Business semantics management

Last updated

Business semantics management [1] [2] (BSM) encompasses the technology, methodology, organization, and culture that brings business stakeholders together to collaboratively realize the reconciliation of their heterogeneous metadata; and consequently the application of the derived business semantics patterns to establish semantic alignment [3] between the underlying data structures.

Contents

BSM is established by two complementary process cycles each grouping a number of activities. The first cycle is the semantic reconciliation cycle, and the second cycle is the semantic application cycle. The two cycles are tied together by the unification process. This double process cycle is iteratively applied until an optimal balance of differences and commonalities between stakeholders are reached that meets the semantic integration requirements. This approach is based on research on community-based ontology engineering [1] [2] that is validated in European projects, government and industry.

Semantic reconciliation

Semantic reconciliation is a process cycle constituted of four subsequent activities: scope, create, refine, and articulate. First, the community is scoped: user roles and affordances are appointed. Next, relevant facts are collected from documentation such as, e.g., natural language descriptions, (legacy) logical schemas, or other metadata and consequently decomposing this scope in elicitation contexts. The deliverable of scoping is an initial upper common ontology that organizes the key upper common patterns that are shared and accepted by the community. These upper common patterns define the current semantic interoperability requirements of the community. Once the community is scoped, all stakeholders syntactically refine and semantically articulate these upper common patterns.

Unification

During unification, a new proposal for the next version of the upper common ontology is produced, aligning relevant parts from the common and divergent stakeholder perspectives. If the semantic reconciliation results in a number of reusable language-neutral and context-independent patterns for constructing business semantics that are articulated with informal meaning descriptions, then the unification is worthwhile.

Semantic application

Semantic application is a process cycle constituted of two subsequent activities: select and commit where the scoped information systems are committed to selected consolidated business semantic patterns. This is done by first selecting relevant patterns from the pattern base. Next, the interpretation of this selection is semantically constrained. Finally, the various scoped sources and services are mapped on (read: committed to) this selection. The selection and axiomatization of this selection should approximate the intended business semantics. This can be verified by automatically verbalization into natural language, and validation of the unlocked data. Validation or deprecation of the commitments may result in another iteration of the semantic reconciliation cycle.

Business semantics

Business semantics [1] are the information concepts that live in the organization, understandable for both business and IT. Business semantics describe the business concepts as they are used and needed by the business instead of describing the information from a technical point of view.

One important aspect of business semantics is that they are shared between many disparate data sources. Many data sources share the same semantics but have different syntax, or format to describe the same concepts.

The way these business semantics are described is less important. Several approaches can be used such as Unified Modeling Language or object-role modeling. This corresponds to Robert Meersman's statement that semantics are "a (set of) mapping(s) from your representation language to agreed concepts (objects, relationships, behavior) in the real-world". [4] In the construction of information systems, semantics have always been crucial, also a concept known as double articulation. In previous approaches, these semantics were left implicit (i.e. In the mind of reader or writer), hidden away in the implementation itself (e.g., in a database table or column code) or informally captured in textual documentation. [5] According to Dave McComb, "The scale and scope of our systems and the amount of information we now have to deal with are straining that model." [6]

Nowadays, information systems need to interact in a more open manner, and it becomes crucial to formally represent and apply the semantics these systems are concerned with. [7]

Application

Business semantics management empowers all stakeholders in the organization by a consistent and aligned definition of the important information assets of the organization.

The available business semantics can be leveraged in the so-called business/social layer of the organization. They can for example be coupled to a content management application to provide the business with a consistent business vocabulary or enable better navigation or classification of information, leveraged by enterprise search engines to make richer semantic web ready websites, etc..

Business semantics can also be used to increase operational efficiency in the technical/operation layer of the organization. It provides an abstracted way to access and deliver data in a more efficient manner. In that respect, it is similar to Enterprise Information Integration (EII) with the added benefit that the shared models are not described in technical terms but in a way that is easily understood by the business.

See also

Related Research Articles

The Semantic Web, sometimes known as Web 3.0, is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

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 of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of concepts and categories that represent the subject.

A modeling language is any artificial language that can be used to express information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure.

<span class="mw-page-title-main">Data modeling</span> Creating a model of the data in a system

Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques.

The semantic spectrum is a series of increasingly precise or rather semantically expressive definitions for data elements in knowledge representations, especially for machine use.

In information science, an upper ontology is an ontology which consists of very general terms that are common across all domains. An important function of an upper ontology is to support broad semantic interoperability among a large number of domain-specific ontologies by providing a common starting point for the formulation of definitions. Terms in the domain ontology are ranked under the terms in the upper ontology, e.g., the upper ontology classes are superclasses or supersets of all the classes in the domain ontologies.

Metadata publishing is the process of making metadata data elements available to external users, both people and machines using a formal review process and a commitment to change control processes.

Semantic integration is the process of interrelating information from diverse sources, for example calendars and to do lists, email archives, presence information, documents of all sorts, contacts, search results, and advertising and marketing relevance derived from them. In this regard, semantics focuses on the organization of and action upon information by acting as an intermediary between heterogeneous data sources, which may conflict not only by structure but also context or value.

The ISO 15926 is a standard for data integration, sharing, exchange, and hand-over between computer systems.

Simple Knowledge Organization System (SKOS) is a W3C recommendation designed for representation of thesauri, classification schemes, taxonomies, subject-heading systems, or any other type of structured controlled vocabulary. SKOS is part of the Semantic Web family of standards built upon RDF and RDFS, and its main objective is to enable easy publication and use of such vocabularies as linked data.

Semantic interoperability is the ability of computer systems to exchange data with unambiguous, shared meaning. Semantic interoperability is a requirement to enable machine computable logic, inferencing, knowledge discovery, and data federation between information systems.

The concept of the Social Semantic Web subsumes developments in which social interactions on the Web lead to the creation of explicit and semantically rich knowledge representations. The Social Semantic Web can be seen as a Web of collective knowledge systems, which are able to provide useful information based on human contributions and which get better as more people participate. The Social Semantic Web combines technologies, strategies and methodologies from the Semantic Web, social software and the Web 2.0.

DOGMA, short for Developing Ontology-Grounded Methods and Applications, is the name of research project in progress at Vrije Universiteit Brussel's STARLab, Semantics Technology and Applications Research Laboratory. It is an internally funded project, concerned with the more general aspects of extracting, storing, representing and browsing information.

The Semantics of Business Vocabulary and Business Rules (SBVR) is an adopted standard of the Object Management Group (OMG) intended to be the basis for formal and detailed natural language declarative description of a complex entity, such as a business. SBVR is intended to formalize complex compliance rules, such as operational rules for an enterprise, security policy, standard compliance, or regulatory compliance rules. Such formal vocabularies and rules can be interpreted and used by computer systems. SBVR is an integral part of the OMG's model-driven architecture (MDA).

Master data management (MDM) is a technology-enabled discipline in which business and information technology work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets.

BORO is an approach to developing ontological or semantic models for large complex operational applications that consists of a top ontology as well as a process for constructing the ontology. It was originally developed as a method for mining ontologies from multiple legacy systems – as the first stage in an architectural transformation or software modernization. It has also been used to enable semantic interoperability between legacy systems. It is described in detail in. It is the analysis method used in the development and maintenance of the U.S. Department of Defense Architecture Framework (DoDAF) Meta Model (DM2), where a data modeling working group of over 350 members was able to systematically resolve a broad spectrum of knowledge representation issues.

<span class="mw-page-title-main">Ontology engineering</span> Field which studies the methods and methodologies for building ontologies

In computer science, information science and systems engineering, ontology engineering is a field which studies the methods and methodologies for building ontologies, which encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities. In a broader sense, this field also includes a knowledge construction of the domain using formal ontology representations such as OWL/RDF. A large-scale representation of abstract concepts such as actions, time, physical objects and beliefs would be an example of ontological engineering. Ontology engineering is one of the areas of applied ontology, and can be seen as an application of philosophical ontology. Core ideas and objectives of ontology engineering are also central in conceptual modeling.

A semantic decision table uses modern ontology engineering technologies to enhance traditional a decision table. The term "semantic decision table" was coined by Yan Tang and Prof. Robert Meersman from VUB STARLab in 2006. A semantic decision table is a set of decision tables properly annotated with an ontology. It provides a means to capture and examine decision makers’ concepts, as well as a tool for refining their decision knowledge and facilitating knowledge sharing in a scalable manner.

Semantic architecture is a novel concept in software architecture which envisions enabling the architecture community to unambiguously capture, catalog, communicate, preserve, and interoperably exchange semantics of their architectures, thus making architecture descriptions true assets.

In philosophy, a process ontology refers to a universal model of the structure of the world as an ordered wholeness. Such ontologies are fundamental ontologies, in contrast to the so-called applied ontologies. Fundamental ontologies do not claim to be accessible to any empirical proof in itself, but to be a structural design pattern, out of which empirical phenomena can be explained and put together consistently. Throughout Western history, the dominating fundamental ontology is the so-called substance theory. However, fundamental process ontologies are becoming more important in recent times, because the progress in the discovery of the foundations of physics spurred the development of a basic concept able to integrate such boundary notions as "energy," "object", and those of the physical dimensions of space and time.

References

  1. 1 2 3 De Leenheer, P.; Christiaens, S.; Meersman, R. (2010). "Business semantics management: A case study for competency-centric HRM". Computers in Industry. 61 (8): 760–775. doi:10.1016/j.compind.2010.05.005.
  2. 1 2 De Leenheer, Pieter (2009). On community-based Ontology Evolution (Thesis). Vrije Universiteit Brussel.
  3. ↑ "Information Management Resources - Information Management" (PDF).
  4. ↑ Sheth, Amit (1997). "Data Semantics: what, where and how?". Proceedings of the 6th IFIP Working Conference on Data Semantics (DS-6). Chapman and Hall. pp. 601–610.
  5. ↑ Morgan, Tony (2005). "Expressing Business Semantics" (PDF). Presentation at the European Semantic Web Conference (2005). Archived from the original (PDF) on 13 May 2008. Retrieved 23 November 2008.
  6. ↑ DMReview.com. "Why is Business Semantics the New Hot Topic?". Archived from the original on 5 January 2009. Retrieved 23 November 2008.
  7. ↑ De Leenheer, Pieter (1 January 2007). "Context dependency management in ontology engineering: a formal approach". Journal of Data Semantics. VIII: 26–56.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.