Posts Tagged ‘semantic interoperability’

The role of Semantics in the future Internet

May 12, 2009

Here is an easy movie that presents the future Internet.  In my article  that was published last year at the Future Internet Symposium 2008 held in Vienna, Austria I discussed to role of business semantics management in here, and the need for a kind of DNS of information consisting of data services compiled from and orchestrated by semantic patterns . See my blog about this here

Principles of ICT Democracy: can communities themselves build their ontologies ?

March 4, 2009

The second-generation Web (2.0) is a complex socio-technical system of unforeseen growth and dynamics. On-line communities emerge and interact all around a usually self-organising manner supported by interactive applications, including bookmarking, tagging, blogging, and wikis, being developed and shared at little or no cost.  The emerging range of Semantic Web and other open technologies promises an increase in scale and maturity of knowledge sharing, achieved through collaboration and integration within and between diverse communities.

Considering the current pace of social and technological development, it seems that the transformation of the Web from a network of separately siloed applications and content repositories to a more seamless and semantically interoperable ecosystem is at hand, opening a wide range of scientific challenges and opportunities. However, while simple, the idea of the Semantic Web remains largely unrealised.

Semantic interoperability is the ability of two or more information systems or their (computerised) components to communicate data and to interpret the information in the data that has been communicated both in a meaningful manner, that is by means of an ontology that is shared by the involved information systems.

The ontologies that will furnish the Semantic Web are lacking, and those few that have been published are usually not based on consensus, and hence unreliable and not reusable beyond individual purposes. Of those domain vocabularies that are published on the Web, only some of them are actively maintained and thus reflect the current domain. Many others are rather outdated prototypes, not “usable and reusable”, and unworthily categorised ontologies as an agreement on the schema vocabulary is non-existing. Current techniques that claim to create semantic interoperability are unsatisfactory, both theoretically and as far as the quality of the results is concerned. In current ontology engineering practice, the underlying methodological pinciples are mostly ignored.

In current ontology engineering practices, the underlying methodological principles are mostly ignored. Furthermore, they systemically disregard the subtle gap that looms between knowledge sharing among people at the community/social level on the one hand; and information exchange between computer systems at the operational/technical level (see figure below).

The gap between knowledge sharing between human beings as an act of socialisation and information exchange between computer systems.

The gap between knowledge sharing between human beings as an act of socialisation and information exchange between computer systems.

Architecting community-driven internet systems will require a paradigm shift that goes beyond mere technological fits. In order to bridge the gap between the social and technical part of the community, one must put into practice the necessary activities to identify common needs from socialisation activities and bring the stakeholders together to find and ontological agreement to support these needs.

Community-based ontology evolution establishes the co-evolution of (A)  social interactions enabled by the community’s design; (B) the information systems that support them; and (C) the semantic patterns to fulfil semantic interoperability between these systems.

In order to enact this co-evolution we start from the following seven principles that every approach should keep in mind:

  1. ICT Democracy An ontology should be defined by its community, and not by a single developer.
  2. Autonomy Semantic interoperability requirements emerge autonomously from community evolution processes.
  3. Co-evolution Ontology evolution processes are driven by the changing semantic interoperability requirements.
  4. Perspective Rendering Ontology evolution processes must reflect the various stakeholders’ perspectives.
  5. Perspective Unification In building the common ontology, relevant parts of the various stakeholder perspectives serve as input for the unified perspective.
  6. Validation The explicit rendering of stakeholders perspectives allows us to capture the ontology evolution process completely, and validate the ontology against these perspectives respectively.
  7. Satisfaction Ultimately, co-evolving communities with their ontology will increase overall stakeholder satisfaction.

Community/business semantics management is our approach to enact community-based ontology evolution.

The Semantic Wave: Messing Around or Hitting the Surf?

February 4, 2009

While we (= Collibra) are  warming up and working out our showcase for SemTech09 in June in the Valley, Tony Shaw invited us to write an article for his revamped Semantic Universe blog. You can find the article here as well.

In today’s business ecosystems, information has become a competitive and strategic asset. Being able to exchange data and to interpret the information in the data that has been exchanged in the right context and within a reasonable time is a top priority for many organizations. Starting from three simple but serious questions regarding data semantics, data utilization, and data governance that pop up daily in information-intensive enterprises, we easily identify a value proposition for semantic alignment. However, current techniques that claim to create semantic alignment in this sense are unsatisfactory, both theoretically and as far as the quality of the results is concerned.

The looming information sharing gap hampering efficiency
The looming information sharing gap difficult to answer the following three simple but critical questions about any data asset describing a part of your organization: (1) what does my data mean? (2) where and how is my data utilized? (3) who is responsible for my data?

They systemically ignore the subtle gap (see figure above) that looms between knowledge sharing among people at the business/social level on the one hand; and information exchange between computer systems at the operational/technical level on the other hand. A solution requires organizations to look beyond mere technical fits and think in terms of mechanisms that transcend their IT infrastructure to a sustainable information-centric infrastructure (see figure below) that meaningfully aligns business with IT. To achieve this goal, we pinpoint two essential requirements: business semantics management and data services.

business semantics management and data services. The illustration below shows the transition from an ad-hoc infrastructure to an information-centric infrastructure to achieve semantic

Our semantic alignment vision is closely related to what Gartner labels as the “Information-centric Infrastructure”. An information-centric infrastructure is the alignment of metadata, standards, content formats and applications to support consistent and seamless enterprise-wide information capture, persistence, transformation and delivery. There are two technologies necessary to implement this vision: business semantics management and data services. The illustration below shows the transition from an ad-hoc infrastructure to an information-centric infrastructure to achieve semantic alignment.

Three simple questions

Current techniques that claim to create semantic alignment are messing around, both theoretically and as far as the quality of the results is concerned1. Semantic alignment is hampered by the usually ignored gap that looms between information sharing among people (i.e. knowledge sharing) at the business/social level on the one hand; and information sharing between computer systems (i.e. data exchange) at the operational/technical level on the other hand (see figure below).

This gap makes it difficult to answer the following three simple but critical questions about any data asset describing a part of your organization:

(i) What does my data mean?

Data has no informational value without meaningful interpretation. It is impossible to share data with business partners or customers. It is difficult to exploit data in order to answer strategical questions. Without understanding, it is impossible this way to compare your data and derive intelligence. Understanding what data means is a time-consuming exercise. As the meaning (that is the semantics) is not made explicit this has to be repeated over and over. This is aggravated by the fact that in the course of this “chain of inquiries for data clarification” many colleagues are wrongly interrupted before that single person who actually does understand the data has been spot by chance.

(ii) Where and how is my data utilized?

For many organizations, it is far from clear where their data is located and how many copies are available. Moreover they lack any overview of how their data is related to each other, which applications access or manipulate their data. Maintaining applications is a time-frittering job. Moreover, most organizations carry the legacy of an information infrastructure that has grown organically over the years, or has gone through a series of refactorings due to organizational restructuring, splits and merges. On top of this, information is usually stored in the minds of employees. While this kind of situation already results in a very inefficient way of handling data, this poses even greater risks when these persons would leave the organization.

(iii) Who is responsible for my data?

In today’s business ecosystems, trust is considered extremely valuable. Gaining the confidence from your business partners and other stakeholders is achieved uniquely through data governance. Being able to pinpoint a person that holds ownership for a certain data asset, knowing how this asset has evolved over time, knowing and being able to track who has changed what and when, are all abilities that are key for genuine governance.

Semantic Alignment

What is semantic alignment?

Bridging the gap between the business/social layer and the technical/operational layer of an organization would reduce daily operational costs and create new opportunities for value creation, both now and in the future. We define semantic alignment as “the ability to exchange data between these layers and to interpret the information in the data that has been exchanged in the right context and within a reasonable time”.

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As illustrated above, semantic alignment creates added value at three different dimensions:

1. It empowers knowledge sharing between business stakeholders through more accurate information delivery. For example, semantic alignment enables better search, navigation, discovery, content management, web sites, and many other knowledge-intensive applications.

2. It empowers data exchange between disparate systems as it takes care of the automatic transformation between data formats. This provides a better foundation for many other initiatives such as master data management, business intelligence, SOA, BPM, etc.

3. It enables the alignment of business and IT by explicating the meaning, usage and whereabouts of all organizational data assets.

How can we achieve semantic alignment?

With the emergence of information as a valuable asset, it is metadata that becomes key to the valuation and leveraging that value, yet most organizations manage metadata in a very ad hoc manner.2Business semantics management as an approach to bring business partners together to realize the reconciliation of their heterogeneous metadata; and consequently apply the derived business semantics patterns to establish semantic alignment.

Business semantics management does for information what business process management does for processes. Semantic Alignment indeed has many similarities to what business process management aims to do for business processes. Another, more technical term for business semantics management would be business metadata management.

Business Semantics

Business semantics are (business) metadata that describe the information concepts that live within the organization. An important difference with other approaches is that our business semantics are modeled according to a fact-oriented paradigm that was introduced by the conceptual modeling approach NIAM, the predecessor of Object Role Modeling (ORM). The use of natural language, example populations, and the description of information in terms of elementary facts enhance the potential for re-use and design scalability during business semantics management. The following figure shows an arbitrary semantic pattern composed of four elementary fact types, that illustrates the graphical power and simplicity of a fact-oriented approach.

The graphical representation is extremely easy to read. From left to right, the above pattern reads:

An Order Report is responsibility of a Warehouse Manager.

An Order Report enlists an Order which takes a Product.

Methodology

By default, business semantics serve “open” information systems, and hence the requirements and limitations of semantic alignment cannot be entirely known before completion. In contrast to waterfall-like approaches that focus on a broad design upfront, agile methods perform short milestone driven revision iterations in order to cope with dynamic environments such as the extended enterprise. Full-cycle business semantics management is established by two operational cycles each grouping a number of activities. This is illustrated below.

1. Semantic Reconciliation In this cycle, business semantics are modeled by extracting, refining, articulating and consolidating fact-types from existing sources such as natural language descriptions, existing metadata, etc. Ultimately, this results in a number of consolidated language-neutral semantic patterns that are articulated with informal meaning descriptions (e.g., WordNet word senses). These patterns are reusable for constructing various semantic applications.

2. Semantic Application During this cycle, existing information sources and services are committed to a selection of semantic patterns. This is done by selecting the relevant patterns, constraining their interpretation and finally mapping (or committing) the selection on the existing data sources. In other words, a commitment creates a bidirectional link between the existing data sources and services and the business semantics that describe the information assets of an organization. The existing data itself is not moved. On the contrary, the business semantics provide a kind of abstraction layer to access and deliver this data in a more efficient and aligned manner.

Architecture & Positioning

Our semantic alignment vision is closely related to what Gartner labels as the “Information-centric Infrastructure”. An information-centric infrastructure is the alignment of metadata, standards, content formats and applications to support consistent and seamless enterprise-wide information capture, persistence, transformation and delivery. There are two technologies necessary to implement this vision: business semantics management and data services. The illustration below shows the transition from an ad-hoc infrastructure to an information-centric infrastructure to achieve semantic alignment.

Business Semantics Management

Business semantics management is the human-driven part of implementing the semantic alignment vision. Using business semantics, existing disparate data and service sources are annotated with rich semantic patterns that establish the meaning of the information assets. It also supports other human-centric involvement such as governance and stewardship. 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. For example they can be combined with a content management application to provide a consistent business vocabulary and enable better navigation or archiving of documentation. This can be further complemented by enterprise search engines, and richer Semantic-Web ready websites, etc.

Data Services

Data services replace the old practices such as EAI (Enterprise Application Integration), EII (Enterprise Information Integration), B2B Integration, and ETL (Extract Transform Load). Technically, they fit better in a Service Oriented Architecture. This is hence often called service-oriented integration.

Applications can leverage the business semantics to enable semantically rich data services including context services (how, where and by whom is this information asset used), definition services (what does this information concept mean in this particular context) and integration services (where is this information stored, transform a piece of data from one format to another, …). This enables these data services to deliver the right information at the right time in the right context and in the required format.

Why would we need semantic alignment ?

The benefits of semantic alignment are twofold:

Documentation

Semantic alignment makes it possible for any stakeholder to answer the following three simple but serious questions: (i) what does it mean; (ii) where and how is it utilized; and (iii) who is responsible for it? How does such a comprehensive overview on your enterprise information creates value for your business:

Reduced Cost: The chain of inquiries for data clarification is maximally reduced. Hence, this minimizes operations, cuts cost and time, and lowers the frustration caused by repeated data misinterpretations.

Less Maintenance: Using business semantics management, (legacy) data sources and services are consistently and clearly documented. Furthermore, because this bidirectional link is also used operationally in the actual information systems, this form of documentation always remains up to date.

Faster time to market: a complete information overview allows to adapt and extend systems seamlessly to address new market requirements.

Smooth integration with M&As: a comprehensive overview of the information makes a smoother merging of departments or organizations possible.

Operational and Technical Efficiency

Knowing the answer to the above three questions would not only be valuable for human stakeholders. Business semantics models must also be put to operation by committing them to the underlying (legacy) data sources or services. This provides the following benefits:

Automatic data transformation: Automatic transformation from one format to another is possible as long as these formats are documented by business semantics. This is the first step towards a linearly scaling solution for an exponentially scaling problem.

Better SOA / BPM: Through Service Oriented Architecture and Business Process Management, many organizations have tried to introduce conceptual insight, understanding, and flexibility to their enterprise architecture. However, information still remains the foundation on which these initiatives are built. Many experts agree that a lot of investments into SOA and BPM will not provide the promised benefits when people forget about the data semantics. Business semantics management provides the unique straight way to support these initiatives and actually deliver on its promises.

Enterprise 2.0: The recent trend towards Enterprise 2.0 gives power to the users to better share information with their peers and use enterprise applications like they use their consumer applications at home. For the IT department, it is not straightforward to support this trend and embed it safely into the overall enterprise architecture. Through semantic alignment, however, business users are empowered to create custom applications (also called mashups) to efficiently share and deliver information. The information assets from the organization are made available for them through business semantics in a way they understand so they can use it without the involvement of IT. However, IT is still in charge to map (or commit) these business semantics to the underlying source data and services.

Cloud Computing: Another emerging trends is cloud computing. It is however highly undesirable to directly connect data that is stored in the cloud to the processes and business logic of the organization. Semantic alignment, creating an abstraction layer between both, is the ideal solution to use and leverage information from the cloud safely and efficiently into the organization.

An information-centric infrastructure: Gartner describes an information-centric architecture as the alignment of metadata, standards, content formats, and applications to support consistent and seamless enterprise-wide information capture, persistence, transformation and delivery. Gartner envisions the information-centric architecture as a key building block for Enterprise Information Management. Business semantics Management is the only way for organizations to convert smoothly to such an information-centric infrastructure.

Outsourcing: Maximize the ability to outsource by keeping the business knowledge local through information models, policies, and rules, and decouple these from implementation code.

Conclusion

Being able to exchange data and to interpret the information in the data that has been exchanged in the right context and within a reasonable time is a top priority for many organizations. In this paper, we set a value proposition for semantic alignment. In terms of three simple but serious questions about data semantics, utilization, and governance typically posed in information-intensive organizations.

Current techniques that claim to create semantic alignment in this sense are disappointing, both theoretically and as far as the quality of the results is concerned. They deny the subtle gap that looms between information sharing among people (i.e. knowledge sharing) at the business/social level on the one hand; and information sharing between computer systems (i.e. data exchange) at the operational/technical level on the other hand.

A solution requires organizations to look beyond mere technical fits and think in terms of mechanisms that transcend their IT infrastructure(s) towards a sustainable information-centric infrastructure that aligns business with IT. To this end, semantic alignment must be implemented with methods and tools for business semantics Management and data services. Through accurate data understanding and governance, and knowledge sharing, organizations are empowered to optimize their operations. Moreover, top-line growth is guaranteed by leveraging previous investments, improving development of new applications, and ultimately increasing shareholder value.

1. “The market lacks tools capable of rationalizing business process models, logical information models and repository man-agement tools for automated semantic resolution in SOA.” Gartner, The Emerging Vision for Data Services: Logical and Se-mantic Management
2. Mark A. Beyer, Gartner

Business Semantics Management: a Case Study for Competency-centric HRM

January 16, 2009

In this article we introduce a novel approach and tool for fact-oriented business semantics management that is inspired by agile design methods. We demonstrate and validate it in a realistic case study that was carried out within the European Codrive project. Codrive’s vision was to contribute to more meaningful competency-centric human resource management. Key challenges are the uniform publication of unambiguous competency information and “time-to-competency” agility. To this end, we developed a shared and formal knowledge representation of competency domains. Stakeholders include educational institutes, public employment organisations, and industry partners from different European countries. The resulting Vocational Competency Ontology wanted to provide a candidate best practice for engineering a community-shared and reusable semantic pattern base that can be applied by all stakeholders to semantically reconcile their contextualised competency models.businesssemanticsmanagement De Leenheer, P., Christiaens, S., and Meersman, R. (2009) Business Semantics Management: a Case Study for Competency-centric HRM. In Journal of Computers in Industry: Special Issue about Semantic Web Computing in Industry. Elsevier, forthcoming

Collaboration across the Enterprise: an Approach for Enterprise Interoperability

June 16, 2008

Liapis, A., Christiaens, S., De Leenheer, P. (2008) Collaboration across the Enterprise: an Approach for Enterprise Interoperability, In Proc. of ICEIS 2008 (Barcelona, Spain)

In the current competitive industrial context, enterprises must react swiftly to market changes. In order to face this problem, enterprises must increase their collaborative activities. This implies at one hand high communication between their information systems and at the other hand the compatibility of their practices. An important amount of work must be performed towards proper practices of standardization and harmonization. This is the concept of Interoperability. Interoperability of enterprises is a strategic issue, caused as well as enabled by the continuously growing ability of integration of new legacy and evolving systems, in particular in the context of networked organisations. of the reconciliation of the communicated business semantics is crucial to success. For this, non-disruptive re-use of existing business data stored in “legacy” production information systems is an evident prerequisite. In addition the integration of a methodology as well as the scalability of any proposed semantic technological solution are equally evident prerequisites. Yet on all accounts current semantic technologies as researched and developed for the so-called Semantic Web may be found lacking. Still, semantic technology is claimed about to become mainstream, as it is pused by enterprise interoperation needs and increasing availability of domain specific content (for example ontologies) rather than pulled by basic technology (for example OWL) providers. In this paper we present a methodology which has resulted in the implementation of a highly customizable collaborative environment focussed to support ontology-based enterprise interoperability. The main benefit of this environment is its ability to integrate with legacy systems, rescuing enterprises from having to adapt or upgrade their existing systems in order to interoperate with their partners.