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.[1] 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.


One of the first known classification schemes applied to data semantics is from William Kent more than two decades ago.[2] 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”.[3] They classify heterogeneities into three broad classes:

  • Structuralconflicts 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.
  • Domainconflicts 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.
  • Dataconflicts 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 [4] .[5] 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.[6] 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.


  1. ^Alon Halevy (2005). “Why your data won’t mix”. Queue. 3 (8).
  2. ^William Kent (February 27 – March 3, 1989). The many forms of a single fact. Proceedings of the IEEE COMPCON. San Francisco. 13 pp.
  3. ^Charnyote Pluempitiwiriyawej and Joachim Hammer (September 2000). “A classification scheme for semantic and schematic heterogeneities in XML data sources” (PDF). Gainesville, Florida: University of Florida. Technical Report TR00-004.
  4. ^K. Bergman (6 June 2006). “Sources and classification of semantic heterogeneities”. AI3:::Adaptive Information. Retrieved 28 September 2014.
  5. ^K. Bergman (12 August 2014). “Big structure and data interoperability”. AI3:::Adaptive Information. Retrieved 28 September 2014.
  6. ^Amit P. Sheth; Cartic Ramakrishnan; Christopher Thomas (2005). “Semantics for the semantic Web: the implicit, the formal and the powerful”. International Journal on Semantic Web and Information Systems. 1 (1): 1–18.

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