Supporting public decision making in policy deliberations: An ontological approach

This is the post-print version of the Paper. The official published version can be accessed from the link below - Copyright @ 2011 SpringerSupporting public decision making in policy deliberations has been a key objective of eParticipation which is an emerging area of eGovernment. EParticipation aims to enhance citizen involvement in public governance activities through the use of information and communication technologies. An innovative approach towards this objective is exploiting the potentials of semantic web technologies centred on conceptual knowledge models in the form of ontologies. Ontologies are generally defined as explicit human and computer shared views on the world of particular domains. In this paper, the potentials and benefits of using ontologies for policy deliberation processes are discussed. Previous work is then extended and synthesised to develop a deliberation ontology. The ontology aims to define the necessary semantics in order to structure and interrelate the stages and various activities of deliberation processes with legal information, participant stakeholders and their associated arguments. The practical implications of the proposed framework are illustrated.This work is funded by the European Commission under the 2006/1 eParticipation call

Relation Discovery from Web Data for Competency Management

This paper describes a technique for automatically discovering associations between people and expertise from an analysis of very large data sources (including web pages, blogs and emails), using a family of algorithms that perform accurate named-entity recognition, assign different weights to terms according to an analysis of document structure, and access distances between terms in a document. My contribution is to add a social networking approach called BuddyFinder which relies on associations within a large enterprise-wide "buddy list" to help delimit the search space and also to provide a form of 'social triangulation' whereby the system can discover documents from your colleagues that contain pertinent information about you. This work has been influential in the information retrieval community generally, as it is the basis of a landmark system that achieved overall first place in every category in the Enterprise Search Track of TREC2006

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Ontologies and Knowledge Aggregation in the Active Semantic Learning System

5 PagesInternational audienceThe construction of semantic-based learning systems depends on the development of ontologies and the capacity to integrate and exploit knowledge using semantic technologies, notably RDF and ontologies. In this paper we present some concepts and ontologies defined in the context of the Active Semantic Learning System (Active SLS) that are used to describe resources and the semantic relation between these concepts defined in different ontologies. The purpose is to obtain a learning system that is capable of aggregating knowledge from different sources from the web and to exploiting that knowledge for the benefit of the learner

Escaping the Trap of too Precise Topic Queries

At the very center of digital mathematics libraries lie controlled vocabularies which qualify the {\it topic} of the documents. These topics are used when submitting a document to a digital mathematics library and to perform searches in a library. The latter are refined by the use of these topics as they allow a precise classification of the mathematics area this document addresses. However, there is a major risk that users employ too precise topics to specify their queries: they may be employing a topic that is only "close-by" but missing to match the right resource. We call this the {\it topic trap}. Indeed, since 2009, this issue has appeared frequently on the i2geo.net platform. Other mathematics portals experience the same phenomenon. An approach to solve this issue is to introduce tolerance in the way queries are understood by the user. In particular, the approach of including fuzzy matches but this introduces noise which may prevent the user of understanding the function of the search engine. In this paper, we propose a way to escape the topic trap by employing the navigation between related topics and the count of search results for each topic. This supports the user in that search for close-by topics is a click away from a previous search. This approach was realized with the i2geo search engine and is described in detail where the relation of being {\it related} is computed by employing textual analysis of the definitions of the concepts fetched from the Wikipedia encyclopedia.Comment: 12 pages, Conference on Intelligent Computer Mathematics 2013 Bath, U

Expert Finding by Capturing Organisational Knowledge from Legacy Documents

Organisations capitalise on their best knowledge through the improvement of shared expertise which leads to a higher level of productivity and competency. The recognition of the need to foster the sharing of expertise has led to the development of expert finder systems that hold pointers to experts who posses specific knowledge in organisations. This paper discusses an approach to locating an expert through the application of information retrieval and analysis processes to an organization’s existing information resources, with specific reference to the engineering design domain. The approach taken was realised through an expert finder system framework. It enables the relationships of heterogeneous information sources with experts to be factored in modelling individuals’ expertise. These valuable relationships are typically ignored by existing expert finder systems, which only focus on how documents relate to their content. The developed framework also provides an architecture that can be easily adapted to different organisational environments. In addition, it also allows users to access the expertise recognition logic, giving them greater trust in the systems implemented using this framework. The framework were applied to real world application and evaluated within a major engineering company

Multivalent Metadata : Exploiting the Layers of Meaning in Digital Resources

The rapid growth of the World Wide Web was due in part to the simplicity of the Hypertext Markup Language (HTML). It is anticipated that the next generation of web technology, coined the Semantic Web, by Tim Berners-Lee (1989, p. 1), will be driven by the Extensible Markup Language (XML). The XML suite of technologies provides a framework for the application of metadata, and hence semantic information, to web resources. Advantages of a semantic web include improved sharing and reuse of resources, enhanced search mechanisms and knowledge management. The knowledge or meaning contained in digital information may vary according to the perspective of the viewer and can be seen therefore as multivalent in nature. Semantic information that is highly relevant to one user may be of no interest to another. The aim of this project was to demonstrate the layers of meaning inherent in a data sample and how they could be encapsulated in metadata then accessed and manipulated using current technologies, thus leveraging the knowledge contained. Analysis of the data sample, a typical component of an online training product, determined meaningful ways in which the knowledge contained could be reused and adapted. From this analysis a set of test criteria was generated. Metadata was then created for the sample data and the tests implemented using a range of XML technologies

Felsőoktatási portfolió kompetencia alapon történő tervezése = Design of Higher Education Portfolio

Mind nemzetközi, mind hazai szinten olyan tendenciák érzékelhetők, amelyek a felsőoktatás munkaerő-piaci igényeknek megfelelő átstrukturálását igénylik. A kutatás során kifejlesztett rendszer egy olyan megoldási lehetőségre világított rá, amely a felsőoktatás átstrukturálását érintő, jelenleg is aktuális közigazgatási döntés-előkészítési folyamatot más megközelítésből tudná támogatni, azaz a munkaerőpiac keresleti és kínálati oldalának képzettségre vonatkozó egyezőségeinek, illetve különbözőségeinek a feltárásával. A kompetenciára épülő modellek, valamint a képesítési keretrendszerek átláthatóbbá, tervezhetőbbé teszik mind a szervezeti tevékenységeket, mind a képzési programokat, ezért a kompetencia megfelelő alapot szolgáltat a munkaerőpiac két oldalán megjelenő szakma-, illetve foglalkozási struktúra összehasonlítására. Mivel a szakirodalomban a fogalomnak nincs egyezményes definíciója, attól függően kerül használatba, hogy milyen tevékenység (szerepkörhöz kapcsolódó feladat elvégzése, vagy tanulás) elvégzése érdekében kerül aktiválásra. Ezért a kutatásban, a mögöttes tartalmát legjobban reprezentáló tudás-képesség-attitűd hármasként lett interpretálva, amely valamilyen feladat elvégzéséhez kapcsolódik. A rendszer ontológia alapú megközelítésben történő fejlesztését támasztja alá, az hogy az ontológia konzisztens alapot biztosít az összehasonlításra, valamint az egyes, ugyanerre a kutatási területre irányuló, továbbfejleszteni kívánt rendszerek is ebben a szemléletben készültek el. A kutatás során egy prototípus került kidolgozásra, amely a BCE Gazdaságinformatikus képzésének, valamint a hozzá kapcsolódó IT munkakörök közül a Szoftverfejlesztő munkakör kompetenciatartalmait kívánta összehasonlítani. A rendszer fejlesztése a kétfázisú inkrementális rendszerfejlesztési módszertan lépéseit követi, vagyis minden egyes szakaszban kifejlesztett részrendszer értékelésre került, amelyhez kapcsolódóan, az eredeti követelményeket az ott felmerült igényekkel kiegészítve került sor a második részrendszer előállítására. A kutatás legvégén a jelenleg is zajló döntés-előkészítési folyamatba való beilleszthetőségét vizsgáltam meg

Method and Instruments for Modeling Integrated Knowledge

MIMIK (Method and Instruments for Modeling Integrated Knowledge) is a set of tools used to formalize and represent knowledge within organizations. It furthermore supports knowledge creation and sharing within communities of interest or communities of practice. In this paper we show that MIMIK is based on a model theory approach and builds on other existing methods and techniques. We also explain how to use the method and its instruments in order to model strategic objectives, processes, knowledge, and roles found within an organization, as well as relations existing between these elements. Indeed MIMIK provides eight types of models in order to describe what is commonly called know-how, know-why and know-what; it uses matrices in order to formally and semantically link strategic objectives, knowledge and actors. We close this paper with a presentation of a prototype we built in order to demonstrate a technical architecture allowing for knowledge creation, formalization and sharing.knowledge modelling; process modelling; public administration; methodology; knowledge sharing; RSS