In this presentation, Hughes will introduce Multimodal GraphRAG, an innovative framework that brings image data to the forefront of graph-based reasoning and retrieval. By extracting meaningful objects and features from images, and linking them with text-based semantics, Multimodal GraphRAG unlocks new pathways for surfacing insights. From images embedded in documents to collections of related visuals, he will demonstrate how this approach enables more comprehensive understanding, amplifying both the depth and accuracy of insights.

Hughes will also be sticking around through Sunday, January 26th for select participants, co-leading an interactive Data Discussion on hyperdimensional computing (HDC) and neuromorphic cognitive computing with Amy Hodler, AI and Graph Analytics Program Manager at Neo4j, on “Hyperdimensional Horizons: Exploring Neuromorphic Intelligence and Graph Applications.”

For more information on the conference, check out the website here.

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This event is brought to you by The Content Wrangler and is sponsored by Heretto, a powerful component content management system (CCMS) platform to deploy help and API documentation in a single portal designed to delight your customers.

The post Fernando Aguilar Islas and Emily Crockett to Present Webinar on Content Graphs for Personalization appeared first on Enterprise Knowledge.

Graph Database as Primary Storage: All data and ontology is stored within the graph database, ingesting all relevant source data and enabling inference and reasoning capabilities.

Graph Database as Relationship Management and Taxonomy Integration: Relationships between core concepts and content metadata (like taxonomy tags on documents) are stored within the graph, but actual content and descriptive metadata are stored within other systems and are connected to the graph via virtualization.

Graph Data Model (Ontology) as Relational Data Schema: The ontology model is an Enterprise Relational Diagram (ERD) that sets the “vision” for how to connect and leverage data stored in a relational database.

Organizations can see the value of capturing relationships in a machine-readable way, even when not all of the data relevant to the use case is captured in a graph database. The model that makes sense for your organization and your use case is dependent on factors including: 

• Restrictiveness of source data systems; 
• Volume and scale of data;
• Enterprise architecture maturity; 
• Inference and reasoning needs; and
• Integration needs with downstream systems.

At EK, we design graph-based architectures in a way that leverages your organization’s specifications and conventions, while introducing best practices and standards from the industry. Looking to get started? Contact us.

The post Where Does a Knowledge Graph Fit Within the Enterprise? appeared first on Enterprise Knowledge.

Enterprise Search Engine Optimization

The first solution to fixing your search without an IT background is doing what I call “Enterprise Search Engine Optimization (ESEO).” Just like Google, enterprise search engines have their own search weighting algorithms to control the order of search results. Unlike Google, however, these algorithms are simple and rarely change. For example, each instance of your search term in the title of the document might be worth 100 points, the abstract worth 20, and the body worth 1 point. Moving an important term to the title would add 100 points to the relevancy rating. Find out how your search engine weighting is set up and you can begin to control the order of your search results. 

The best place to begin tackling this Enterprise Search Engine Optimization is by starting with the top 20 search terms. Enter the most popular search term and decide which results should be in the top 5. Then, correct the search order by adding the search term to the title or abstract. Lastly, re-execute the search and keep making corrections until you get the search result order you want. Repeat this process for the rest of the top 20 search terms and you will have a huge improvement in your customers’ search experience. The top 20 search terms in some intranets represent as much as 30% of the searches. Spending a couple of hours on ESEO each month will have a huge impact on the success of your search engine.

Best Bets

A similar solution to this problem involves using the best bets or promoted items feature of your search engine. While this is a brute force approach to fixing search, it can be a great way to ensure that specific content always appears first for important search terms. We approach the use of best bets functionality in the same way that we handle ESEO. Begin by looking at the list of the top 20 search terms. If one of these search terms has an obvious result that should always appear first, add it as a best bet. Most search engines let you do this by entering a search term and then pointing at the search result that should appear first.

Synonyms

One of the primary reasons that content is not appearing in search when it should be is due to the way that search is utilizing acronyms or abbreviations. This problem can be corrected using your search engine’s synonym/thesaurus functionality. Many search engines have an administrative screen for managing synonyms. Get access to this screen and create synonyms for your most common acronyms so that people who search, for instance, for “EK” also find “Enterprise Knowledge.” If the search engine does not have a screen that a business user can leverage, ask your IT person how you can send them a list of  synonyms so that they can load them into the search engine (I know this second option requires IT, but the business is still driving the results).

Keywords

Another reason that your search may not surface specific documents is because there is a problem with a document, rather than a term or abbreviation. There are some documents that should be found using a particular search term even if that term is not in the title or body of the document. In this case use the keywords field found in most content types. Keywords were once used as a replacement for taxonomies so that content could more easily be tagged. As organizations have matured their content models (adding metadata and taxonomies), the keywords field has become less important. These fields still exist in many content types and they tend to have a higher weighting in search relevancy calculations than the body or a piece of content. Add the search term to the keywords field and the document will begin to appear in the search results. If necessary, ask IT to increase the weighting on this field so that you have more control over how high the search result appears.

These four approaches to fixing search do not require IT and can make a huge difference in the accuracy of your search results. One of our clients implemented this process and saw the percentage of selected search results that were in the first three items jump from 12% to over 33% after just two weeks of tuning. You can do the same thing with your search. Additionally, by having all of the above recommendations be guided by a search governance plan, you can ensure that there is consistent decision-making that promotes the best interest of your end users. Are you interested in improving your current search environment either by implementing a new search engine or fixing the one that you are already using? Our search consultants can help you turn search from a point of frustration to the destination where users go to find the information they are looking for. Contact us at info@enterprise-knowledge.com.

The post Fixing Enterprise Search Without Changing Code appeared first on Enterprise Knowledge.

Joseph Kidd, former Manager of Cybersecurity Architecture and Analysis for a global telecommunications company, will discuss how graph databases can create value by generating insights about the relationships within data as well as communicate the structure of a network in an intuitive and memorable way in his presentation, “Cybersecurity Analysis with Graphs.” Kidd will also speak to how graph database technology has shown promise in being able to predict and minimize damage to assets on a network from a cyber-attack.

Interested in learning more about Enterprise Knowledge Graphs and Kidd’s experience working with graph databases? Sign-up on Enterprise Knowledge Graph’s Meetup Page.

The post Upcoming Enterprise Knowledge Graph MeetUp appeared first on Enterprise Knowledge.