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Ontology in Artificial Intelligence Powered Digital Transformations Projects

Robust information architecture is needed to support sophisticated solutions that are emerging as enterprises move forward with their digital transformations. Ontologies provide the re-usable, adaptive structure for enterprises that want to power their Artificial Intelligence (AI) initiatives. There are two approaches enterprises can take to power AI. One is the machine learning approach and the other is through ontologies.

Improving user experience through card sorting and tree testing

The category taxonomy serves as the backbone of a social marketplace for buyers and as a structure to which vendors assign their products. However, along with the top-level categories and the taxonomy segmentation for the website, the category taxonomy was tuned more for the company, leaving users – buyers and vendors – with poor user experience.

Leveraging Ontology in the fight against COVID-19

Integrating the mass of growing and constantly changing data generated by the multiple disciplines – from immunochemistry to behavioral population modeling – involved in fighting the COVID-19 pandemic is one of the biggest challenges faced by researchers and public health officials.

The relevance of the Five Laws of Library Science to Taxonomy Design

A common error while designing a taxonomy is the use of overtly specific language that does not align with the content. This error leads to two consequences. One, most of the content contained in the information environment cannot be mapped to the taxonomy, and two, the terms used in the taxonomy are not understandable for the end-users. Applying the Five Laws of Library Science when designing a taxonomy may help overcome this challenge.

How Taxonomies Enable Intelligent Search

Enterprise search engines have made retrieving information from humongous databases simple. Obtaining contextually relevant results from enterprise search engines, however, remain a challenge. A taxonomy can enable enterprise search engines to deliver relevant results by enabling entity extraction, facilitating faceted search, and enriching the vocabulary.

Knowledge Graphs-Enabling Machine Learning Enhance Artificial Intelligence

The future of Artificial Intelligence (AI) and machine learning is intertwined with knowledge graphs. Knowledge graphs can connect with and contribute to machine learning for better AI.

Creating Digital Collections Rich in Connections through Knowledge Graphs

Digitization is helping galleries, libraries, archives, and museums provide more information about every artifact in their collections. Digital artifacts, and written records of things, places, and people, however, need to evolve into high-quality machine-readable data if it is to fulfill, the need to know more about the world of cultural heritage. Knowledge graphs can help bridge this gap.

Looking at Concept Models as Business Ontologies

A concept model is about capturing business knowledge and disambiguating business communications. When a concept model is created, a special kind of business blueprint – one crucial for information architecture, business analysis, and knowledge management – is also being designed.

An Open Source Platform Enabling Life Sciences Researchers Describe, Store, and Retrieve Plant Data

There are relatively very few technical platforms trying to bridge the gap between what researchers need to do to meet their FAIR responsibilities – ensuring research data is Findable, Accessible, Interoperable, and Reusable – and how they need to do it. One such technical platform is Collaborative Open Plant Omics (COPO).

Alibaba’s CrossE, Improves Knowledge Graph Embedding

Researchers at Alibaba, Zhejiang University and the University of Zurich have developed a knowledge graph embedding (KGE) model called CrossE. The model overcomes the earlier challenges associated with knowledge graphs— the inability to learn from existing triples without additional help. The CrossE model overcomes the challenge by learning multiple triple-specific embeddings for each graph entity. […]