The vision of semantic interoperability, the fluid sharing of digitalized knowledge, has led much research on ontology/schema mapping/aligning. Although this line of research is fundamental and has brought valuable contributions to this endeavor, it does not represent a solution to the challenge, semantic heterogeneity, since the performance of proposed approaches significantly relies on the degree of uniformity, formalization and sufficiency of data representations but most of today's independently developed information systems seldom have common knowledge modeling frameworks and their data are often not formally and adequately specified. Consequently, a workable solution usually requires interventions of domain experts.
In human society, hierarchically structured standards (or taxonomies) for characterizing complex application processes and objects used in the processes are often used as a common and effective way to achieve some semantic agreements among stakeholders within a domain. This research hypothesizes that the establishment and the use of such standards can serve as a framework that can effectively facilitate the reconciliation of semantic heterogeneity in complex application domains. However, the reality shows that a comprehensive priori consensus is extremely difficult, if not impossible, to reach. Consequently, various complementary and competing standards are often created and their constant-changing nature yields another level of challenge in achieving the hypothesis.
This paper focuses on the development of methodology for bridging complementary standards within an application domain. It exemplifies such standards in building construction industry where interoperability problems are prevalent and human interactions are commonplace. It proposes a semi-automatic approach for semantically associating the standards to reduce costly human intervention in a workflow. The approach formalizes standards by using ontology and discovers their affinity (to what degree they are related Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission by the copyright owners.
Copyright 2005 with respect to their usage) from automated project document processing and semi-automatic domain expert inputs. A high-level architecture of an integration framework in web environment is suggested for depicting the role of the semantic association approach in the system.
The vision of semantic interoperability, the fluid sharing of
digitalized knowledge, has led much research on ontology
(formal specification of conceptualization) and its
languages, such as Web Ontology Language (OWL) [
This paper focuses on the development of methodology for
bridging complementary standards within an application
domain. We have chosen a target application in the
building construction domain, where interoperability problems
are prevalent and human interactions are commonplace. In
that domain, a variety of taxonomy-based standards have
been established but still lack a uniform and systematic way
for supporting efficient collaboration among project
participants using different standards. This problem is further
compounded by the complexity and the dynamics of
business applications, which often require changes of the
wellknown standards. The interoperability cost in such
environment is tremendous. For example, based on a recent
National Institute of Standards and Technology (NIST)
report [
Two mainstream complementary standards, MasterFormat
and UniformatII, in that domain are considered in our
research. MasterFormat [
Directly matching approaches based on attributes of the entities of the standards are expected to be inefficient due to the heterogeneous nature of complementary standards. This paper proposes a practical compromise by redefining the notion of mapping with a semi-automatic semantic extraction framework to assist domain experts in achieving interoperability. The mapping is termed as semantic association for relating elements between standards, and is dependent on the intended use such as cross-referencing of elements or specification semantic mapping. The semantic relationship can be characterized in two measurements: similarity (how closely objects resemble each other in their representation) and affinity (to what degree they are coupled in their usage). In some sense similarity is more static while affinity is more dynamic and general. For example, a bicycle is similar to a car due to their physical structures and properties. However, gasoline is more affinitive to a car although they do not resemble each other. Exploiting affinity in addition to similarity through semantic association is the focus of this research.
The approach consists of three components: formalization of taxonomies, ontology-based semantic extraction and measurement of affinity. The first component is a simple and yet novel approach for annotating a standard in primitive descriptive statements constructed by a set of necessary and sufficient orthogonal relations. They are then normalized and generalized into ontology. The second component shows how the ontology can be used for the extraction of relevant information from the instances in other standards for semantic association. The third component quantifies the affinity for ranking the extracted metadata to identify optimal association. The following sections detail the three components and outline an overall architecture of an integration framework depicting the relationship between the proposed approach and other related technologies and systems.
Taxonomies are initially designed for human consumption therefore some domain knowledge that is obvious and assumed by stakeholders is often omitted in their specifications. Moreover, taxonomies classifying large and complex items usually have the following characteristics: 1. The entities being classified and the attributes upon which the classification is based, are themselves complex concepts. 2. 3.
Multiple attributes (different concepts) might be used to classify entities at the same level.
Attributes are not orthogonal and might result in overlapping concepts in low-level entities (an object can fit into multiple categories).
There is a need for a systematic approach for annotating assumed semantics, clarifying complex concepts, and transforming them into formal representation before taxonomies can be effectively used for semantic association. Semantic depends on context and context depends on applications. In other words, the semantic of a standard is open depending on how they are used. To avoid a standard being bound to specific applications, the intrinsic semantic of a standard without context should include the following: 1. the attributes being used for classification under the general perception in the application domain and 2. the entities under the inheritance of the taxonomy and the attributes.
To model the intrinsic semantics, ontology is considered in this research. The following subsection describes a systematic approach for transforming taxonomy into ontology.
The term, ontology, has been widely used in several
disciplines, such as philosophy, epistemology, and computer
science. There is much confusion in its definition. For
example, in philosophy it refers to the subject of existence
while in epistemology it is about knowledge and knowing.
In computer science, many people use Gruber’s definition
[
The goal of this step is to identify a sufficient and necessary set of orthogonal relations for a given taxonomy/standard so that assumed domain knowledge and complex concepts can be formally specified. This step should be manually done by standard committees who know best about the original intended use of the standards. The set should be constructed from two types of relations: primitive and derived. Primitive relations are those that are unambiguously understood by the general public and the relationship between concepts connected by them does not change over time. Moreover, they reflect the intrinsic properties of objects or describe time and space and the intention of users when the objects are used. In addition, their definitions should include set relationship, such as instance-instance, instanceclass, and class-class, to avoid ambiguity. For example, part_of is ambiguous since it could mean a subcomponent of an object or the membership of an object in a class. Its meaning can be identified as the first explanation if instance-instance is specified.
Derived relations are those that can be composed/modeled from primitive relations.
To elaborate this step, a small portion of the top three levels in MasterFormat taxonomy, Division 5 (D5) Metals and Division 6 (D6) Wood and Plastic rooted from Material, is exemplified as follows:
Division 5- Metals 05100 Structural Metal Framing 05120 Structural steel 05140 Structural aluminum 05160 Metal framing systems 05400 Cold formed metal framing 05410 Load bearing metal studs 05420 Cold formed metal joists 05430 Slotted channel framing
06100 Rough carpentry 06400 Architectural woodwork
06460 Wood frames The following relations are identified for formalizing the above example: 1. 2. 4. 3. instance_of ship used_for (class-class, human intention): purpose kind_of (class-class, intrinsic): containment relation of attributes of instances.
(instance-class, intrinsic): membermade_of (class-class, intrinsic): material component This step is to construct simple statements using the relations defined in step one and all keywords in the taxonomy. The statements are then processed in subsequent steps for constructing ontology. There are two advantages using this bottom-up approach for formalizing taxonomies. One is that it can better address the dynamic nature of standards by enabling incremental updates and modifications of the statements and their resulting ontology. The other advantage is that domain experts who are not familiar with ontology can directly express their knowledge in the simple statements without communication overhead with knowledge modeling experts.
The following are examples of relation statements that partially describe the example shown in previous step. 1. 2. 3.
Metals (D5), Wood (D6), Plastics (D6_1) are instance_of Material (root) (D5_root, D6_root, D6_1_root)
05100_1 Structural is a kind_of “metal framing” (05100_1) 05100 Cold formed is a kind_of “metal framing” (05100_1) 05400 Studes are made_of Metals (D5) (05410_1) 6. 7.
“Load bearing metal studs” are kind_of Metal studs (05410_1) 05410 05410 is used_for 05400 Note that each statement is given a unique identifier (following ) derived from the original identifier of a taxonomy entity.
It is likely that redundant or conflict statements are generated along the way when domain experts annotate their taxonomies in the above steps. Based on the mathematical properties of the relations, this step normalizes the statements by: 1. 2.
redundancy elimination (removing same or equivalent statements) conflict detection (for example: A-r1-B, and B-r1-A statements are conflict if r1 has asymmetric property) 3. implication detection (for example, A-r1-B, and B-r1 C statements imply A-r1-C through transitive property).
This step is to generalize the resulting statements from step 3 into higher-level concepts connected by the same set of relations. Human being intervention is required in this step due to the complexity of the process. For example, if there exist A-r1-C, A-r1-D, B-r1-C, and B-r1-D, they can be generalized to concept1{A,B}-r1-concept2{C,D} by union. However, it becomes difficult when the above example is extended to include concept1{A,B}-r1-E and concept2{C,D}-r2-F. One cannot conclude concept1{A,B}-r1concept2{C,D,E} unless an exception indicating no E-r2-F is added. Alternatively, it can be generalized to concept1{A,B}-r1-concept3{E,concept2{C,D}}. The system interacts with users by prompting the dilemmas for resolutions along the process of a whole taxonomy.
Figure 1 shown below depicts the generalized view or ontology of the relation statements shown in previous steps. made_of
Item kind_of
Material Metal kind_of used_for
Function kind_of kind_of Steel
Aluminum
Process {metals, wood, plastics ..} are instance_of Material {stud, joist ..}are instance_of Item {framing, ..}are instance_of Function {cold formed, structural ..} are instance_of Process
ONTOLOGY-BASED SEMANTIC EXTRACTION
The task of the previous module, standard formalization, is
usually a one-time effort (though it is an iterative process)
and it needs significant domain experts’ involvement.
This module is different in that it is used in every
workflow/task and extracted semantics can be accumulated in
repository and used for improving future semantic
association performance. Also, it can be relatively automated by
using general linguistic processing technologies.
Standards, such as UniformatII and MasterFormat,
addressed in this paper are functionally complementary to
each other in an application domain and they are costly
cross-referenced by domain experts in workflows due to
their complexity (vast many-to-many mappings). This
module basically is to automat the process by mimicking a
domain expert doing cross-referencing from the context of a
standard-compliant project specification, a script
representation indexed of the standard, which defines intentionality.
For example, the following text is quoted from a PPD [
B2010 EXTERIOR WALLS 1. Exterior Wall Framing: Cold-formed, light gage steel studs, C-shape, galvanized finish, 6" metal thickness as designed by manufacturer according to American Iron and Steel Institute (AISI) Specification for the Design of Cold Formed Steel Structural Members, for L/240 deflection. Downside: specifications often contain note-style sentences. Supposedly, the PPD is written by an architect and a contractor wants to estimate cost for exterior walls. He might comprehend that the wall framing will be made of coldformed steel studs (semantic). Based on his expertise, he identifies that its corresponding entity in Masterformat is 05410 Load bearing metal studs (association). The following paragraph shows how the ontology/relation statements being used for discovering the semantic under the context of entity B2010 that links the entity to MasterFormat entity 05410 (semantic association):
B2010 Exterior Wall: 1. Exterior Wall Framing: Cold-formed, light gage steel studs, C-shape, galvanized finish, 6" metal thickness 05100_1 05400 05410 In the diagram, “steel” and “framing” match the statement 05100_1 (one of the identifiers of the relation statements exemplified in previous subsection) which is Metals (D5) used_for framing. The “steel” matches “Metals” through the transitive property of the relation, kind_of. The match is extended to statement 05400, which includes “cold-formed”. Finally “studs” is added to the match of statement 05410, through statement (05400_05410). Indeed the entity B2010 Exterior Wall in UniformatII has a semantic relationship with 05410 Load bearing metal studs in MasterFormat and the semantic can be described by the relation made_of. One characteristic worthy of mentioning is that the entity B2010 Exterior Wall in the taxonomy provides a good context for helping refining the association. For instance, the above matching, even without the “framing” keyword, is still possible since the inherited semantic of the hierarchy, shell, closure, and exterior walls, has very close meaning as framing. As shown in the above example, the documents or specifications that this research addresses have following characteristics: 1. 2. 3. 4.
Content has limited scope. It often details what, where, how, and when objects and activities being involved in a domain application. It usually contains rich semantics (author’s intention for communicating with other stakeholders) related to standards (due to the agreement among stakeholders) that coordinate objects and activities in the domain.
Content are categorized according to taxonomy. In other words, text in a document has some assumption or context, which is inherited along the taxonomy hierarchy.
Terminologies are relatively unified and unambiguous.
Sentences are relatively free styled, such as notestyled or template-styled due to writing convention or standards.
These characteristics distinguish this research from others,
such as [
In addition to the intrinsic semantics of standards, this module also explores their application or context semantics in order to achieve more effective semantic extraction. The application semantics depend on the stakeholders’ view or interests, such as information they intent for. For example, a cost estimator might look for MasterFormat items and some numerical information so that they can link them to their MasterFormat-based cost databases. On the other hand, an inspector might be interested in the same information but in different view points that yield to different semantics. For example, to a cost estimator, “6" metal thickness” in the PPD means how much the studs with such thickness cost. But for an inspector, it means 6” thickness compliance to associated code.
In summary, this module extracts semantics from the instances (specifications) of multiple standards based on three kinds of ontologies: the ontology of the source standard, the Ontology
Generation Semantic Extraction Association
& Internet
Ontology Mapping, Reconciling, Merge, Data Mining Ontology Editing and Presentation ontology of target standard, and the application ontology based on the stakeholders’ views. The extracted semantics are evidences of semantic association of entities between source and target standards.
The ontology-based semantic extraction module can be
implemented via a matching process between relation
statements and text. The goal is to identify a set of matched
relation statements of related entities with respect to their
standards. For a given entity, its associated relation
statements carry different weights depending on their positions
in the taxonomy and the information content [
Number of relation statements matched.
Number of keywords matched.
Quality of the matches. The measurement of the
quality is an open question. Basically the more specific the
matches are, the higher quality they represent. One
effective way to model the quality is by their positions in
the taxonomy (higher level means less specific and thus
carries less weight) and by the information content of
their keywords. The information content can be
quantified by their inverse document frequency (IDF) [
The major thrust of the research is to develop an integration framework that facilitates exploitation of semantics from taxonomy-based standards and instantiations of the standards to achieve higher interoperability between domain participants and their information systems. To demonstrate the applicability of the proposed approach toward the goal, this section shows an overall architecture depicting one possible implementation and its relationship with other related technologies.
Ontology DB Standards, Instances, Views, meta data, Relations Change Management Taxonomy
Formalization Protocol Adapter
Component Plug-in: XML Interface & API,
Data Standards.
View: AppliInstance, cation OntolPPD…. ogy, standard
IDs
Human Browsers: PC, Mobile Devices
Semantic Web
Services
Based
Dynamic
Workflow Systems
In the framework shown in Figure 2, relations and relation
statements of various versions of standards written in
natural languages are developed and uploaded via web-based
tools to the system by stakeholders in the application
domain. The taxonomy formalization along with the change
management modules process them through parsing,
normalization, generalization, linguistic processing (such as
inflection, derivation, compounds, and synonyms), and
indexing for incremental update in the ontology database.
For a particular application, the stakeholders upload
instances of the source standard (e.g., PPDs), target standard,
and its application ontology. After processing the free text
of PPD instances through linguistic techniques such as
tokenization, chunk parsing, and grammatical function
recognition [
The formalized standards, their instances, users’ application
ontologies, and extracted metadata form a semantic rich
ontology repository. Integrating the repository with other
ontology techniques through the plug-in mechanism allows
the effective construction of application domain ontology.
Web services enriched with the vision of the semantic web
have emerged as a mainstream solution to system
integration over the Internet. Following the same trend, the
implementation of the proposed framework adopts the Web
Ontology Language (OWL) [
This work is partially supported by an NSF research grant ITR-0404113.