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Frequently Asked Questions

What is Contextual Reasoning (SearchQuery) ?
A SearchQuery "understands" the keywords entered which formulate the question. It matches them to distinctive subjects / categories / concepts as well as time- and geo-/location- information. It then returns contextual results based on multiple keywords entered which form "human like questions" (the Linked-Data approach is used to derive the intelligent context). These contextual connections are precise and associative (which is unique -> contextual reasoning in a search experience). It is not a statistical or NLP (natural language processing) approach. Technically speaking it is a superset of SPARQL functionality.

A classic semantic search matches strings with concepts but has no contextual, semantic understanding on the data level (only on the concept level).

What is significant about this ?
The user is given a tool to precisely and associatively get to information which he is looking for. In other words: he easily discovers and finds information without knowing how to search (without knowing the keywords). On top of that the knowledge base is intelligent which allows smart agents to conduct digital reasoning. See Digital Inference

What is Information Linking ?
Information Linking integrates data from any datasource and structure in a virtual manner. This means that physically the data are still separate. They appear to be combined and intelligent context is created during runtime. It is the intelligent, semantic context that creates new business opportunities and ROI. Further more this approach eliminates many of the bitfalls in classic data integration approaches and is the key in moving forward.

What is the difference to Semantic Search Solutions ?
Semantic Search solutions add an ontology on top of a classic search (full text search). This means they improve the ranking and show associative search results based on the ontology. However they do not work with Linked-Data where the intelligence can be derived from the relationships and context within the data itself.

What is Digital Inference / Digital Reasoning ?
Digital inference is a distinctive operation and means to derive new facts from given facts (in other words create new information from given one). Digital inference allows queries to be precise, associative, unambiguous and contextual. In semantic systems this operates on linked-data or knowledge graphs which means that more complex conclusions can be automatically conducted (machine learning, artificial intelligence). E.g. see http://en.wikipedia.org/wiki/Inference

What is an Ontology ?
In computer science and information science, an ontology is a formal representation of knowledge as a set of concepts, terms, categories within a domain, and the relationships between them.
Ontologies are under heavy development since they will be the backbone of the future web ("web 3.0"). In the social networking domain Facebook manages the biggest ontology, linked-dataset or "social graph" (people and their connections). See http://en.wikipedia.org/wiki/Ontology_(information_science)

What is a Dynamic Schema / Dynamic Ontology ?
In classic semantic systems a data schema or ontology must be created first before data can be populated and the application can be developed. This is very much like in the classical database world (tables and indices must be created above all). This prevents short implementation cycles and leads to the known problem that the requirements have changed when the application is finally ready for usage.
A dynamic ontology needs no static schema. Information is fed into the system at any given point in time and the contextual factbase adapts automatically. Instant access through contextual searches is always active.

What are Linked-Data ?
Linked-Data is a world wide movement & technology for 'semantics' in IT systems. It brings intelligence and context into software. The user or software agent is able to ask complex contextual questions without having to know how to ask and without having to know where the information resides (databases, documents or websites).

In linked-datasets the data form a network of information. Whereas in a database all semantics (categories, related subcategories, terms, concepts and relationships) are implicit and not accessible they become explicit and accessible in Linked-Data. This allows for machine learning, digital reasoning, semantic query, virtual information integration and smart operations on the level of information.

Linked-Data can be used behind the firewall and for cross firewall queries.

In the Semantic Web technology stack the concept of linked data is encoded with XML. This allows for easy information sharing outside the firewall. See http://en.wikipedia.org/wiki/Linked_Data

What are Semantic Agents ?
A semantic agent implements the knowledge of an expert to conduct his reasoning methods. It is a small granular piece of software that acts for a user or on behalf of other agents (artificial collective intelligence). Agents are event based and invoke themselves whenever necessary. This requires a communication and coordination framework. The result are lean and powerful application design patterns. Semantic agents operate on Linked-Data, Data Graphs and Semantic Data Models. See http://en.wikipedia.org/wiki/Software_agent

What is a FTS-Server ?
FTS stands for "Full Text Server" (short "Index Server"). It is an application which manages an index over free text (typically text-files or html-files). It collects, parses, and stores data to facilitate fast and accurate information retrieval. The index enables the quick search for a sequence of characters over a big body of text-data. E.g. see http://en.wikipedia.org/wiki/Index_(search_engine)

Further Reads

APPLICATION AREAS

Contextual SearchQuery

Contextual Dashboards

Distributed Information Discovery

Qualitative Machine Learning

Computational Biology

Spatial Information Research

Social Network Intelligence

CASE STUDIES

.com Businesses

Financial Services

Pharma

Government

Enterprise 2.0

Educational Apps

Mobile Apps

WHITE PAPERS

Executive Summary

SBSGRID Brochure

SBSGRID Smart Information Spaces

Contextual Queries Explained

Visual Linked-Data Publishing

Cross DB Information Linking

INFO POOL

Industry Linked Data Clouds

Industry Videos

Industry Articles

Industry News

FAQ

"SBSGRID In 10 Seconds"

This is SBSGRID

The Company

High Performance Reasoning

Intelligent Data

Intelligent Software

Global Information Linking

Thinking Ahead

Slide Presentations

SearchQuery Explained

SBSGRID Overview

White Papers

Meaning based Information Linking

Visual Linked-Data Publisher

General

FAQ

Industry Linked Data Clouds

What are Linked-Data?

Linked-Data Testimonials

SBSGRID in 10 Seconds

SBSGRID injects intelligence into your software. The system learns from all information added by putting it into an overall information context. Through a "SearchQuery" the user (or software agent) instantly finds what he is looking for. IT budgets are turned into sustainable investments because the static information model approach is eliminated. Productivity and intelligence in the system and between systems are brought to the next level.