An important problem for many kinds of knowledge systems is their initial set-up. It is difficult to choose the right information to include in such systems, and the right information is also a prerequisite for maximizing the uptake and relevance. To tackle this problem, most developers adopt heavyweight solutions and rely on a faithful continuous interaction with users to create and improve content. In this paper, we explore the use of an automatic, lightweight ontology-based solution to the bootstrapping problem, in which domain-describing ontologies are analysed to uncover significant yet implicit relationships between instances. We illustrate the approach by using such an analysis to provide content automatically for the initial set-up of an organizational memory

Alani, Harith

Kalfoglou, Yannis

O'Hara, Kieron

Shadbolt, Nigel

English

Southampton (e-Prints Soton)

6Integration between Quickstep and ONTOCOPI has been investigated(Middleton et al 2002), using ONTOCOPI to help with the bootstrapping problem fornew users. Upon start-up, an ontology gives Quickstep an initial set of publicationsfor each user. Each user’s known publications are correlated with Quickstep’s paperdatabase, and a set of interests is then generated for the users. This provides one wayof overcoming the initialisation problem for the new system. Then when a new useris added the ontology provides the publication list of the new user (i.e. the new user’spapers that have already made it into the ontology), and ONTOCOPI provides theCoP of the new user, i.e. performing an ONA using the new user as a starting node.The CoP of the user, in this case a list of the most similar users, can then feed into acorrelation between the user’s history of publication and similar user profiles to forman initial profile of the new user.2.3.2 Referential IntegrityA second example application area for ONTOCOPI is within ontology developmentitself. Since ontologies will be a central technology for the development of thesemantic web, we can expect to see more being developed, and, in particular, morebeing created for hybrid domains by merging legacy ontologies, databases, and otherinformation stores. In that case, it is inevitable that there will be problems preservingreferential integrity across the components of the merger; the same object/conceptmay be referred to with different names (e.g. Alani, Harith and H. Alani), oralternatively different objects may be referred to using the same name (e.g. two JohnSmiths). Although it is often possible to live with such referential confusion, clearlythe use of an ontology and any knowledge service relying on it will be less efficient ifit does not have referential integrity. Even something as minor as a typographicalerror could impair effectiveness.The field of ontology-merging tools and techniques has shown that evenrelatively unsophisticated heuristics for uncovering conflicts can be quite effective,and this fact has been exploited in the development of a stepwise approach (Alani etal 2002b). To begin with, an ontology is populated, using semi-automatic methods,from existing resources; then soft string similarity measures and a set of genericheuristics (e.g. if two different names have equivalent personal attributes; telephonenumber, address, email, etc., the entities could be identical) are used to clusterpotential instance duplicates.This leads onto a second step, in which ONA is used to analyse the connectionsbetween the instances thus clustered. Using the clustered instances as the startingnodes of the analysis, the CoPs of the instances can be calculated, and the degree ofoverlap between the CoPs can be calculated as a similarity value; if the value is oversome threshold, then the two instances can be assumed to be identical. Naturally,such an approach is bound to fail on some occasions, particularly for names that arenot well connected within the ontology. The more connected the individuals, themore likely it is that the similarity of their CoPs will be correlated with their identityor distinctness.This approach is currently under test within the Advanced KnowledgeTechnologies (AKT 2001), using a reference ontology created from legacy resourcessuch as personnel databases, publications databases, and so on, harvested from thewebsites initially of the AKT partners, and intended ultimately to serve the UKcomputer science community with a repository of knowledge about, for example,declared areas of research or currently active projects. The scale of such anapplication will inevitably involve referential promiscuity, and will give plenty ofscope for testing the use of ONA for preserving referential modesty.1325. Zave, P. and Jackson, M. Four Dark Corners of Requirements Engineering. ACMTransactions on Software Engineering and Methodology, 6(1), pp. 1-30, 1997.

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Open Research Online (The Open University)

Initiating organizational memories using ontology-based network analysis as a bootstrapping tool

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.5271

Initiating organizational memories using ontology-based network analysis as a bootstrapping tool

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