The Maana Knowledge Graph
The Knowledge Layer that powers enterprise decision flows
The core of the Maana Knowledge Platform™ is the patented Maana Knowledge Graph™. Unlike traditional “semantic” systems, the Knowledge Graph does not rely exclusively on ontologies and description logics. Instead, the Knowledge Graph separates the structure of data from the content itself. This separation provides a fluidity of modeling, allowing data from any source and of any format to be seamlessly integrated, modeled, searched, analyzed, operationalized and re-purposed.
Each resulting model is a unique combination of three key ingredients – subject matter expertise, relevant data from silos, and the right algorithm – all of which are instrumental in optimizing assets and decision flows.
The first knowledge graph of its kind, the Maana Knowledge Graph™ is also dynamic, which means that it can represent both data and computational models. Users can perform complex transformations and calculations at interactive speeds – making it a game-changing technology for agile development of knowledge applications.
Maana has over 8 Knowledge Assistants modules such as SimilarityAssist™, DocAssist™ and JoinAssist™. These user-guided, machine-assisted Knowledge Assistants enable data scientists and technical experts to collaborate and rapidly create new iterative knowledge models for optimizing assets and processes.
Guides users in finding non-obvious relations, even when the user does not fully understand their data relationships. Often users have a good understanding of the available data, however the effort to manually connect it all on large data models makes the job difficult and time consuming. Maana provides a user-guided, machine-assisted mechanism for automatically building the relevant joins through a visual interface and little manual intervention.
Empowers users to identify instances, cases, events or records similar to the one they would like to investigate. For example, when looking for unprofitable insurance contracts, companies won’t be limited to searching for fixed attributes like high claims. Instead, they can simply use SimilarityAssist to pinpoint those contracts that are most similar to a sample unprofitable contract. Its guiding algorithms search across several dimensions (performance, economic, competitive, etc.) to identify similarities in the context of what the user is trying to solve.
Enables extraction of targeted knowledge in the relevant context from unstructured documents, like PDF or word files, emails, or images. For example, a large amount of data is usually stored in tables or hidden in unstructured documents like PDFs or Word files. Maana’s Knowledge Platform uses machine learning to extract structure from the documents as well as domain specific terminology that can be labeled for further training of the machine learning model.
Data are often spread over multiple files, parts or sources, making it hard and time consuming to get to more engaging phases of solution development. You can now interactively discover and securely acquire the relevant data for use within Maana. SourceAssist also helps with the challenge of constantly changing, growing data by leting you configure how Maana deals with repeated or periodic acquisition of the same content.
Building models at speed requires quick and easy access to available components. SearchAssist allows you to browse all data indexed in Maana and easily create or modify models by dragging and dropping results into a workspace. An intuitive search experience ensures that as you type, results will be continuously updated to match what is being typed. Results are displayed in order based on a MaanaRank™ where results with a higher ranking are displayed first.
Visually build knowledge models – fundamental building blocks for Knowledge Applications – from pertinent problem-questions; “Given a vessel, omitted port, and date, what are the set of alternative port options ranked by score?”
You can search concepts, bring them into the workspace, define relationships between them and apply appropriate functions in order to digitize your subject-matter expertise.
As you build out your Knowledge Application, you can visualize all knowledge graph elements in your workspace and explore all connected elements, including data sources. This not only helps you get an overview of how the different models are fitting together to form the desired solution, but also provides the level of transparency required to explain how the models are working to answer specific questions.
As you build knowledge models, you need to be able to view the actual data represented by them. PresentationAssist provides you with the ability to view information as tables. You can also change visualization types, edit the presented data, and provide inputs to get the answer (output) to a specific question (e.g., which units had the most spare parts replacement last quarter).
Maana enables business users and domain experts to utilize Maana’s pre-built Knowledge Applications or easily develop their own. Enabling employees to make better and faster decisions. These applications provide recommendations aimed at optimal decision making and learning for business processes like supply chain, call center, accounts receivables or optimize asset performance through predictive maintenance and more.
Proactive Financing for Customer Upsell
Identify a most desirable subset of current customers for targeted marketing campaigns to offer them a line of credit
Learns and Adapts
Once Maana’s Knowledge Graph recommendations are operationalized via the Knowledge Applications, the models continue to learn and adapt from the actions and feedback of subject-matter experts. This is another key advantage of having flexible models as they can be easily modified when needed. The user doesn’t have to start from scratch and can take advantage of prior work.
To solve a complex business problem, the first step is to define the problem in the form of precise problem-questions. Maana enables you to represent these problem-questions in the platform in the form of knowledge models.
Building these knowledge models is fundamental in getting to the right answers that solve business problems. It requires collaboration between the business leaders, subject matter experts, data scientists and business analysts so that the problem is truly understood in the proper business context and defined in commonly used terms.
Visualize and Configure
Once the precise question has been defined, Maana’s rich, graphical UI can be used to model relevant business concepts and represent their inter-relations as a knowledge model.
Knowledge models are declarative in nature, in that they do not tell the user how to answer the question, but rather focus attention on what needs to be answered next. This helps define further questions without getting too deep on the algorithms and data.
Compose and Reuse
Once built, all models are connected to form an intelligent, relationship driven, easily searchable and scalable semantic Knowledge Graph™, which is at the core of the Maana platform. The benefits don’t end once you create your initial Knowledge Graph. The models contained in it can be reused as is or modified and re-applied to another problem within the organization or across the enterprise.
IT organizations should be able to integrate and extend any new, enterprise wide platform to their current technologies. The knowledge platform can support traditional 3rd party ETL, data wrangling, visual analytics, dashboards etc. through plug-ins, libraries and REST APIs.
Maana supports access to Python and R modules in an interactive environment as well as via a command line interface. Jobs can be run in a distributed environment for quick results. By using knowledge assistants in the platform and by leveraging external libraries, problems can be solved using the best tool available for the job.
Knowledge exists in many forms which is required to support any backend system, such as Google’s TensorFlow, gridMathematica, or even a proprietary system through a pluggable interface. Users should also have access to the right tool for each job. Whether it is a Python-based optimization package or a complex, deep learning network, fluid integration and composition is necessary and available.
Search and Explore
Maana’s patented Semantic Search enables users to find the most relevant data and knowledge inputs from across silos in the context of optimizing an asset or process. Once these knowledge inputs are indexed in the knowledge graph, this patented technology suggests and completes user queries using domain-specific knowledge from the knowledge graph.
By entering a keyword or phrase, the user is presented with a disambiguation dialog that provides a ranked and filtered view of all knowledge in the graph. Once the user guides the knowledge graph on the relevancy of the suggestions, data is presented using rich, interactive visualizations for subsequent refinement and action.