Semantic heterogeneity is when
database schema or
datasets for the same domain are developed by independent parties, resulting in differences in meaning and interpretation of data values. Beyond
structured data
A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be c ...
, the problem of semantic heterogeneity is compounded due to the flexibility of
semi-structured data Semi-structured data is a form of structured data that does not obey the tabular structure of data models associated with relational databases or other forms of data tables, but nonetheless contains tags or other markers to separate semantic elem ...
and various
tagging methods applied to documents or
unstructured data
Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, num ...
. Semantic heterogeneity is one of the more important sources of differences in
heterogeneous datasets.
Yet, for multiple data sources to interoperate with one another, it is essential to reconcile these
semantic differences. Decomposing the various sources of semantic heterogeneities provides a basis for understanding how to map and transform data to overcome these differences.
Classification
One of the first known classification schemes applied to
data semantics is from William Kent more than two decades ago. Kent's approach dealt more with structural
mapping issues than differences in meaning, which he pointed to
data dictionaries as potentially solving.
One of the most comprehensive classifications is from Pluempitiwiriyawej and Hammer, "Classification Scheme for Semantic and Schematic Heterogeneities in XML Data Sources". They classify heterogeneities into three broad classes:
* ''
Structural
A structure is an arrangement and organization of interrelated elements in a material object or system, or the object or system so organized. Material structures include man-made objects such as buildings and machines and natural objects such ...
'' conflicts arise when the schema of the sources representing related or overlapping data exhibit discrepancies. Structural conflicts can be detected when comparing the underlying schema. The class of structural conflicts includes generalization conflicts, aggregation conflicts, internal path discrepancy, missing items, element ordering, constraint and type mismatch, and naming conflicts between the element types and attribute names.
* ''
Domain'' conflicts arise when the semantics of the data sources that will be integrated exhibit discrepancies. Domain conflicts can be detected by looking at the information contained in the schema and using knowledge about the underlying data domains. The class of domain conflicts includes schematic discrepancy, scale or unit, precision, and data representation conflicts.
* ''
Data
In the pursuit of knowledge, data (; ) is a collection of discrete Value_(semiotics), values that convey information, describing quantity, qualitative property, quality, fact, statistics, other basic units of meaning, or simply sequences of sy ...
'' conflicts refer to discrepancies among similar or related data values across multiple sources. Data conflicts can only be detected by comparing the underlying sources. The class of data conflicts includes ID-value, missing data, incorrect spelling, and naming conflicts between the element contents and the attribute values.
Moreover, mismatches or conflicts can occur between set elements (a "population" mismatch) or attributes (a "description" mismatch).
Michael Bergman expanded upon this schema by adding a fourth major explicit category of language, and also added some examples of each kind of semantic heterogeneity, resulting in about 40 distinct potential categories
. This table shows the combined 40 possible sources of semantic heterogeneities across sources:
A different approach toward classifying semantics and integration approaches is taken by
Sheth et al.
[{{cite journal , title=Semantics for the semantic Web: the implicit, the formal and the powerful , author1=Amit P. Sheth, author2=Cartic Ramakrishnan, author3=Christopher Thomas, journal=International Journal on Semantic Web and Information Systems , volume=1 , issue=1 , pages=1–18 , date=2005 , doi=10.4018/jswis.2005010101, url=http://www.informatik.uni-trier.de/~ley/db/journals/ijswis/ijswis1.html] Under their concept, they split semantics into three forms: implicit, formal and powerful. Implicit semantics are what is either largely present or can easily be extracted; formal languages, though relatively scarce, occur in the form of
ontologies
In computer science and information science, an ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains ...
or other
description logics; and powerful (soft) semantics are fuzzy and not limited to rigid set-based assignments. Sheth et al.'s main point is that
first-order logic
First-order logic—also known as predicate logic, quantificational logic, and first-order predicate calculus—is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quantifie ...
(FOL) or description logic is inadequate alone to properly capture the needed semantics.
Relevant applications
Besides data interoperability, relevant areas in
information technology
Information technology (IT) is the use of computers to create, process, store, retrieve, and exchange all kinds of Data (computing), data . and information. IT forms part of information and communications technology (ICT). An information te ...
that depend on reconciling semantic heterogeneities include
data mapping
In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including:
* Data transforma ...
,
semantic integration
Semantic integration is the process of interrelating information from diverse sources, for example calendars and to do lists, email archives, presence information (physical, psychological, and social), documents of all sorts, contacts (including ...
, and
enterprise information integration
Enterprise information integration (EII) is the ability to support an unified view of data and information for an entire organization. In a data virtualization application of EII, a process of information integration, using data abstraction to pr ...
, among many others. From the conceptual to actual data, there are differences in perspective, vocabularies, measures and conventions once any two data sources are brought together. Explicit attention to these semantic heterogeneities is one means to get the information to integrate or interoperate.
A mere twenty years ago, information technology systems expressed and stored data in a multitude of formats and systems. The Internet and Web protocols have done much to overcome these sources of differences. While there is a large number of categories of semantic heterogeneity, these categories are also patterned and can be anticipated and corrected. These patterned sources inform what kind of work must be done to overcome semantic differences where they still reside.
See also
*
Data integration
Data integration involves combining data residing in different sources and providing users with a unified view of them.
This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies ...
*
Data mapping
In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including:
* Data transforma ...
*
Enterprise information integration
Enterprise information integration (EII) is the ability to support an unified view of data and information for an entire organization. In a data virtualization application of EII, a process of information integration, using data abstraction to pr ...
*
Heterogeneous database system
*
Interoperability
Interoperability is a characteristic of a product or system to work with other products or systems. While the term was initially defined for information technology or systems engineering services to allow for information exchange, a broader defi ...
*
Ontology-based data integration
*
Schema matching
*
Semantic integration
Semantic integration is the process of interrelating information from diverse sources, for example calendars and to do lists, email archives, presence information (physical, psychological, and social), documents of all sorts, contacts (including ...
*
Semantic matching
*
Semantics
Semantics (from grc, σημαντικός ''sēmantikós'', "significant") is the study of reference, meaning, or truth. The term can be used to refer to subfields of several distinct disciplines, including philosophy, linguistics and comp ...
References
Further reading
Classification of semantic heterogeneity
Data management
Interoperability
Knowledge management
Semantics