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Taking the Right Approach to Building A Spend Taxonomy

The power of data is indisputable and so is the challenge posed by poor quality data to procurement organizations that are turning increasingly to data for spotting patterns, predicting spend, and improving processes and procurement performances. Good data is the key to spend analysis and one of the cornerstones of good data in the context of spend analysis is structure.

Semantic Artificial Intelligence Trends That Could Shape the Future of Enterprise Automation

Semantic Artificial Intelligence (AI) emerged as a result of the limitations in Machine Learning (ML). However, it has grown from there to drive semantic search engines with the help of ontologies, semantic extraction using an ontology-based knowledge model, and chatbots infused with the domain knowledge of an organization. But, where is semantic AI heading and how will it impact enterprise automation?

Enhancing Drug Discovery with Knowledge Graphs

Data for drug discovery and healthcare is often trapped in silos. This hampers effective interpretation and reuse. An antidote is to link data both internally and externally and create a Findable, Accessible, Interoperable, and Reusable (FAIR) data landscape that can power semantic models and knowledge graphs.

A Knowledge Discovery Framework for Analyzing COVID-19 Related Research Quickly

Scientists and clinicians have responded to the rapid spread of COVID-19, by generating new research materials. This has given rise to two unique challenges. One, it is humanly impossible for scientists and clinicians to review the vast amount of new research on time. Two, there is no way to establish the quality of these preprint materials as they have not undergone peer-review.

Substitute Frameworks for Topic Cluster Models

A topic cluster is a content taxonomy method that uses a single page as a hub for many posts. The single page can be visualized as a pillar, with many posts clustering around it. A topic cluster offers many benefits such as helping to organize an internal content calendar and associating different high-level entities to a brand. But, is this model beneficial for all enterprises that are deploying content marketing at scale?

A Field to Fork Ontology

FoodOn is a consortium-driven project for building a comprehensive and easily accessible global farm-to-fork ontology that accurately and consistently describes different kinds of food commonly known in cultures from around the world. The ontology addresses the gaps in food product terminology and supports food traceability.

A Semantic Search Interface for Exploring COVID-19 Scientific Literature

Keyboard based approaches employed by traditional search engines lack the retrieval precision needed to identify relevant evidence from the corpus of COVID-19 scientific literature. To overcome this challenge, Google has launched the COVID-19 Research Explorer, an interface on top of the COVID-19 Open Research Dataset (CORD-19).

Amazon Releases Kendra to Solve Enterprise Search Challenges with Artificial Intelligence and Machine Learning

The overarching goal of all in-house enterprise search engines is to operate like Google. Unlike Google, enterprises have substantially less amount of content to work with. This lack of content is at the root of all enterprise search engine challenges because more the data more Google-like response. Many enterprises are attempting to overcome the challenges posed to enterprise search by using today’s technology, and Amazon is one among them.

Getting Taxonomy Work done during the Pandemic

Now that information, knowledge, and content professionals are currently working from home, can taxonomy design and development be done remotely? The answer is a big yes. Even though it is always beneficial to have in-person time, taxonomy work can be done remotely.

Reaping the Benefits of Artificial Intelligence through Ontology

Most of the Artificial Intelligence (AI) projects that were supposed to help enterprises use data to improve customer service, reduce costs, and speed the core processes that provide competitive advantage have failed. The reasons are many, but the primary reason is that enterprise data was not properly prepared for AI.