A Generative Programming Approach to Interactive Information Retrieval: Insights and Experiences

We describe the application of generative programming to a problem in interactive information retrieval. The particular interactive information retrieval problem we study is the support for out-of-turn interaction with a website – how a user can communicate input to a website when the site is not soliciting such information on the current page, but will do so on a subsequent page. Our solution approach makes generous use of program transformations (partial evaluation, currying, and slicing) to delay the site’s current solicitation for input until after the user’s out-of-turn input is processed. We illustrate how studying out-of-turn interaction through a generative lens leads to several valuable insights: (i) the concept of a web dialog, (ii) an improved understanding of web taxonomies, and (iii) new web interaction techniques and interfaces. These notions allow us to cast the design of interactive (and responsive) websites in terms of the underlying dialog structure and, further, suggest a simple implementation strategy with a clean separation of concerns. We also highlight new research directions opened up by the generative programming approach to interactive information retrieval such as the idea of web interaction axioms

The Partial Evaluation Approach to Information Personalization

Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a conceptual level and outline representational choices. We present two application case studies that use PIPE for personalizing web sites and describe how PIPE suggests a novel evaluation criterion for information system designs. Finally, we mention several fundamental implications of adopting the PIPE model for personalization and when it is (and is not) applicable.Comment: Comprehensive overview of the PIPE model for personalizatio

How To Build Enterprise Data Models To Achieve Compliance To Standards Or Regulatory Requirements (and share data).

Sharing data between organizations is challenging because it is difficult to ensure that those consuming the data accurately interpret it. The promise of the next generation WWW, the semantic Web, is that semantics about shared data will be represented in ontologies and available for automatic and accurate machine processing of data. Thus, there is inter-organizational business value in developing applications that have ontology-based enterprise models at their core. In an ontology-based enterprise model, business rules and definitions are represented as formal axioms, which are applied to enterprise facts to automatically infer facts not explicitly represented. If the proposition to be inferred is a requirement from, say, ISO 9000 or Sarbanes-Oxley, inference constitutes a model-based proof of compliance. In this paper, we detail the development and application of the TOVE ISO 9000 Micro-Theory, a model of ISO 9000 developed using ontologies for quality management (measurement, traceability, and quality management system ontologies). In so doing, we demonstrate that when enterprise models are developed using ontologies, they can be leveraged to support business analytics problems - in particular, compliance evaluation - and are sharable

Dealing with uncertain entities in ontology alignment using rough sets

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision