AI Glossary

DENDRAL (acronym for “Dendritic Algorithm”) was an expert system developed at Stanford University in the 1960s by Joshua Lederberg, Edward Feigenbaum, and others. DENDRAL was designed to analyze the molecular structure of organic compounds using spectroscopic data, and to suggest possible chemical structures based on that data. It used a set of rules and heuristics to generate hypotheses about the structure of a compound, and then refined those hypotheses through a process of deductive reasoning. DENDRAL was a groundbreaking system that demonstrated the potential of AI for solving complex scientific problems, and it paved the way for later expert systems in other domains.


LHASA (acronym for “LHASA Heuristic Analog Structure Analysis”) was another AI system developed in the 1970s for use in the field of toxicology. LHASA was developed by researchers at the Royal Society of Chemistry in the UK, and it was designed to predict the toxicity of chemical compounds based on their molecular structure. Like DENDRAL, LHASA used a set of rules and heuristics to analyze the structure of a compound and generate hypotheses about its toxicity. LHASA was one of the first AI systems to be used in the chemical industry, and it helped to pave the way for later applications of AI in drug discovery and toxicology.


In computer programming, instantiation refers to the process of creating an instance of a class, which means creating a specific object that has the attributes and behaviors defined by that class. Think of a class as a blueprint or a template, and an instance as a copy of that blueprint that can be customized with specific values for its attributes. When you create an instance of a class, you are allocating memory for that object and setting its initial values, so that it can be used and manipulated by the program. The process of instantiation is a fundamental concept in object-oriented programming, which is used in many modern programming languages such as Java, Python, and C++.


A knowledge graph is a type of database that represents knowledge in a structured format using a graph-based model.

In a knowledge graph, nodes represent entities or concepts, and edges represent the relationships between them. For example, a knowledge graph about movies might have nodes representing actors, directors, and movies, and edges representing relationships such as “acted in”, “directed”, and “released in”.

Knowledge graphs are typically built using semantic web technologies and can be used to represent and integrate data from multiple sources and domains. They are designed to support complex queries and reasoning, and can be used for applications such as search, recommendation systems, and question answering.

One of the most well-known knowledge graphs is Google’s Knowledge Graph. It provides structured information in an info box beside its search results.


An ontology is a formal representation of knowledge that defines the concepts and relationships within a particular domain. Think of an ontology as a way to organize and categorize information about a specific topic. It’s like a structured vocabulary that describes the concepts, properties, and relationships between different things within that topic.

For example, an ontology about animals might define concepts such as “mammal”, “reptile”, and “bird”, and describe the relationships between them, such as “mammals give birth to live young”. This allows computers to understand the relationships and connections between different concepts and information, which can be useful for tasks such as natural language processing, information retrieval, and knowledge management.


Semantic web technologies refer to a set of standards and tools for representing and linking data on the web in a machine-readable format, with the goal of enabling automated processing and integration of data from diverse sources.

The core technologies of the semantic web include RDF (Resource Description Framework), OWL (Web Ontology Language), and SPARQL (SPARQL Protocol and RDF Query Language). These technologies allow data to be structured and linked according to standardized vocabularies and ontologies, which enable computers to understand the meaning and relationships between different data elements.

In practical terms, the semantic web technologies can be used to create a “web of data” that extends beyond the traditional web of documents, enabling the integration of data from various sources and domains, such as scientific data, government data, and social media data. This can facilitate a wide range of applications, such as intelligent search, recommendation systems, and data analysis.


Web Ontology Language (OWL) is a standardized language for representing and sharing ontologies on the web. OWL provides a rich set of constructs for modeling complex concepts and relationships, including classes, properties, individuals, restrictions, and rules. It is designed to be expressive enough to support a wide range of domains and applications, and to enable automated reasoning and inference.

OWL has become one of the most widely used and recognized standards for ontology development and sharing. OWL is supported by many tools and frameworks for ontology engineering. It is used in various fields such as artificial intelligence, knowledge management, and semantic web technologies.



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