding of a subject, which in terms of classical AI means facts and rules. In fact, an algorithm is a process or set of rules. An expert system is a computer system that emulates the decision-making ability of a human expert, using reasoning. And in this case, reasoning is a rule-based process applied to a knowledgebase. In this day and age, web semantics are considered the sine qua non of knowledge representation.

SPARQL is to RDF as SQL is to RDB. Triples can be thought of as basic sentences, with subject-predicate-object, showing relationship. Thus knowledge about things is represented in the relationship. A knowledge graph is a set of triples, or RDF statements. A graph store, or graph database, is a repository of these knowledge graphs. Almost all big social media monitoring products, such as Alterian and Radian6, will store tweets for you in a database, at a price.

Linked Data:

Best DBpedia Videos | DBpedia & Natural Language Agents 2011 | DBpedia & Question Answering Systems | Freebase & Agents 2011 | YAGO-QA | YAGO2

Wikipedia:

Cloud Database | Data Architecture | Data Integration | XML Databases with database APIs

See also:

100 Best Cloud Database Videos | 100 Best Data Integration Videos | 100 Best Graph Database Videos | Best XML Database Videos | Best Fusion Tables Videos | Meta Guide System Architecture | Robitron Database Survey | Spreadsheet Scraping


2007: Project AIML-OWL – This was my initial investigation into the conversion of XML dialects into semantic formats such as RDF.

2007: Project JE – This was a joint project with Mamet & Associates to represent early feed bots in a comprehensive, dynamic ASP website.

2008: Project James – This was a short-lived exploration with a professional associate into WAMP, MySQL, Microsoft SQL Server, Entity Relationship Diagramming, and XSL Transformations (XSLT).

2008: Project Simon – This was a collaboration with an expert colleague to create a proof of concept for a proprietary component to convert concgrams (similar to n-grams) into associated latent semantic indexes using SPSS.

2009: Project VagaBot – This phase represented the culmination of efforts to convert my book, VAGABOND GLOBETROTTING (ePub), into the VagaBot conversational agent.

2009: Project STELARC – I consulted with the famous performance artist known as STELARC, as much about his work as my own.


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2010:

Interface Layer (Avatar System)

Dialog System Layer (AI Engine)
Machine Learning Layer (Machine Learning)

RapidMiner

Logic / Rules Layer (Summarization Component / Conceptual Generator)
DERI Pipes: RDFa > Reasoner > XSLT | GATECloud

Semantic Data Layer (Knowledgebase)
Managed AllegroGraph Hosting in the Cloud (Beta) | Dydra | Cumulus RDF | Stardog | Talis

Semantic Pre-Processing Layer
Yahoo! Pipes | DataSift | AlchemyAPI (AlchemyAPI Pipes Examples) | EditGrid APIs | Zoho API Program

Pre-Filter Layer (Feed Bots)


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2011:

Architecture: In this proposed solution cloud-based middleware integration of remote APIs would substitute the solution stack layer.

Service-Oriented Architecture:

>>
In IaaS, you select the pre-canned OS layer, deploy the application stack, deploy your code & then add your data

In PaaS, you deploy your code (OS/Application Stack is part of the offering) & then add your data

In SaaS, you add your data (everything else part of the offering)

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According to Gartner, Cloud middleware services, also known as platform as a service, is a new battlefield in which the leading software vendors compete for industry influence and market leadership. And according to Aaron Levie?, SaaS is Crate and Barrel, PaaS is IKEA, and IaaS is HomeDepot.