Knowledge graphs represent an exciting evolution in data management that can transform how users retrieve information in the enterprise. Rather than relying solely on keywords and metadata, knowledge graphs rely on semantic relationships to connect relevant data points. This allows for more intuitive search experiences where users can query information conversationally.
Rather than simply returning documents that match keywords, knowledge graphs can understand the meaning behind natural language queries and provide direct answers. By mapping connections between data entities, these AI-powered systems can reason about relationships and derive insights. Knowledge graphs essentially create a brain-like web of enterprise data.
Conversational interfaces like chatbots offer the ideal modality for humans to access these rich knowledge repositories. Chatbots essentially function as an interactive search engine, translating text or voice queries into meaningful graph traversals. This allows users to tap into enterprise data through intuitive, natural conversations.
Knowledge graphs combined with conversational search stands to revolutionize how employees and customers find information. Enterprises can use these capabilities to improve knowledge sharing, customer service, and data accessibility. But thoughtfully designing the ontology and applying the technology does have its challenges. This article will explore the potential of knowledge graphs, best practices for implementation, and the future possibilities of AI-oriented enterprise search.
The Value of Enterprise Knowledge Graphs
Enterprise knowledge graphs represent a powerful way for organizations to put their internal data assets to work. By structuring and connecting information from across departments and databases, knowledge graphs enable deeper analysis and insights.
At their core, they are knowledge bases that use graph structures for data modeling. This allows relationships between different “nodes” to be defined, like how a customer is connected to specific products and services. The connections enable sophisticated queries across multiple domains.
For example, a knowledge graph could connect CRM data on customers and sales, marketing data on campaigns, and service data on support tickets. This would allow queries like “show all high-value customers who have opened our last 3 email campaigns but have an active service ticket.” This type of cross-domain analysis is extremely difficult without the connected structure of a knowledge graph.
The ability to rapidly analyze data across domains has immense strategic value. Such graphs help uncover hidden insights that live between siloed data sources. This can identify new opportunities.
Conversational Interfaces Improve Accessibility
Chatbots provide a more natural and intuitive way for users to access enterprise data and knowledge graphs. By enabling natural language conversations, chatbots remove the need to use rigid search interfaces or remember specific query syntax. This makes it easier for employees, customers, and other users to find the information they need.
Some key benefits of conversational interfaces include:
- Natural language understanding – Users can ask questions or make requests in everyday language. The chatbot interprets the intent and context to provide relevant results.
- Personalized responses – Chatbots can have personalized conversations at scale, providing customized information for each user.
- 24/7 availability – Chatbots are always on and ready to respond, enabling users to get immediate answers any time of day.
- Process automation*- Chatbots can not just provide information, but take action on users’ behalf, such as filling out forms or triggering workflows.
- Multi-modal interfaces – Chatbots can understand and respond to different modes of conversation, including text, voice, images, and more.
- Contextual awareness – Chatbots can keep track of long conversations, understand context, and provide recommendations based on what they know about the user and previous interactions.
By implementing a conversational interface on top of their knowledge graphs, enterprises can make organizational knowledge more accessible and usable for a broader range of users. This helps drive increased engagement and satisfaction.
Use Case: Service Chatbot
Many customer service organizations are implementing conversational AI chatbots to improve the customer experience and increase efficiency. One compelling example is the chatbot built by a major credit card provider to handle common customer inquiries.
This chatbot is accessible 24/7 on the company’s website and mobile app. It uses natural language processing to understand customer questions and provides helpful answers by referencing the company’s knowledge base.
The chatbot can handle a wide range of customer needs like checking account balances, reviewing recent transactions, reporting lost cards, and more. It uses the credit card company’s repositories of customer data and service policies to directly resolve issues whenever possible.
If the chatbot reaches the limit of its knowledge base, it effortlessly escalates the conversation to a live agent. This ensures customers always get the support they need.
Since launching the chatbot, the credit card provider has seen a 30% decrease in calls to its customer support center. The chatbot can independently handle 500,000 customer conversations per month.
This has dramatically improved efficiency and freed agents to focus on more complex issues. Customers get quick answers 24/7 instead of waiting on hold or exchanging emails. The chatbot delivers a smoother, more satisfying customer experience.
This service chatbot example demonstrates the immense value of applying conversational interfaces to unlock enterprise knowledge. Companies gain huge benefits in customer engagement, employee productivity, and leveraging institutional knowledge.
Use Case: HR Chatbot
Human resources departments often hold a vast trove of internal knowledge – from employee records and org charts to policies, procedures, and training materials. While this information is incredibly useful, it can be difficult for employees to find answers to their HR questions. This reduces productivity as workers spend time searching or waiting for help from HR staff.
To improve accessibility, leading companies have deployed HR chatbots on their intranets. These bots serve as a natural language interface to internal HR databases. Employees can simply ask questions in plain language to get instant answers.
For example, a worker might ask “How do I add my spouse to my health insurance?” The HR chatbot understands the intent and responds with step-by-step instructions from the knowledge base. Or an employee preparing for retirement can inquire “What is the policy for unused PTO payout when leaving the company?” The bot can parse the question and provide the relevant policy details.
HR chatbots deliver immense value by allowing employees self-serve access to important HR information. This reduces the burden on HR staff while empowering workers to get the knowledge they need, when they need it. Advanced natural language processing and machine learning algorithms enable the bots to handle a wide range of questions and continually improve their capabilities over time.
As a result, companies using AI-powered HR chatbots report increased employee productivity, satisfaction, and retention. The bots essentially serve as always-available virtual HR assistants, connecting employees to the right information in the moment. This showcases the immense potential of knowledge graphs and conversational interfaces within the enterprise.
Building a Knowledge Graph
A knowledge graph connects relevant data from across an organization into a network of relationships, enabling intelligent responses to natural language queries. Building an effective knowledge graph involves:
- Identifying key domains of knowledge and subject matter experts to consult. What information does the business need quick access to?
- Auditing existing data sources like databases, documents, archives. Structured and unstructured data can provide nodes.
- Defining connections between data points based on meaning, not just keywords. This contextual graph facilitates discovery.
- Leveraging AI like machine learning and NLP to extract insights. Continuously enrich the graph with new data.
- Developing a flexible data model that can ingest diverse sources into a connected structure. Balance breadth and depth of knowledge.
- Curating the graph with high-quality nodes and relationships validated by SMEs. Precision over quantity avoids misleading paths.
- Enabling conversational access through chatbots and virtual assistants. Natural language queries traverse the graph.
- Optimizing for specific use cases and questions to enhance utility. User testing refines the model.
- Maintaining data governance and access controls for security. Balance open knowledge with privacy.
- Monitoring usage patterns to identify knowledge gaps and increase coverage. Expand high-value domains.
- Integrating into workflows to provide relevant, contextual answers during tasks. Deliver knowledge seamlessly.
The end result is an intelligent knowledge network that makes an organization’s proprietary information easily discoverable through natural conversation. Employees gain quick access to institutional knowledge that can increase productivity, decision making, and customer satisfaction.
Challenges and Limitations
Enterprise knowledge graphs provide immense value, but they also come with unique challenges. One of the biggest obstacles is the difficulty of modeling complex queries in a natural language interface.
Users will often ask questions that require understanding of nuance, context, and domain-specific knowledge. It can be hard for chatbots to interpret these queries correctly and provide the most relevant information. For instance, an HR chatbot may struggle to comprehend complex questions related to company policies, benefits eligibility, or legal regulations.
The knowledge graph itself can also get very large and unruly. As more data sources are ingested, it becomes exponentially harder to connect all the entities and keep information updated. There will inevitably be gaps in coverage as new content is generated faster than the knowledge graph can expand.
Another key limitation is how pricky natural language interfaces are. If users phrase queries differently than the bot expects, it often fails to understand. Extensive training data is required so chatbots can handle a wide range of linguistic variations.
Overall, enterprise knowledge graphs provide game-changing improvements in discoverability and accessibility. But they require continued maintenance, refinement, and oversight to handle complex enterprise searches at scale. Companies must invest in knowledge engineering and AI training to maximize the value.
Emerging Best Practices
As more companies implement enterprise knowledge graphs and chatbots, some best practices are beginning to emerge around avoiding common pitfalls:
- Start small, then expand – Don’t try to build an enterprise-wide knowledge graph from the start. Focus first on a limited domain or use case. Once the initial system is running smoothly, expand the scope.
- Involve subject matter experts – Domain experts across the business should be closely involved in planning schema, defining intents, providing training data, and evaluating results. Their expertise is key.
- Rely on existing data – Build the knowledge graph using structured data from existing databases, content management systems, and other repositories. Avoid having to create data from scratch.
- Focus on precision over recall – It’s better for chatbots to return fewer, highly precise results rather than more results of mixed quality. Users will lose trust in inaccurate answers.
- Enable conversational refinement – Allow users to refine queries by asking follow-up questions. Add capabilities like “Any other options?” to prompt the bot for more results.
- Plan for change – Expect to iterate on the ontology, knowledge graph, and chatbot capabilities. As use cases expand, the system will need to adapt. Build in agility.
- Monitor and assess – Analyze chatbot conversations to identify areas for improvement. User feedback and testing will point the way to enhancements.
By starting small, involving experts, and focusing on precision, companies can overcome common stumbling blocks. With iterative refinement, enterprise knowledge graphs and chatbots hold great promise for unlocking proprietary knowledge.
The Future of Enterprise Search
Enterprise search powered by knowledge graphs and conversational AI is still in its early days, but rapid advancements are being made. Here’s where this innovative technology is likely headed next:
- Deeper Personalization and Context – Future enterprise search systems will understand not just the user’s query but their role, past behavior, and current context. This will enable the results to be highly personalized and relevant. For example, an engineer may get technical documentation while a sales rep gets customer-facing materials.
- Multi-modal Interfaces – Voice-only interfaces will be augmented with other modes like images, videos and interactive elements. This will allow for a richer, more intuitive search experience. Users may share a photo of a piece of equipment and ask the chatbot for documentation.
- Tighter Integration with Business Systems – Knowledge graphs will interconnect with multiple internal systems – from product databases to CRMs – enabling users to not just find information but take action. Reps may be able to create support tickets or order parts directly from the chatbot.
- Proactive Recommendations – Instead of waiting for user queries, enterprise search bots will proactively suggest content, connections and actions based on situational awareness. For example, the bot could highlight relevant troubleshooting guides when production line issues arise.
- Expanded Knowledge Domains- Enterprise knowledge graphs will expand beyond technical documentation and internal data to encompass external sources, news, regulations, market dynamics and more. This will allow the search system to meet a wider array of business needs.
- Continuous Improvement – With user feedback loops, contextual understanding and AI, enterprise search bots will continuously improve their knowledge, relevance and utility over time. They will learn to better serve each user and organization.
Enterprise search stands to become far more powerful as knowledge graphs tap into proprietary corporate knowledge and conversational interfaces make it accessible. With personalization, integration and proactivity, these AI systems will transform how organizations access information to drive productivity, growth and innovation.
In summary, enterprise knowledge graphs and conversational interfaces like chatbots have the potential to revolutionize how businesses organize information and enable employees to access it. By structuring proprietary data into knowledge graphs, companies can map relationships between entities to uncover new insights. Chatbots then provide a natural language interface for retrieving this knowledge on demand.
When thoughtfully implemented, these AI-powered systems create more intuitive, efficient experiences for knowledge workers. Rather than hunting through documents or asking a colleague, employees can simply have a conversation with a bot to get answers. This saves time and democratizes access to expertise.
While building enterprise knowledge graphs is still an emerging practice, early adopters have seen dramatic improvements in workplace productivity and employee satisfaction. As the technology matures, knowledge graphs and conversational interfaces will likely become a standard component of the enterprise tech stack.
The future is bright for platforms that combine knowledge graphs and chatbots to deliver powerful enterprise search. With the right strategy, any company can leverage these innovations to connect people with knowledge in entirely new ways. This has the potential to drive business growth, empower workers and ultimately realize competitive advantages.
