Entity–relationship model

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A sample Entity – Relationship diagram using Chen's notation

In software engineering, an entity–relationship model (ER model) is a data model for describing a database in an abstract way. This article refers to the techniques proposed in Peter Chen's 1976 paper.[1] However, variants of the idea existed previously,[2] and have been devised subsequently such as supertype and subtype data entities[3] and commonality relationships.


An ER model is an abstract way of describing a database. In the case of a relational database, which stores data in tables, some of the data in these tables point to data in other tables - for instance, your entry in the database could point to several entries for each of the phone numbers that are yours. The ER model would say that you are an entity, and each phone number is an entity, and the relationship between you and the phone numbers is 'has a phone number'. Diagrams created to design these entities and relationships are called entity–relationship diagrams or ER diagrams.

Using the three schema approach to software engineering, there are three levels of ER models that may be developed.

Conceptual data model
This is the highest level ER model in that it contains the least granular detail but establishes the overall scope of what is to be included within the model set. The conceptual ER model normally defines master reference data entities that are commonly used by the organization. Developing an enterprise-wide conceptual ER model is useful to support documenting the data architecture for an organization.
A conceptual ER model may be used as the foundation for one or more logical data models (see below). The purpose of the conceptual ER model is then to establish structural metadata commonality for the master data entities between the set of logical ER models. The conceptual data model may be used to form commonality relationships between ER models as a basis for data model integration.
Logical data model
A logical ER model does not require a conceptual ER model, especially if the scope of the logical ER model includes only the development of a distinct information system. The logical ER model contains more detail than the conceptual ER model. In addition to master data entities, operational and transactional data entities are now defined. The details of each data entity are developed and the entity relationships between these data entities are established. The logical ER model is however developed independent of technology into which it will be implemented.
Physical data model
One or more physical ER models may be developed from each logical ER model. The physical ER model is normally developed to be instantiated as a database. Therefore, each physical ER model must contain enough detail to produce a database and each physical ER model is technology dependent since each database management system is somewhat different.
The physical model is normally forward engineered to instantiate the structural metadata into a database management system as relational database objects such as database tables, database indexes such as unique key indexes, and database constraints such as a foreign key constraint or a commonality constraint. The ER model is also normally used to design modifications to the relational database objects and to maintain the structural metadata of the database.

The first stage of information system design uses these models during the requirements analysis to describe information needs or the type of information that is to be stored in a database. The data modeling technique can be used to describe any ontology (i.e. an overview and classifications of used terms and their relationships) for a certain area of interest. In the case of the design of an information system that is based on a database, the conceptual data model is, at a later stage (usually called logical design), mapped to a logical data model, such as the relational model; this in turn is mapped to a physical model during physical design. Note that sometimes, both of these phases are referred to as "physical design". It is also used in database management system.

Entity–relationship modelling[edit]

Two related entities
An entity with an attribute
A relationship with an attribute

An entity may be defined as a thing which is recognized as being capable of an independent existence and which can be uniquely identified. An entity is an abstraction from the complexities of a domain. When we speak of an entity, we normally speak of some aspect of the real world which can be distinguished from other aspects of the real world.[4]

An entity may be a physical object such as a house or a car, an event such as a house sale or a car service, or a concept such as a customer transaction or order. Although the term entity is the one most commonly used, following Chen we should really distinguish between an entity and an entity-type. An entity-type is a category. An entity, strictly speaking, is an instance of a given entity-type. There are usually many instances of an entity-type. Because the term entity-type is somewhat cumbersome, most people tend to use the term entity as a synonym for this term.

Entities can be thought of as nouns. Examples: a computer, an employee, a song, a mathematical theorem.

A relationship captures how entities are related to one another. Relationships can be thought of as verbs, linking two or more nouns. Examples: an owns relationship between a company and a computer, a supervises relationship between an employee and a department, a performs relationship between an artist and a song, a proved relationship between a mathematician and a theorem.

The model's linguistic aspect described above is utilized in the declarative database query language ERROL, which mimics natural language constructs. ERROL's semantics and implementation are based on reshaped relational algebra (RRA), a relational algebra which is adapted to the entity–relationship model and captures its linguistic aspect.

Entities and relationships can both have attributes. Examples: an employee entity might have a Social Security Number (SSN) attribute; the proved relationship may have a date attribute.

Every entity (unless it is a weak entity) must have a minimal set of uniquely identifying attributes, which is called the entity's primary key.

Entity–relationship diagrams don't show single entities or single instances of relations. Rather, they show entity sets and relationship sets. Example: a particular song is an entity. The collection of all songs in a database is an entity set. The eaten relationship between a child and her lunch is a single relationship. The set of all such child-lunch relationships in a database is a relationship set. In other words, a relationship set corresponds to a relation in mathematics, while a relationship corresponds to a member of the relation.

Certain cardinality constraints on relationship sets may be indicated as well.

Mapping natural language[edit]

Chen proposed the following "rules of thumb" for mapping natural language descriptions into ER diagrams:[5]

English grammar structureER structure
Common nounEntity type
Proper nounEntity
Transitive verbRelationship type
Intransitive verbAttribute type
AdjectiveAttribute for entity
AdverbAttribute for relationship

Physical view show how data is actually stored.

Relationships, roles and cardinalities[edit]

In Chen's original paper he gives an example of a relationship and its roles. He describes a relationship "marriage" and its two roles "husband" and "wife".

A person plays the role of husband in a marriage (relationship) and another person plays the role of wife in the (same) marriage. These words are nouns. That is no surprise; naming things requires a noun.

However as is quite usual with new ideas, many eagerly appropriated the new terminology but then applied it to their own old ideas. Thus the lines, arrows and crows-feet of their diagrams owed more to the earlier Bachman diagrams than to Chen's relationship diamonds. And they similarly misunderstood other important concepts.[citation needed]

In particular, it became fashionable (now almost to the point of exclusivity) to "name" relationships and roles as verbs or phrases.

Role naming[edit]

It has also become prevalent to name roles with phrases e.g. is-the-owner-of and is-owned-by etc. Correct nouns in this case are "owner" and "possession". Thus "person plays the role of owner" and "car plays the role of possession" rather than "person plays the role of is-the-owner-of" etc.

The use of nouns has direct benefit when generating physical implementations from semantic models. When a person has two relationships with car then it is possible to very simply generate names such as "owner_person" and "driver_person" which are immediately meaningful.[6]


Modifications to the original specification can be beneficial. Chen described look-across cardinalities. As an aside, the Barker-Ellis notation, used in Oracle Designer, uses same-side for minimum cardinality (analogous to optionality) and role, but look-across for maximum cardinality (the crows foot).[clarification needed]

In Merise,[7] Elmasri & Navathe[8] and others[9] there is a preference for same-side for roles and both minimum and maximum cardinalities. Recent researchers (Feinerer,[10] Dullea et al.[11]) have shown that this is more coherent when applied to n-ary relationships of order > 2.

In Dullea et al. one reads "A 'look across' notation such as used in the UML does not effectively represent the semantics of participation constraints imposed on relationships where the degree is higher than binary."

In Feinerer it says "Problems arise if we operate under the look-across semantics as used for UML associations. Hartmann[12] investigates this situation and shows how and why different transformations fail." (Although the "reduction" mentioned is spurious as the two diagrams 3.4 and 3.5 are in fact the same) and also "As we will see on the next few pages, the look-across interpretation introduces several difficulties which prevent the extension of simple mechanisms from binary to n-ary associations."

Various methods of representing the same one to many relationship. In each case, the diagram shows the relationship between a person and a place of birth: each person must have been born at one, and only one, location, but each location may have had zero or more people born at it.
Two related entities shown using Crow's Foot notation. In this example, an optional relationship is shown between Artist and Song; the symbols closest to the song entity represents "zero, one, or many", whereas a song has "one and only one" Artist. The former is therefore read as, an Artist (can) perform(s) "zero, one, or many" song(s).

Chen's notation for entity–relationship modeling uses rectangles to represent entity sets, and diamonds to represent relationships appropriate for first-class objects: they can have attributes and relationships of their own. If an entity set participates in a relationship set, they are connected with a line.

Attributes are drawn as ovals and are connected with a line to exactly one entity or relationship set.

Cardinality constraints are expressed as follows:

Attributes are often omitted as they can clutter up a diagram; other diagram techniques often list entity attributes within the rectangles drawn for entity sets.

Related diagramming convention techniques:

Crow's Foot Notation[edit]

Crow's Foot notation is used in Barker's Notation, SSADM and Information Engineering. Crow's Foot diagrams represent entities as boxes, and relationships as lines between the boxes. Different shapes at the ends of these lines represent the cardinality of the relationship.

Crow's Foot notation was used in the consultancy practice CACI. Many of the consultants at CACI (including Richard Barker) subsequently moved to Oracle UK, where they developed the early versions of Oracle's CASE tools, introducing the notation to a wider audience. The following tools use Crow's Foot notation: ARIS, System Architect, Visio, PowerDesigner, Toad Data Modeler, DeZign for Databases, Devgems Data Modeler, OmniGraffle, MySQL Workbench and SQL Developer Data Modeler. CA's ICASE tool, CA Gen aka Information Engineering Facility also uses this notation. Historically XA Systems Silverrun-LDM (logical data model) also supported this notation.

ER diagramming tools[edit]

There are many ER diagramming tools. Free software ER diagramming tools that can interpret and generate ER models and SQL and do database analysis are MySQL Workbench (formerly DBDesigner), and Open ModelSphere (open-source). A freeware ER tool that can generate database and application layer code (webservices) is the RISE Editor. SQL Power Architect while proprietary also has a free community edition.

Proprietary ER diagramming tools are Avolution, ER/Studio, ERwin, DeZign for Databases, MagicDraw, MEGA International, ModelRight, Navicat Data Modeler, OmniGraffle, Oracle Designer, PowerDesigner, Prosa Structured Analysis Tool, Rational Rose, Software Ideas Modeler, Sparx Enterprise Architect, SQLyog, System Architect, Toad Data Modeler, and Visual Paradigm.

Free software diagram tools just draw the shapes without having any knowledge of what they mean, nor do they generate SQL. These include Creately, yEd, LucidChart, Calligra Flow, and Dia.

ER and semantic modelling[edit]

Peter Chen, the father of ER modelling said in his seminal paper:

"The entity-relationship model adopts the more natural view that the real world consists of entities and relationships. It incorporates some of the important semantic information about the real world." [1]

He is here in accord with philosophic and theoretical traditions from the time of the Ancient Greek philosophers: Socrates, Plato and Aristotle (428 BC) through to modern epistemology, semiotics and logic of Peirce, Frege and Russell. Plato himself associates knowledge with the apprehension of unchanging Forms (The forms, according to Socrates, are roughly speaking archetypes or abstract representations of the many types of things, and properties) and their relationships to one another. In his original 1976 article Chen explicitly contrasts entity–relationship diagrams with record modelling techniques:

"The data structure diagram is a representation of the organisation of records and is not an exact representation of entities and relationships."

Several other authors also support his program:[14][15][16][17][18]

A semantic model is a model of concepts, it is sometimes called a "platform independent model". It is an intensional model. At the latest since Carnap, it is well known that:[19]

"...the full meaning of a concept is constituted by two aspects, its intension and its extension. The first part comprises the embedding of a concept in the world of concepts as a whole, i.e. the totality of all relations to other concepts. The second part establishes the referential meaning of the concept, i.e. its counterpart in the real or in a possible world".

An extensional model is one which maps to the elements of a particular methodology or technology, and is thus a "platform specific model". The UML specification explicitly states that associations in class models are extensional and this is in fact self-evident by considering the extensive array of additional "adornments" provided by the specification over and above those provided by any of the prior candidate "semantic modelling languages"."UML as a Data Modeling Notation, Part 2"


See also[edit]


  1. ^ a b "The Entity Relationship Model: Toward a Unified View of Data" for entity–relationship modeling.
  2. ^ A.P.G. Brown, "Modelling a Real-World System and Designing a Schema to Represent It", in Douque and Nijssen (eds.), Data Base Description, North-Holland, 1975, ISBN 0-7204-2833-5.
  3. ^ “Designing a Logical Database: Supertypes and Subtypes”
  4. ^ Paul Beynon-Davies (2004). Database Systems. Houndmills, Basingstoke, UK: Palgrave
  5. ^ "English, Chinese and ER diagrams" by Peter Chen.
  6. ^ Thomas Basboell: Motion and society. On meaningfulness of concepts
  7. ^ Hubert Tardieu, Arnold Rochfeld and René Colletti La methode MERISE: Principes et outils (Paperback - 1983)
  8. ^ Elmasri, Ramez, B. Shamkant, Navathe, Fundamentals of Database Systems, third ed., Addison-Wesley, Menlo Park, CA, USA, 2000.
  9. ^ ER 2004 : 23rd International Conference on Conceptual Modeling, Shanghai, China, November 8-12, 2004
  10. ^ A Formal Treatment of UML Class Diagrams as an Efficient Method for Configuration Management 2007
  11. ^ James Dullea, Il-Yeol Song, Ioanna Lamprou - An analysis of structural validity in entity-relationship modeling 2002
  12. ^ Hartmann, Sven. "Reasoning about participation constraints and Chen's constraints". Proceedings of the 14th Australasian database conference-Volume 17. Australian Computer Society, Inc., 2003.
  13. ^ IDEF1X[dead link]
  14. ^ Kent in "Data and Reality" : "One thing we ought to have clear in our minds at the outset of a modelling endeavour is whether we are intent on describing a portion of "reality" (some human enterprise) or a data processing activity."
  15. ^ Abrial in "Data Semantics" : "... the so called "logical" definition and manipulation of data are still influenced (sometimes unconsciously) by the "physical" storage and retrieval mechanisms currently available on computer systems."
  16. ^ Stamper: "They pretend to describe entity types, but the vocabulary is from data processing: fields, data items, values. Naming rules don't reflect the conventions we use for naming people and things; they reflect instead techniques for locating records in files."
  17. ^ In Jackson's words: "The developer begins by creating a model of the reality with which the system is concerned, the reality which furnishes its [the system's] subject matter ..."
  18. ^ Elmasri, Navathe: "The ER model concepts are designed to be closer to the user’s perception of data and are not meant to describe the way in which data will be stored in the computer."
  19. ^ http://wenku.baidu.com/view/8048e7bb1a37f111f1855b22.html
  20. ^ P. Chen. Suggested research directions for a new frontier: Active conceptual modeling. ER 2006, volume 4215 of Lecture Notes in Computer Science, pages 1–4. Springer Berlin / Heidelberg, 2006.
  21. ^ M. L. Brodie and J. T. Liu. The power and limits of relational technology in the age of information ecosystems. On The Move Federated Conferences, 2010.
  22. ^ A. Badia and D. Lemire. A call to arms: revisiting database design. SIGMOD Record 40, 3 (November 2011), 61-69.
  23. ^ Gregersen, Heidi, and Christian S. Jensen. "Temporal Entity-Relationship models—a survey." IEEE Transactions on Knowledge and Data Engineering, 11.3 (1999): 464-497.
  24. ^ RICCARDO TORLONE (2003). "Conceptual Multidimensional Models". In Maurizio Rafanelli. Multidimensional Databases: Problems and Solutions. Idea Group Inc (IGI). ISBN 978-1-59140-053-0. 

Further reading[edit]

The basic ER model is covered in most database textbooks.

In-depth monographs about ER-based modelling:

External links[edit]