Frequently Asked Questions
A SQL Knowledge Graph is the implementation of ontologies and graph theory in standard SQL. It has three components: (i) a virtual SQL ontology of connected, context-enriched concepts with inference capabilities and graph analytics features; (ii) a mapping of the virtual SQL ontologies to existing databases accessible in SQL and, (iii) a query runtime engine that translates SQL queries of the ontology into SQL queries pushed down to the underlying databases. The SQL Knowledge Graph closes the gap between knowledge representation and enterprise databases/legacy systems/data warehouses/data lakes, to conveniently enable smart, semantic data fabrics and digital twins without need to change DBMS infrastructure.
timbr offers a fast, easy and no-risk implementation of the semantic graph. The main reasons are that there’s no need to move data or learn any new proprietary query languages to work with the Knowledge Graph.
Modeling a SQL ontology can be done either manually or automatically from an ERD, OWL ontologies, or from data catalogs.
The mapping of the data to the Knowledge Graph is also done either manually or semi-automatically.
Conceptual modeling is a representation of the real world. It is the first step of data modeling, a method developed to help with the design of databases and defining a formal vocabulary for the organization.
The process leading to the actual modeling and creation of databases leaves out information that is key to understanding and using data effectively. To make up for this information left behind, enterprises require coding complex queries in complex applications.
Ontologies are an effective means to re-create the information left behind, giving back business meaning to the data, simplifying data access and delivering unique analytical capabilities.
An ontology defines a common vocabulary for an organization that needs to share information in a domain.
This includes machine-interpretable definitions of basic concepts in the domain and relations among them.
An ontology is structured as a graph, where every node on the graph represents a “concept.”
A concept could be anything: Person, Place, Customer, Car, Country, Product, Event etc.
SQL ontologies are ontologies that implement the Semantic Web in SQL (what is an ontology?) and are designed to provide common business meaning to data distributed in varied sources and enable them as concepts with inference and graph traversal capabilities to facilitate discovery, use and access to data.
With timbr you can model and explore your ontology visually or in standard SQL. The SQL Ontology is exposed to the SQL user as a virtual schema with virtual tables (concepts) using any SQL client with JDBC/ODBC.
Semantic SQL is SQL used for querying SQL ontologies instead of directly querying the underlying data. By querying the ontology’s concepts, users benefit from graph traversals and semantic reasoning features, so SQL queries become significantly less complex and query size is reduced significantly.
A semantic data catalog is an intelligent catalog/inventory of data assets that automatizes sharing common meanings of data across data silos and provides a means to define hierarchies and relationships featuring semantic reasoning. It serves as a queryable, AI-enabled knowledge encyclopedia of the organization. timbr enables the fastest and most convenient implementation of semantic data catalogs connected to your databases and business intelligence tools. Contact us to schedule a demo.
The semantic data fabric is a flexible, reusable layer and set of data services used as the single source providing universal meaning and context to data for the entire organization. The data fabric integrates on-premise and cloud data sources in use by the organization, handing them semantic capabilities to provide answers to complex queries and to facilitate understanding and use of data. It provides consistent capabilities across on-premises and multiple cloud environments to accelerate digital transformation. timbr enables the fastest and most convenient implementation of semantic data fabric connected to your cloud and on-premise databases and business intelligence tools. Contact us to schedule a demo.
A digital twin refers to a digital replica of potential and actual physical assets, processes, people, places, systems and devices that can be used for various purposes. The digital representation provides both the elements and the dynamics of how an Internet of things (IoT) device operates and lives throughout its life cycle.
Digital twins have two important characteristics.
1. each definition emphasizes the connection between the physical model and the corresponding virtual model or virtual counterpart.
2. this connection is established by generating real-time data using sensors.
timbr helps enterprises create digital twins by enabling the definition of the virtual model using SQL ontologies and by connecting the virtual model to data lakes that contain the sensor’s data. Contact us to schedule a demo to see why timbr facilitates the fastest and most convenient implementation of digital twins.
The Semantic Web is a project devised by Tim Berners Lee and James Hendler (et al), and adopted by the W3C (the manager of the Internet). The Semantic Web implements ontologies so that machines connected to the Web “understand” each other by sharing common meaning of data using a set of standards. The standards developed by the W3C define among others, an ontology modeling language (OWL) and a query language (SPARQL).
timbr implements the principles of the Semantic Web in standard SQL, meaning that both the ontology modeling and the queries are done in SQL.
Creating a SQL Knowledge Graph is a simple process:
1. Connect your databases to the virtual layer using JDBC connectors.
2. Model the SQL ontology visually or using timbr SQL DDL statements, or import from other sources (data catalogs, OWL ontologies, ERD tools).
3. Map the ontology concepts to the data.
That’s it, your SQL Knowledge Graph is ready for use and can start delivering unique insights via SQL queries, graph data exploration, your BI tools, or using timbr’s embedded charts and dashboard module.
The SQL Knowledge Graph serves as a virtual graph for all the enterprise data engines. Organizations use it to integrate, analyze and explore their data sources and silos of information without the need to move or transform data. Data consumers benefit from a 360° access to data to get fast answers to key business questions. By querying concepts instead of the tables, SQL queries are reduced in length and complexity significantly. The SQL Knowledge Graph seamlessly integrates with popular business intelligence tools so business analysts can focus on the business questions and derive deeper insights.
timbr is not a database. timbr is a platform used for creating virtual SQL Knowledge Graphs that enable semantic (ontology-based) graph capabilities on existing data engines (data warehouses and data lakes). The SQL Knowledge Graphs integrate data sources into a semantic data fabric queryable in SQL. timbr does not require to copy or transform data (no ETL operations), no new DBMS infrastructure and no new skills as required by graph databases.
A knowledge graph is a knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs are often used to store interlinked descriptions of objects, events or concepts with free-form semantics. Knowledge graphs use ontologies to put data in context via linking and semantic metadata providing a framework for data integration, unification, analytics and sharing.
They are also prominently associated with and used by Google, Bing, and Yahoo, and with question-answering services such as Google Assistant, Siri and Alexa. All these examples were developed with proprietary tools.
For organizations that look to benefit from knowledge graphs, the available solutions in the market require significant changes in their IT departments. This is due to the fact that most of the data in the world is stored in formats that are not compatible with the format in which data is stored in knowledge graphs, so data needs to be extracted from its current DBMS, transformed to a new format and loaded into a separate, suitable DBMS. Another reason being is that to use knowledge graphs, data engineers and consumers need to acquire news skills to model in OWL and query in SPARQL.
Different from most other solutions, the timbr SQL Knowledge Graph platform creates a virtual layer that works in standard SQL to seamlessly connect to existing databases and is implemented without requiring new skills.
Contact us to learn how timbr can help your organization join the knowledge revolution.
Gartner describes the data fabric architecture as the means of supporting “frictionless access and sharing of data in a distributed network environment.” To make this architecture work, it is necessary to implement a means to understand and assembly heterogeneous data by providing it with business meaning and flexibly integrating sources of any structural type. These features are characteristic of knowledge graphs that deliver rapid data discovery and integration across distributed computing resources.
The biggest obstacle for enterprises wishing to implement a data fabric, is the investment required for the transformation of existing data assets to suit the new architecture and in the acquisition of organizational skills necessary to create, manage and use this architecture. This is a complex, time-consuming, highly disruptive and expensive process with a long ROI.
Data virtualization solutions are a viable solution to this challenge but their use is costly because of the continuous maintenance required and because of the lack of relationship-rich semantic capabilities which are key for analytics that make use of dynamic data sources.
Knowledge representation is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets (Wikipedia).
Translated for the use of enterprises, knowledge representation provides the most efficient means for non-technical data consumers to access and retrieve data stored in databases (in the form of tables and columns for example), using abstract concepts that represent the real world (known as semantics in knowledge representation), such as “customer”, “product”, “employee”, “asset”, etc. This need arises from the fact that databases do not provide a means to use such concepts to give uniform meaning to data, because real world concepts such as “customer”, “product”, etc. are usually contained in several tables and columns or even multiple databases which are inaccessible to non-technical data consumers.
timbr enables knowledge representation and reasoning – a field of artificial intelligence, in SQL.
timbr’s AI features include:
- Logical concept/object compositions combining objects or data types into more complex ones.
- Contextual modeling that enables contextual adaptation required to construct models for classes of real-world phenomena.
- Composition of semantic relationships.
- A semantic reasoner that infers knowledge from semantic relationships.
How do external tools connect to timbr? Are providing any additional drivers (like JDBC) required in order to connect using different IDEs or custom applications?
Creating an ontology: You can either use our Visual Ontology Modeler (no SQL needed) or use timbr extended SQL DDL statements.
Mapping data to the ontology: You can either use our Visual Ontology Data Mapper (no SQL needed) or use timbr’s extended SQL DDL statements.
Querying the Knowledge Graph: SQL, Python/R, dataframes, and natively in Apache Spark (SQL, Python, R, Java, Scala). GraphQL can be supported by integrating external open source projects that support the translation of GraphQL to SQL.
Yes, timbr is compatible with OWL-DL and some OWL-2 inferences.
If there is a clear business value to add more OWL-2 inferences, we can support them as well. timbr’s inference engine is based on query-rewriting techniques. If timbr encounters slow queries/performance, timbr can specifically materialize the part of knowledge that is required.
Timbr offers a visual data mapper to manually or semi-automatically select tables and columns from the database, as well as the option for coders to conveniently use SQL DDL statements. timbr can filter, clean and transfer the data that is been mapped to the ontology. No need for ETL operations.
timbr allows creating virtual PKs for concepts (used as unique identifiers), and FK to PKs in the ontology (used as relationships between concepts). As long as the ontology author maps the physical tables PKs to the ontology PKs, client join will follow these declarations. In the ontology, you can create relationships between concepts using FK statements. In each relationship, you specify the properties in the ontology that represent the relationship (used for the JOIN).
Yes, timbr is accessible in JDBC/ODBC and the ontology can be created programmatically using timbr SQL DDL statements:
CREATE CONCEPT (extension of CREATE TABLE statement)
CREATE MAPPING (extension of CREATE VIEW statement)
In many cases, we build small scripts to generate parts of the ontology programmatically.