Ontology

Overview

Project Haystack's semantic model is structured into three layers:

• Vocabulary: giving our tag names formal definitions as terms
• Taxonomy: organizing our terms into a subtype tree
• Ontology: modeling more complex relationships between definitions

Each standardized tag name used by Haystack data has a formal definition or def. For example, the geoCountry tag must be a string and is always expected to be a two letter ISO 3166-1 code. Conjuncts are terms that are defined by composing two or more marker tags. Conjuncts are like coining new compound words from existing simple words. For example, the term we use for measurement of carbon dioxide is the conjunct co2-concentration, which is coined from the individual tags co2 and concentration. The collection of tag and conjunct definitions form what we call a controlled vocabulary. The vocabulary ensures that every term we use is precisely defined.

All of the terms in the controlled vocabulary are further organized into a taxonomy tree that organizes terms from most general to most specific via the mechanism of subtyping. Subtyping is a tool for the classification of concepts. For example, we say that co2 is a subtype of gas because it is a specific type of gas.

We classify Project Haystack as a full ontology because we also define many relationships between our terms (beyond the taxonomy classifications). For example, we define the relationships between substances and the quantities used to perform measurements on those substances: we define that temp is a quantityOf of physical substances, but that luminous-flux is a quantity of light.

Instance Models

The ontology defines the meta model which is used to model concepts. We use the term instance model when we build a Haystack data model for specific buildings and systems. One good way to think of instances is as proper nouns with a unique names and identities. For example, the Empire State Building is an instance of a site, as opposed to the site tag which models the concept of all buildings.

Entities

We use the term entity to describe a unique instance in a Haystack model. Entities model things from the real world like buildings, rooms, equipment, and sensors. An entity in Haystack is always modeled as a dict (collection of tags).

The entity taxonomy tree defines the fundamental types used to build Haystack data models. We define how to model the following fundamental entities of the built environment:

• site: single building with its own street address
• space: location or zone within a site
• equip: physical or logical piece of equipment within a site
• point: sensor, actuator or setpoint for an equip
• weatherStation: weather station observations
• device: computers, controllers, networking gear

Each of these entity types is discussed in detail in the following chapters.

All entities are uniquely identified via the id tag. The id tag serves as the primary key and must be unique within the scope of the entity's dataset. We use the id tag to cross-reference our relationships between entities. For example, spaces and equipment contained within a given site will model their containment relationship via the siteRef tag.

Entities should always be given a dis tag that provides a human friendly name of the entity. A general rule is that display names should be relatively short (under 40 characters), but also fully descriptive of the entity.