Semantic heterogeneity

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, the problem of semantic heterogeneity is compounded due to the flexibility of semi-structured data and various tagging methods applied to documents or unstructured data. 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 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 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:

Class	Category	Subcategory	Examples
Language	Encoding	Ingest Encoding Mismatch	For example, ASCII v UTF-8
		Ingest Encoding Lacking	Mis-recognition of tokens because not being parsed with the proper encoding
		Query Encoding Mismatch	For example, ASCII v UTF-8 in search
		Query Encoding Lacking	Mis-recognition of search tokens because not being parsed with the proper encoding
	Languages	Script Mismatch	Variations in how parsers handle, say, stemming, white spaces or hyphens
		Parsing / Morphological Analysis Errors (many)	Arabic languages (right-to-left) v Romance languages (left-to-right)
		Syntactical Errors (many)	Ambiguous sentence references, such as I'm glad I'm a man, and so is Lola (Lola by Ray Davies and the Kinks)
		Semantics Errors (many)	River bank v money bank v billiards bank shot
Conceptual	Naming	Case Sensitivity	Uppercase v lower case v Camel case
		Synonyms	United States v USA v America v Uncle Sam v Great Satan
		Acronyms	United States v USA v US
		Homonyms	Such as when the same name refers to more than one concept, such as Name referring to a person v Name referring to a book
		Misspellings	As stated
	Generalization / Specialization		When single items in one schema are related to multiple items in another schema, or vice versa. For example, one schema may refer to "phone" but the other schema has multiple elements such as "home phone", "work phone" and "cell phone"
	Aggregation	Intra-aggregation	When the same population is divided differently (such as, Census v Federal regions for states, England v Great Britain v United Kingdom, or full person names v first-middle-last)
		Inter-aggregation	May occur when sums or counts are included as set members
	Internal Path Discrepancy		Can arise from different source-target retrieval paths in two different schemas (for example, hierarchical structures where the elements are different levels of remove)
	Missing Item	Content Discrepancy	Differences in set enumerations or including items or not (say, US territories) in a listing of US states
		Missing Content	Differences in scope coverage between two or more datasets for the same concept
		Attribute List Discrepancy	Differences in attribute completeness between two or more datasets
		Missing Attribute	Differences in scope coverage between two or more datasets for the same attribute
	Item Equivalence		When two types (classes or sets) are asserted as being the same when the scope and reference are not (for example, Berlin the city v Berlin the official city-state)
			When two individuals are asserted as being the same when they are actually distinct (for example, John F. Kennedy the president v John F. Kennedy the aircraft carrier)
	Type Mismatch		When the same item is characterized by different types, such as a person being typed as an animal v human being v person
	Constraint Mismatch		When attributes referring to the same thing have different cardinalities or disjointedness assertions
Domain	Schematic Discrepancy	Element-value to Element-label Mapping	One of four errors that may occur when attribute names (say, Hair v Fur) may refer to the same attribute, or when same attribute names (say, Hair v Hair) may refer to different attribute scopes (say, Hair v Fur) or where values for these attributes may be the same but refer to different actual attributes or where values may differ but be for the same attribute and putative value. Many of the other semantic heterogeneities herein also contribute to schema discrepancies
		Attribute-value to Element-label Mapping
		Element-value to Attribute-label Mapping
		Attribute-value to Attribute-label Mapping
	Scale or Units	Measurement Type	Differences, say, in the metric v English measurement systems, or currencies
		Units	Differences, say, in meters v centimeters v millimeters
	Precision		For example, a value of 4.1 inches in one dataset v 4.106 in another dataset
	Data representation	Primitive Data Type	Confusion often arises in the use of literals v URIs v object types
		Data Format	Delimiting decimals by period v commas; various date formats; using exponents or aggregate units (such as thousands or millions)
Data	Naming	Case Sensitivity	Uppercase v lower case v Camel case
		Synonyms	For example, centimeters v cm
		Acronyms	For example, currency symbols v currency names
		Homonyms	Such as when the same name refers to more than one attribute, such as Name referring to a person v Name referring to a book
		Misspellings	As stated
	ID Mismatch or Missing ID		URIs can be a particular problem here, due to actual mismatches but also use of name spaces or not and truncated URIs
	Missing Data		A common problem, more acute with closed world approaches than with open world ones
	Element Ordering		Set members can be ordered or unordered, and if ordered, the sequences of individual members or values can differ

A different approach toward classifying semantics and integration approaches is taken by Sheth et al. 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 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 (FOL) or description logic is inadequate alone to properly capture the needed semantics.

Relevant applications

Besides data interoperability, relevant areas in information technology that depend on reconciling semantic heterogeneities include data mapping, semantic integration, and enterprise information integration, 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 mapping
• Enterprise information integration
• Heterogeneous database system
• Interoperability
• Ontology-based data integration
• Schema matching
• Semantic integration
• Semantic matching
• Semantics

Further reading

• Classification of semantic heterogeneity