Dave Clarke gave a keynote at KMWorld 2021 on Enterprise Auto Categorization. As taxonomies evolve into more ontologies what is the impact on enterprise auto categorization? Dave explored some alternative methodologies and tools for categorizing content against large scale ontologies as well as smaller enterprise taxonomies.

Good governance manages risk and sustains ongoing value, ensuring program goals are met. Ahren Lehnert presented taxonomy lifecycle management as part of the taxonomy governance process, including both the lifecyle of individual concepts as well as the overall versioning and archiving of entire vocabularies.

An ongoing challenge in knowledge management is categorizing existing and newly created content so it is findable. Knowledge should not only be findable, but should lead to the serendipitous discovery of additional, related content. Lehnert presented a Ubisoft case study in which ontologies and taxonomies tie together disparate business systems and content throughout the enterprise KM information ecosystem using common metadata. Ahren Lehnert covered the ontology management features required to deliver controlled vocabulary workflow and governance to support an enterprise-scale deployment and IA at Ubisoft.

The far-reaching and perhaps optimistic realization of the semantic web has been called into question, especially the further we are removed from its original proposal. Among the barriers are technical limitations, but mostly the realization of the dream confronts the reality of the sheer scale of the World Wide Web and lack of consensus on meanings and standards. The semantic web may be thwarted by the disintegration of common protocols in favor of nationalistic, idealistic, and technological separation referred to as the “Splinternet.” Ahren Lehnert looked at ways it is working, even if those ways are not what was originally envisioned. Ahren discusses how ontologies and knowledge graphs are growing in popularity and use within organizations to get to the vision of a semantic web.

The scale and complexity of enterprise taxonomies have accelerated over the last few years. Taxonomy teams are facing new tooling and business challenges that push the limits of traditional theory and practices. As taxonomy programs become more geared towards shaping product strategy, taxonomy tooling must evolve to keep pace. Since licensing Synaptica’s taxonomy software, Meta’s (formerly Facebook) unique taxonomy needs have driven the development of several new KMS features and tools. Bonnie Bowes, Facebook and Bob Kasenchak, Synaptica described the management and mapping of large taxonomies and some of the customizations developed to support new workflows and business cases.

Well-developed taxonomies that are rich in synonyms make search results more accurate and are an ideal tool for content classification. While classifying content and improving search remain the bedrock use-cases for taxonomy, enterprises are adopting more sophisticated ontological knowledge models, thereby expanding use cases beyond information retrieval. Taxonomies are now being developed to manage business operations and processes and can represent a logical model of how a business conducts its operations. For this talk, Dave Clarke walked through taxonomy’s new role, providing examples that include product information and supply chain management and transactional process flow in service-based businesses.

Organized and hosted by our partners, Ontotext. Dave Clarke explored Semantic Knowledge Organization Systems. What are Knowledge Organization Systems (KOS), and what is a semantic KOS? What is the difference between a taxonomy and an ontology? When is it best to use SKOS versus OWL? This talk will define the terminology and general principles of KOS, and then illustrate practical tools and methodologies that may be used to design, create, govern, publish and integrate KOS with enterprise search and content management.

Is taxonomy out and ontology in? Are ontologies cooling off and knowledge graphs heating up? To take advantage of advances in information and data science, and to avoid reworking designs to fit the latest hype cycle, it is important to have a coherent strategy for building a knowledge-based enterprise from the foundations up, one step at a time.

Watch the Keynote on YouTube from Dave Clarke, CEO & Founder, Synaptica with Atanas Kiryakov, CEO Ontotext.

Knowledge graphs are not a plug-and-play solution. The implementation of a knowledge graph is equal parts art and architecture involving business and technical teams. An overall knowledge graph strategy should be created, the underlying ontology must be modelled and developed, data and content sources identified, and systems integrated. In this workshop, learn to define the strategic and technical components which need to come together to successfully implement a knowledge graph.

Watch the KMWorld mini-workshop on YouTube from Bob Kasenchak and Ahren Lehnert, Senior Managers at Synaptica.

Organizations with multiple vocabularies often wish to map them to one another or to external vocabularies for knowledge management, information retrieval, content strategy, and other uses. This is not as simple as it seems and can represent a great deal of manual effort. What does it mean to map terms? What are the different ways to express, for example, exact and near topical matches?

Watch the talk on YouTube from Bob Kasenchak, Senior Manager, Client Solutions, Synaptica discusses strategies and options for mapping and crosswalking taxonomies including models, techniques, and tools.

Ahren Lehnert, Senior Manager, Synaptica discusses how wikis are a great way to share knowledge and are an active practice in many KM scenarios. Wiki content, being textual, is only semi-structured and requires metadata tagging and analysis to surface useful information. While many wiki platforms include content tagging options, these concepts are typically limited to categorizing content and aiding search. What if wiki content could be supplemented with ontologies and linked data and made part of a larger knowledge graph?

Watch on YouTube about an ontology, a wiki, and linked data who walked into a bar and walked out dreadfully sober and clear.