Posts Tagged ‘methodology’

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 Many others are rather outdated prototypes, not “usable and reusable”, and unworthily categorised ontologies as an agreement on the schema vocabulary is non-existing.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.


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

Ontology Elicitation in Springer Encyclopedia of Database Systems

January 15, 2009

The Encyclopedia of Database Systems (edited by Ling Liu and M. Tamer Özsu) will be a comprehensive reference to topics in database systems for students, researchers and practitioners who need a quick and authoritative reference to the subject of databases, data management, and database systems, such as basic concept definition, data processing algorithms, key results to date, and references to source materials. The encyclopedia will feature an alphabetical organization of nearly 1000 entries, covering both topics of current interest and key research results of historical significance in all the main areas of database systems.

Publication by Springer is planned for April 2009; the Encyclopedia of Database Systems will be available as a printed volume and an online reference work.

I was invited to write the entry for Ontology elicitation. deleenheer_edb_2007_fig2Ontology elicitation embraces the family of methods and techniques to explicate, negotiate, and ultimately agree on a partial account of the structure and semantics of a particular domain, as well as on the symbols used to represent and apply this semantics unambiguously. Ontology elicitation only results in a partial account because the formal definition of an ontology cannot completely specify the intended structure and semantics of each concept in the domain, but at best can approximate it. Therefore, the key for scalability is to reach the appropriate amount of consensus on relevant ontological definitions through an effective meaning negotiation in an efficient  manner. In this entry we give definitions, historical background, scientific fundamentals, key applications, and finally future directions for ontology elicitation.

This reference is designed to address the needs of a wide audience including researchers, graduate and undergraduate students, and other professionals and practitioners who might need speedy and reliable information in the databases, data management, and database systems subject area. We anticipate many to benefit from this reference, including database specialists, software developers, scientists and engineers who need to deal with (structured, semi-structured or unstructured) large datasets. In addition database and data mining researchers and scholars in the many areas that apply database technologies, such as artificial intelligence, software engineering, robotics and computer vision, machine learning, finance and marketing are expected to benefit from the encyclopedia.

This home page is being updated continuously during the course of this project. The Editor-in-Chiefs and advisory board value feedback from the database community concerning every aspect of the Encyclopedia of Database Systems.

De Leenheer, P. (2009) Ontology Elicitation. In Encyclopedia of Database Systems, eds. Liu, L. and Ôzsu, T., Springer, forthcoming Spring 2009.

Ontology Evolution: State of the Art and Future Directions

January 19, 2008

The research area of ontology engineering seems to have reached a certain level of maturity, considering the vast amount of contemporary methods and tools for formalising and applying knowledge representation models. However, there is still little understanding of, and support for, the evolutionary aspects of ontologies. This is particularly crucial in distributed and collaborative settings such as the Semantic Web, where ontologies naturally co-evolve with their communities of use. For managing the evolution of single ontologies, established techniques from data schema evolution have been successfully adopted, and consensus on a general ontology evolution process model seems to emerge. Much less explored, however, is the problem of evolution of interorganisational ontologies. In this “complex” and dynamic setting, a collaborative change process model requires more powerful engineering, argumentation and negotiation methodologies, complemented by support for context dependency management.. It turns out that much can be learned from other domains where formal artefactsucps are being collaboratively engineered. In particular, the field of system engineering offers a wealth of techniques and tools for versioning, merging and evolving software artefacts, and many of these techniques can be reused in an ontology engineering setting. Based on this insight, this chapter gives a unified overview of the wide variety of models and mechanisms that can be used to support all of the above aspects of ontology evolution. The key remaining challenge is to construct a single framework, based on these mechanisms, which can be tailored for the needs of a particular version environment.

De Leenheer, P. and Mens, T. (2008) Ontology Evolution: State of the Art and Future Directions. In Ontology Management for the Semantic Web, Semantic Web Services, and Business Applications, from Semantic Web and Beyond: Computing for Human Experience”, eds. Hepp, M., De Leenheer, P., de Moor, A., and Sure, Y.,Springer

DOGMA-MESS: A Meaning Evolution Support System for Interorganizational Ontology Engineering

June 17, 2006

de Moor, A., De Leenheer, P., and Meersman, R. (2006) DOGMA-MESS: A Meaning Evolution Support System for Interorganizational Ontology Engineering . In Proc. of the 14th Int’l Conference on Conceptual Structures (ICCS 2006) (Aalborg, Denmark), LNAI 4068, Springer, pp. 189-203.

In this paper, we explore the process of interorganizational ontology engineering. Scalable ontology engineering is hard to do in interorganizational settings where there are many pre-existing organizational ontologies and rapidly changing collaborative requirements. A complex socio-technical process of ontology alignment and meaning negotiation is therefore required.   In particular, we are interested in how to increase the efficiency and ioe2relevance of this process using context dependencies between ontological elements. We describe the DOGMA-MESS methodology and system for scalable, community-grounded ontology engineering. We illustrate this methodology  with examples taken from a case of interorganizational competency ontology evolution in the vocational training domain.

Disambiguation of Natural-language Terms in Business Semantics Engineering

July 10, 2005

De Leenheer, P. and de Moor, A. (2005) Context-driven Disambiguation in Ontology Elicitation. In Shvaiko P. & Euzenat J.,(eds.), Context and Ontologies: Theory, Practice and Applications, AAAI Technical Report WS-05-01 (Pittsburgh, Pennsylvania), AAAI Press, pp. 17–24.


Illustration of the two levels in DOGMA ontology: on the left -- the lexical level, lexons are elicited from various contexts. On the right, there is the conceptual level consisting of a concept definition server. The meaning ladder in between illustrates the articulation of lexical terms into concept definitions.

Ontologies represent rich semantics in a lexical way. Lexical labels are used to identify concepts and relationships, though there is no bijective mapping between them. Phenomenons such as synonyms and homonyms exemplify this, and can result in frustrating misunderstanding and ambiguity. In the elicitation and application of ontologies, the meaning of the ontological knowledge is dependent on the context. We consider the role of context in ontology elicitation by introducing context in a concept definition server for ontology representation. We also adopt other features of context found in literature, such as packaging of knowledge, aligning elements of different contexts, and reasoning about contexts. Finally, we illustrate context-driven ontology elicitation with a real world case study.

This paper was further extended in Journal of Data Semantics. See my related blog in this. In our business semantics management approach, disambiguation forms an important but difficult exercise during the consolidation activities.