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AI

How NASA is using knowledge graphs to find talent

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One of NASA’s biggest challenges is identifying where data science skills reside within the organization. Not only is data science a new discipline – it’s also a fast-evolving one. Knowledge for each role is constantly shifting due to technological and business demands.

That’s where David Meza, acting branch chief of people analytics and senior data scientist at NASA, believes graph technology can help. His team is building a talent mapping database using Neo4j technology to build a knowledge graph to show the relationships between people, skills, and projects.

Meza and his team are currently working on the implementation phase of the project. They eventually plan to formalize the end user application and create an interface to help people in NASA search for talent and job opportunities. Meza told VentureBeat more about the project.

VentureBeat: What’s the broad aim of this data led project?

David Meza: It’s about taking a look at how we can identify the skills, knowledge and abilities, tasks, and technology within an occupation or a work role. How do we translate that to an employee? How do we connect it to their training? And how do we connect that back to projects and programs? All of that work is a relationship issue that can be connected via certain elements that associate all of them together – and that’s where the graph comes in.

VentureBeat: Why did you decide to go with Neo4j rather than develop internally?

Meza: I think there was really nothing out there that provided what we were looking for, so that’s part of it. The other part of the process is that we have specific information that we’re looking for. It’s not very general. And so we needed to build something that was more geared towards our concepts, our thoughts, and our needs for very specific things that we do at NASA around spaceflights, operations, and things like that.

VentureBeat: What’s the timeline for the introduction of Neo4j?

Meza: We’re still in the implementation phase. The first six to eight months was about research and development and making sure we had the right access to the data. Like any other project, that’s probably our most difficult task – making sure we have the right access, the right information and thinking about how everything is related. While we were looking at that, we also worked in parallel on other issues: what’s the model going to look like, what algorithms are we going to use, and how are we going to train these models? We’ve got the data in the graph system now and we’re starting to produce a beta phase of an application. This summer through the end of the year, we’re looking towards formalizing that application to make it more of an interface that an end user can use.

VentureBeat: What’s been the technical process behind the implementation of Neo4j?

Meza: The first part was trying to think about what’s going to be our occupational taxonomy. We looked at: “How do we identify an occupation? What is the DNA of an occupation?” And similarly, we looked at that from an employee perspective, from a training perspective, and from a program or project perspective. So simply put, we broke everything down into three different categories for each occupation: a piece of knowledge, a skill, and a task.

VentureBeat: How are you using those categories to build a data model?

Meza: If you can start identifying people that have great knowledge in natural language processing, for example, and the skills they need to do a task, then from an occupation standpoint you can say that specific workers need particular skills and abilities. Fortunately, there’s a database from the Department of Labor called O*NET, which has details on hundreds of occupations and their elements. Those elements consist of knowledge, skills, abilities, tasks, workforce characteristics, licensing, and education. So that was the basis for our Neo4j graph database. We then did the same thing with training. Within training, you’re going to learn a piece of knowledge; to learn that piece of knowledge, you’re going to get a skill; and to get that skill, you’re going to do exercises or tasks to get proficient in those skills. And it’s similar for programs: we can connect back to what knowledge, skills, and tasks a person needs for each project.

VentureBeat: How will you train the model over time?

Meza: We’ve started looking at NASA-specific competencies and work roles to assign those to employees. Our next phase is to have employees validate and verify that the associated case — around knowledge, skills, abilities, tasks, and technologies — that what we infer based on the model is either correct or incorrect. Then, we’ll use that feedback to train the model so it can do a little bit better. That’s what we’re hoping to do over the next few months.

VentureBeat: What will this approach mean for identifying talent at NASA?

Meza: I think it will give the employees an opportunity to see what’s out there that may interest them to further their career. If they want to do a career change, for example, they can see where they are in that process. But I also think it will help us align our people better across our organization, and we will help track and maybe predict where we might be losing skills, where we maybe need to modify skills based on the shifting of our programs and the shifting of our mission due to administration changes. So I think it’ll make us a little bit more agile and it will be easier to move our workforce.

VentureBeat: Do you have any other best practice lessons for implementing Neo4j?

Meza: I guess the biggest lesson that I’ve learned over this time is to identify as many data sources that can help you provide some of the information. Start small – you don’t need to know everything right away. When I look at knowledge graphs and graph databases, the beauty is that you can add and remove information fairly easily compared to a relational database system, where you have to know the schema upfront. Within a graph database or knowledge graph, you can easily add information as you get it without messing up your schema or your data model. Adding more information just enhances your model. So start small, but think big in terms of what you’re trying to do. Look at how you can develop relationships, and try to identify even latent relationships across your graphs based on the information you have about those data sources.

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Categories
AI

Why knowledge graphs are key to working with data efficiently, powerfully

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This post is by Dr. Mukta Paliwal, senior domain expert at Persistent Systems.

As many as 50% of Gartner client inquiries on the topic of artificial intelligence involve a discussion involving the use of graph technology, the market research firm said in its Top 10 Data and Analytics Trends for 2021. Every large enterprise wants to exploit available data to bring more insights for doing business at scale. To achieve this, connected data has become a logical need, as it helps in bringing context within the existing organizational data to create knowledge.

Businesses have to face the pace of constantly evolving data needs. Knowledge graphs can help companies move away from traditional databases and use the power of natural language processing, machine learning, and semantics to better leverage data.

What is a knowledge graph?

Knowledge graphs represent a collection of interlinked facts about a domain. Essentially, entities and relations are extracted from the unstructured data and stored in the form of a triple: subject-predicate-object. For example, the statement “Captain Marvel is the strongest Avenger” can be broken into a subject (Captain Marvel), a predicate (is the strongest) and an object (Avenger) and stored as a triple (Captain Marvel-is the strongest-Avenger) along with other related entities in a knowledge graph of Avengers, the popular Marvel movie characters.

Essentially, we can define knowledge graphs with these features: 1) they define real-world entities of a domain; (2) they provide relationships between them; (3) they define rules for possible classes of entities and relations via some schema; (4) they enable reasoning to infer new knowledge.

Knowledge graphs can be auto-generated or human-curated, may have been designed with a rigid ontology or may be evolving with time, can be in different shapes and sizes, and may have been developed by a company or by an open-source community. Irrespective of these differences, they help in organizing unstructured data in a way that information can easily be extracted where explicit relations between multiple entities help in the process.

Why use knowledge graphs? 

A knowledge graph is self-descriptive, as it provides a single place to find the data and understand what it is all about. As the meaning of the data is encoded alongside the data in the graph itself, the word semantics is associated with the knowledge graph. Knowledge graphs bring additional value by providing:

  • Context: Knowledge graphs provide context to algorithms by integrating various types of information into an ontology and flexibility to add new derived knowledge on the go. Most traditional knowledge graphs can simultaneously use various types of raw data.
  • Efficiency: Once desired entities and relations are available, knowledge graphs offer computational efficiencies for querying stored data resulting in effective use of data for generating insights.
  • Explainability: Large networks of entities and relations provide solutions for the issue of understandability by integrating the meaning of entities available within the graph itself. As such, knowledge graphs become intrinsically explainable.

Where to use knowledge graphs

According to Gartner’s Top 10 Data and Analytics Trends for 2021,  knowledge graphs are the foundation of modern data and analytics, with capabilities to enhance and improve user collaboration, machine learning models, and explainable AI. Although graph technologies are not new to data and analytics, there has been a shift in the way they are used. A knowledge graph brings together machine learning and graph technologies to give AI the context it needs.

To solve complex problems, where there is a need to integrate multiple unstructured and semi-structured sources of data coming from a variety of sources, we need connected, reusable, and flexible data foundation to reflect the complexity of the real world. Connected data, enriched with meaning, allows for multiple interpretations from the same data, which is helpful in getting answers to complex queries to derive insights with more efficiency.

Organizations are identifying an increasing number of use cases for knowledge graphs, including:

Fraud detection: Identifying fraudulent transactions is the most prevalent use case and has applications in banking, mobile phone transactions, government benefits and tax fraud. The use of knowledge graphs also enhances fraud, waste, and abuse detection on insurance claims. Knowledge graphs empowered by machine learning and reasoning capabilities allow companies to better identify fraudulent patterns by traversing many real-time interconnected entities in a large network.

Drug discovery: Drug discovery is an extremely complex and cost-intensive process. Knowledge graphs have shown considerable promise across a range of tasks, including drug repurposing, drug interactions, and target gene-disease prioritization. A large number of open- source databases are integrated along with published literature to create huge biomedical knowledge graphs. These KGs have become very helpful in mining the relations between entities like genes, drugs, disease, etc. and use them in downstream applications.

Semantic search:  A knowledge graph stores meanings of the entities; hence, knowledge graph-powered search is referred to as “semantic search,” or search enriched with meaning. Semantic search is used to improve the accuracy of search results when exploring the internet or the internal systems of an organization. For semantic search to work, along with a well- curated knowledge graph, the capabilities of text analytics and indexing techniques are used.

Recommender systems: Recommender systems are developed to model users’ preferences for personalized recommendations of products. There are a variety of modeling techniques used to develop the recommendation system. In spite of their considerable merit, these systems suffer from such challenges as data sparsity, cold start, and expandability of the recommendations. Knowledge graph-based recommender systems are able to help solve these challenges to an extent. In this approach, user and item entities are connected through multiple relationships. The relations are used to obtain a probable candidate list for the target user, and the path between target user and recommended item is used as an explanation for recommended items.

Mukta Paliwal Ph.D. is Senior Domain Expert (Data Science) at Persistent Systems. She leads and consults with teams to create and deliver cutting-edge software solutions based on AI/ML in multiple business domains. She has a Ph.D. in Applied Machine Learning.

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Repost: Original Source and Author Link