Highlighting the Gaps in Enterprise Systems Models by Interoperating CGs and FCA

Enterprises arise from creative human endeavours, articulated through business concepts encoded in enterprise information systems through a modular Enterprise information Model (EIM). The EIM thus brings the productivity of computers to bear. Essentially, the EIM represents conceptual structures, which align the computer's structured way of working with the human's conceptual way of thinking. Using an industrial-strength SAP exemplar known as 'Global Bike Inc.', and expressing its EIM's meta-objects as meta-object ! relation ! metaobject, Conceptual Graphs (CGs) simplified the EIM's modules, which consist of four business layers and two information systems layers. The logical simplification of these modules is extended into four levels of detail that culminate in performance indicators being assigned to each of the six layers. From the CGs, Formal Concept Analysis (FCA)'s CGtoFCA algorithm was used to generate the meta-objectarelation ! meta-object binaries that identified the pathways layer-wise and level-wise between the meta-objects. Through the interoperability of CGs and FCA, gaps in the conceptual structure of the EIM as highlighted by its performance indicator or measure, implying that the layer is not as modular as intended

The EMPRISES pan-European Framework:

There is a need for further integration of information systems globally for tackling Serious Organised Economic Crime (SOEC). Taking Europe as the illustration, and levering existing pan-EU (European Union) systems such as Europol's SIENA and the FIU.NET as well as national systems, further steps can be taken to provide a more coherent and coordinated approach for detecting and deterring SOEC. This aim is achievable through the EMPRISES framework, which adds value to national, SIENA and FIU.NET systems by increasing the effectiveness of communication across Europe. EMPRISES would introduce an agreed common language (taxonomy) of SOEC, including multi-lingual support. Moreover, by enriching the taxonomy with current business tools and analysis techniques through the SOEC Architecture that EMPRISES embodies, the illegitimate businesses of SOEC can be monitored and combatted

FCAIR 2012 Formal Concept Analysis Meets Information Retrieval Workshop co-located with the 35th European Conference on Information Retrieval (ECIR 2013) March 24, 2013, Moscow, Russia

International audienceFormal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classifiation. The area came into being in the early 1980s and has since then spawned over 10000 scientific publications and a variety of practically deployed tools. FCA allows one to build from a data table with objects in rows and attributes in columns a taxonomic data structure called concept lattice, which can be used for many purposes, especially for Knowledge Discovery and Information Retrieval. The Formal Concept Analysis Meets Information Retrieval (FCAIR) workshop collocated with the 35th European Conference on Information Retrieval (ECIR 2013) was intended, on the one hand, to attract researchers from FCA community to a broad discussion of FCA-based research on information retrieval, and, on the other hand, to promote ideas, models, and methods of FCA in the community of Information Retrieval

Découverte de définitions dans le web des données

In this thesis, we are interested in the web of data and knowledge units that can be possibly discovered inside. The web of data can be considered as a very large graph consisting of connected RDF triple databases. An RDF triple, denoted as (subject, predicate, object), represents a relation (i.e. the predicate) existing between two resources (i.e. the subject and the object). Resources can belong to one or more classes, where a class aggregates resources sharing common characteristics. Thus, these RDF triple databases can be seen as interconnected knowledge bases. Most of the time, these knowledge bases are collaboratively built thanks to human users. This is particularly the case of DBpedia, a central knowledge base within the web of data, which encodes Wikipedia content in RDF format. DBpedia is built from two types of Wikipedia data: on the one hand, (semi-)structured data such as infoboxes, and, on the other hand, categories, which are thematic clusters of manually generated pages. However, the semantics of categories in DBpedia, that is, the reason a human agent has bundled resources, is rarely made explicit. In fact, considering a class, a software agent has access to the resources that are regrouped together, i.e. the class extension, but it generally does not have access to the ``reasons'' underlying such a cluster, i.e. it does not have the class intension. Considering a category as a class of resources, we aim at discovering an intensional description of the category. More precisely, given a class extension, we are searching for the related intension. The pair (extension, intension) which is produced provides the final definition and the implementation of classification-based reasoning for software agents. This can be expressed in terms of necessary and sufficient conditions: if x belongs to the class C, then x has the property P (necessary condition), and if x has the property P, then it belongs to the class C (sufficient condition). Two complementary data mining methods allow us to materialize the discovery of definitions, the search for association rules and the search for redescriptions. In this thesis, we first present a state of the art about association rules and redescriptions. Next, we propose an adaptation of each data mining method for the task of definition discovery. Then we detail a set of experiments applied to DBpedia, and we qualitatively and quantitatively compare the two approaches. Finally, we discuss how discovered definitions can be added to DBpedia to improve its quality in terms of consistency and completeness.Dans cette thèse, nous nous intéressons au web des données et aux ``connaissances'' que potentiellement il renferme. Le web des données se présente comme un très grand graphe constitué de bases de triplets RDF connectées entre elles. Un triplet RDF, dénoté (sujet, prédicat, objet), représente une relation (le prédicat) qui existe entre deux ressources (le sujet et l'objet). Les ressources peuvent appartenir à une ou plusieurs classes, où une classe regroupe des ressources partageant des caractéristiques communes. Ainsi, ces bases de triplets RDF peuvent être vues comme des bases de connaissances interconnectées. La plupart du temps ces bases de connaissances sont construites de manière collaborative par des utilisateurs. C'est notamment le cas de DBpedia, une base de connaissances centrale dans le web des données, qui encode le contenu de Wikipédia au format RDF. DBpedia est construite à partir de deux types de données de Wikipédia : d'une part, des données (semi-)structurées telles que les infoboxes et d'autre part les catégories, qui sont des regroupements thématiques de pages générés manuellement. Cependant, la sémantique des catégories dans DBpedia, c'est-à-dire la raison pour laquelle un agent humain a regroupé des ressources, n'est pas explicite. De fait, en considérant une classe, un agent logiciel a accès aux ressources qui y sont regroupées --- il dispose de la définition dite en extension --- mais il n'a généralement pas accès aux ``motifs'' de ce regroupement --- il ne dispose pas de la définition dite en intension. Dans cette thèse, nous cherchons à associer une définition à une catégorie en l'assimilant à une classe de ressources. Plus précisément, nous cherchons à associer une intension à une classe donnée en extension. La paire (extension, intension) produite va fournir la définition recherchée et va autoriser la mise en œuvre d'un raisonnement par classification pour un agent logiciel. Cela peut s'exprimer en termes de conditions nécessaires et suffisantes : si x appartient à la classe C, alors x a la propriété P (condition nécessaire), et si x a la propriété P, alors il appartient à la classe C (condition suffisante). Deux méthodes de fouille de données complémentaires nous permettent de matérialiser la découverte de définitions, la fouille de règles d'association et la fouille de redescriptions. Dans le mémoire, nous présentons d'abord un état de l'art sur les règles d'association et les redescriptions. Ensuite, nous proposons une adaptation de chacune des méthodes pour finaliser la tâche de découverte de définitions. Puis nous détaillons un ensemble d'expérimentations menées sur DBpedia, où nous comparons qualitativement et quantitativement les deux approches. Enfin les définitions découvertes peuvent potentiellement être ajoutées à DBpedia pour améliorer sa qualité en termes de cohérence et de complétud

Interrogation d'un réseau sémantique de documents : l'intertextualité dans l'accès à l'information juridique

A collection of documents is generally represented as a set of documents but this simple representation does not take into account cross references between documents, which often defines their context of interpretation. This standard document model is less adapted for specific professional uses in specialized domains in which documents are related by many various references and the access tools need to consider this complexity. We propose two models based on formal and relational concept analysis and on semantic web techniques. Applied on documentary objects, these two models represent and query in a unified way documents content descriptors and documents relations.Une collection documentaire est généralement représentée comme un ensemble de documents mais cette modélisation ne permet pas de rendre compte des relations intertextuelles et du contexte d'interprétation d'un document. Le modèle documentaire classique trouve ses limites dans les domaines spécialisés où les besoins d'accès à l'information correspondent à des usages spécifiques et où les documents sont liés par de nombreux types de relations. Ce travail de thèse propose deux modèles permettant de prendre en compte cette complexité des collections documentaire dans les outils d'accès à l'information. Le premier modèle est basée sur l'analyse formelle et relationnelle de concepts, le deuxième est basée sur les technologies du web sémantique. Appliquées sur des objets documentaires ces modèles permettent de représenter et d'interroger de manière unifiée les descripteurs de contenu des documents et les relations intertextuelles qu'ils entretiennent

Narrative as creative quest: the hero's journey and its alternatives

The ancient creative practice of narrative is recognized as an important meaning making activity in business, in the contexts of both academic study and commercial practice. Drawing on these developments, this chapter will focus on the creative potential, within business, for narrative models developed in the field of screenwriting. The latter is an area of creative writing that focuses particularly on story structure. It has produced a range of normative narrative models that recur as the underpinning templates of film and television narratives. The most well known of these is perhaps Christopher Vogler’s reworking of Joseph Campbell’s Hero’s Journey. Taking this text as my starting point, this chapter discusses the particular implications of the use of the Hero’s journey narrative model in a business context, examining the power of narrative retelling and the meaning making possibilities and limitations of different narrative models

Re-examining and re-conceptualising enterprise search and discovery capability: towards a model for the factors and generative mechanisms for search task outcomes.

Many organizations are trying to re-create the Google experience, to find and exploit their own corporate information. However, there is evidence that finding information in the workplace using search engine technology has remained difficult, with socio-technical elements largely neglected in the literature. Explication of the factors and generative mechanisms (ultimate causes) to effective search task outcomes (user satisfaction, search task performance and serendipitous encountering) may provide a first step in making improvements. A transdisciplinary (holistic) lens was applied to Enterprise Search and Discovery capability, combining critical realism and activity theory with complexity theories to one of the worlds largest corporations. Data collection included an in-situ exploratory search experiment with 26 participants, focus groups with 53 participants and interviews with 87 business professionals. Thousands of user feedback comments and search transactions were analysed. Transferability of findings was assessed through interviews with eight industry informants and ten organizations from a range of industries. A wide range of informational needs were identified for search filters, including a need to be intrigued. Search term word co-occurrence algorithms facilitated serendipity to a greater extent than existing methods deployed in the organization surveyed. No association was found between user satisfaction (or self assessed search expertise) with search task performance and overall performance was poor, although most participants had been satisfied with their performance. Eighteen factors were identified that influence search task outcomes ranging from user and task factors, informational and technological artefacts, through to a wide range of organizational norms. Modality Theory (Cybersearch culture, Simplicity and Loss Aversion bias) was developed to explain the study observations. This proposes that at all organizational levels there are tendencies for reductionist (unimodal) mind-sets towards search capability leading to fixes that fail. The factors and mechanisms were identified in other industry organizations suggesting some theory generalizability. This is the first socio-technical analysis of Enterprise Search and Discovery capability. The findings challenge existing orthodoxy, such as the criticality of search literacy (agency) which has been neglected in the practitioner literature in favour of structure. The resulting multifactorial causal model and strategic framework for improvement present opportunities to update existing academic models in the IR, LIS and IS literature, such as the DeLone and McLean model for information system success. There are encouraging signs that Modality Theory may enable a reconfiguration of organizational mind-sets that could transform search task outcomes and ultimately business performance

The great divide? Occupational limbo and permanent liminality amongst ‘teaching only’ staff in higher education

In this paper, we contribute new theoretical perspectives and empirical findings to the conceptualisation of occupational liminality, specifically in relation to so-called ‘teaching-only’ staff at UK universities. Here, we posit ‘occupational limbo’ as a state distinct from both transitional and permanent liminality; an important analytic distinction in better understanding occupational experiences. In its anthropological sense, liminality refers to a state of being betwixt and between; it is temporary and transitional. Permanent liminality refers to a state of being neither-this-nor-that, or both-this-and-that. We extend this framework in proposing a conceptualisation of occupational limbo as always-this-and-never-that. Based on interviews with 51 teaching-only staff at 20 research-intensive ‘Russell Group’ universities in the United Kingdom, findings revealed participants’ highly challenging occupational experiences. Interviewees reported feeling ‘locked-in’ to an uncomfortable state by a set of structural and social barriers often perceived as insurmountable. These staff felt negatively ‘marked’ (Allen-Collinson, 2009), subject to identity contestation as academics, and were found to engage in negative, often self-deprecatory identity talk that highlighted a felt inability to cross the līmen to the elevated status of ‘proper academics’ (Bamber et al., 2017). The findings and the new conceptual framework provide sociological insights with wider application to other occupational spheres