Agent-oriented constructivist knowledge management

In Ancient Times, when written language was introduced, books and manuscripts were often considered sacred. During these times, only a few persons were able to read and interpret them, while most people were limited in accepting these interpretations. Then, along with the industrial revolution of the XVIII and XIX centuries and especially boosted by the development of the press, knowledge slowly became available to all people. Simultaneously, people were starting to apply machines in the development of their work, usually characterized by repetitive processes, and especially focused in the production of consuming goods, such as furniture, clocks, clothes and so on. Following the needs of this new society, it was finally through science that new processes emerged to enable the transmission of knowledge from books and instructors to learners. Still today, people gain knowledge based on these processes, created to fulfill the needs of a society in its early stages of industrialization, thus not being compatible with the needs of the information society. In the information society, people must deal with an overloading amount of information, by the means of the media, books, besides different telecommunication and information systems technology. Furthermore, people’s relation to work has been influenced by profound changes, for instance, knowledge itself is now regarded as a valuable work product and, thus, the workplace has become an environment of knowledge creation and learning. Modifications in the world economical, political and social scenarios led to the conclusion that knowledge is the differential that can lead to innovation and, consequently, save organizations, societies, and even countries from failing in achieving their main goals. Focusing on these matters is the Knowledge Management (KM) research area, which deals with the creation, integration and use of knowledge, aiming at improving the performance of individuals and organizations. Advances in this field are mainly motivated by the assumption that organizations should focus on knowledge assets (generally maintained by the members of an organization) to remain competitive in the information society’s market. This thesis argues that KM initiatives should be targeted based on a constructivist perspective. In general, a constructivist view on KM focuses on how knowledge emerges, giving great importance to the knowledge holders and their natural practices. With the paragraph above, the reader may already have an intuition of how this work faces and targets Knowledge Management, however, let us be more precise. Research in Knowledge Management has evolved substantially in the past 30 years, coming from a centralized view of KM processes to a distributed view, grounded in organizational and cognitive sciences studies that point out the social, distributed, and subjective nature of knowledge. The first Knowledge Management Systems (KMSs) were centrally based and followed a top-down design approach. The organization managers, supported by knowledge engineers, collected and structured the contents of an organizational memory as a finished product at design time (before the organizational memory was deployed) and then disseminated the product, expecting employees to use it and update it. However, employees often claimed that the knowledge stored in the repository was detached from their real working practices. This led to the development of evolutionary methods, which prescribe that the basic KM system is initially developed and evolves proactively in an on-going fashion. However, most of the initiatives are still based on building central repositories and portals, which assume standardized vocabularies, languages, and classification schemes. Consequently, employees’ lack of trust and motivation often lead to dissatisfaction. In other words, workers resist on sharing knowledge, since they do not know who is going to access it and what is going to be done with it. Moreover, the importance attributed to knowledge may give an impression that these central systems take away a valuable asset from his or her owner, without giving appreciable benefits in return. The problems highlighted in the previous paragraph may be attenuated or even solved if a top-down/bottom-up strategy is applied when proposing a KM solution. This means that the solution should be sought with aim at organizational goals (top-down) but at the same time, more attention should be given to the knowledge holders and on the natural processes they already use to share knowledge (bottom-up). Being active agency such an important principle of Constructivism, this work recognizes that the Agent Paradigm (first defined by Artificial Intelligence and more recently adopted by Software Engineering) is the best approach to target Knowledge Management, taking a technological and social perspective. Capable of modeling and supporting social environments, agents is here recognized as a suitable solution for Knowledge Management especially by providing a suitable metaphor used for modeling KM domains (i.e. representing humans and organizations) and systems. Applying agents as metaphors on KM is mainly motivated by the definition of agents as cognitive beings having characteristics that resemble human cognition, such as autonomy, reactivity, goals, beliefs, desires, and social-ability. Using agents as human abstractions is motivated by the fact that, for specific problems, such as software engineering and knowledge management process modeling, agents may aid the analyst to abstract away from some of the problems related to human complexity, and focus on the important issues that impact the specific goals, beliefs and tasks of agents of the domain. This often leads to a clear understanding of the current situation, which is essential for the proposal of an appropriate solution. The current situation may be understood by modeling at the same time the overall goals of the organization, and the needs and wants of knowledge holders. Towards facilitating the analysis of KM scenarios and the development of adequate solutions, this work proposes ARKnowD (Agent-oriented Recipe for Knowledge Management Systems Development). Systems here have a broad definition, comprehending both technology-based systems (e.g. information system, groupware, repositories) and/or human systems, i.e. human processes supporting KM using non-computational artifacts (e.g. brain stormings, creativity workshops). The basic philosophical assumptions behind ARKnowD are: a) the interactions between human and system should be understood according to the constructivist principle of self-construction, claiming that humans and communities are self-organizing entities that constantly construct their identities and evolve throughout endless interaction cycles. As a result of such interactions, humans shape systems and, at the same time, systems constrain the ways humans act and change; b) KM enabling systems should be built in a bottom-up approach, aiming at the organizational goals, but understanding that in order to fulfill these goals, some personal needs and wants of the knowledge holders (i.e. the organizational members) need to be targeted; and c) there is no “silver bullet��? when pursuing a KM tailoring methodology and the best approach is combining existing agent-oriented approaches according to the given domain or situation. This work shows how the principles above may be achieved by the integration of two existing work on agent-oriented software engineering, which are combined to guide KM analysts and system developers when conceiving KM solutions. Innovation in our work is achieved by supporting topdown/bottom-up approaches to KM as mentioned above. The proposed methodology does that by strongly emphasizing the earlier phases of software development, the so-called requirement analysis activity. In this way, we consider all stakeholders (organizations and humans) as agents in our analysis model, and start by understanding their relations before actually thinking of developing a system. Perhaps the problem may be more effectively solved by proposing changes in the business processes, rather than by making use of new technology. And besides, in addition to humans and organizations, existing systems are also included in the model from start, helping the analyst and designer to understand which functionalities are delegated to these so-called artificial agents. In addition to that, benefits as a result of the application of ARKnowD may be also attributed to our choice of using the proper agent cognitive characteristics in the different phases of the development cycle. With the main purpose of exemplifying the use of the proposed methodology, this work presents a socially-aware recommender agent named KARe (Knowledgeable Agent for Recommendations). Recommender Systems may be defined by those that support users in selecting items of their need from a big set of items, helping users to overcome the overwhelming feeling when facing a vast information source, such as the web, an organizational repository or the like. Besides serving as a case for our methodology, this work also aims at exploring the suitability of the KARe system to support KM processes. Our choice for supporting knowledge sharing through questioning and answering processes is again supported by Constructivism proponents, who understand that social interaction is vital for active knowledge building. This assumption is also defended by some KM theories, claiming that knowledge is created through cycles of transformation between two types of knowledge: tacit and explicit knowledge. Up to now, research on KM has paid much attention to the formalization and exchange of explicit knowledge, in the form of documents or other physical artifacts, often annotated with metadata, and classified by taxonomies or ontologies. Investigations surrounding tacit knowledge have been so far scarce, perhaps by the complexity of the tasks of capturing and integrating such kind of knowledge, defined as knowledge about personal experience and values, usually confined on people’s mind. Taking a flexible approach on supporting this kind of knowledge conversion, KARe relies on the potential of social interaction underlying organizational practices to support knowledge creation and sharing. The global objective of this work is to support knowledge creation and sharing within an organization, according to its own natural processes and social behaviors. In other words, this work is based on the assumption that KM is better supported if knowledge is looked at from a constructivist perspective. To sum up, this thesis aims at: 1) Providing an agent-oriented approach to guide the creation and evolvement of KM initiatives, by analyzing the organizational potentials, behaviors and processes concerning knowledge sharing; 2) Developing the KARe recommender system, based on a semantically enriched Information Retrieval technique for recommending knowledge artifacts, supporting users to ask and answer to each others’ questions. These objectives are achieved as follows: - Defining the principles that characterize a Constructivist KM supporting environment and understanding how they may be used to support the creation of more effective KM solutions; - Providing an agent-oriented approach to develop KM systems. This approach is based on the integration of two different agent-oriented software engineering works, profiting from their strengths in providing a comprehensive methodology that targets both analysis and design activities; - Proposing and designing a socially aware agent-oriented recommender system both to exemplify the application of the proposed approach and to explore its potential on supporting knowledge creation and sharing. - Implementing an Information Retrieval algorithm to support the previously mentioned system in generating recommendations. Besides describing the algorithm, this thesis brings experimental results to prove its effectiveness

Web application for reliability analysis within civil aviation domain

Analýzy spolehlivosti jsou klíčovými složkami při hodnocení posouzení rizik během fáze návrhu v leteckém průmyslu. Analýza stromu poruch (FTA) a analýza poruchových režimů a efektů (FMEA) se běžně kombinují při analýze systému a vyhodnocování možných poruch. Kombinování metodik vyžaduje sjednocení struktury dat tak, aby byla použitelná pro všechny analytické metody zároveň. Existující aplikace poskytují nástroje samostatně, což vede k nekonzistenci dat, duplikátům a překlepům při migraci napříč aplikacemi. Tato práce si klade za cíl vytvořit rozšiřitelné řešení, které by poskytlo nástroje k provedení jedné z technik FTA a FMEA a přitom se spoléhalo na ontologický model použitelný pro obě techniky zároveň. Diplomová práce analyzuje existující řešení a ontologie a na základě těchto vstupů navrhuje nezbytné požadavky, které jsou ve spolupráci se zúčastněnými doménovými odborníky prioritizovány. Výsledné řešení implementuje aplikaci zaměřenou primárně na FTA, která nabízí definování partonomie systému, konstrukci FTA a automatický převod stromů do FMEA vzhledem k jednotnému ontologickému modelu. Aplikace je na závěr otestována doménovými odborníky na základě skutečných leteckých dat.Reliability analyses are key components in a risk assessment evaluation during the design phase in an aviation industry. Fault Tree Analysis (FTA) and Failure Modes and Effects Analysis (FMEA) are commonly combined together to review the system and to evaluate possible failures. The combination of methodologies requires a unified data usable for all the analyses. Existing applications provide the tools separately which introduces inconsistencies, duplicates and typos when the data are migrated across the applications. This thesis thus aims to create an extensible solution that would provide tools to perform one of FTA and FMEA techniques and yet rely on an ontological model usable for both. The thesis analyses existing solutions and ontologies and given these inputs proposes necessary requirements that are prioritized in cooperation with involved domain experts. The resulting solution implements an application focusing primarily on FTA which offers possibilities for system partonomy definition, FTA construction and an automatic conversion of the trees to FMEA tables given the unified ontological model. The application is finally reviewed by the domain experts on real aviation data

A model-driven approach to the conceptual modeling of situations : from specification to validation

A modelagem de situações para aplicações sensíveis ao contexto, também chamadas de aplicações sensíveis a situações, é, por um lado, uma tarefa chave para o funcionamento adequado dessas aplicações. Por outro lado, essa também é uma tafera árdua graças à complexidade e à vasta gama de tipos de situações possíveis. Com o intuito de facilitar a representação desses tipos de situações em tempo de projeto, foi criada a Linguagem de Modelagem de Situações (Situation Modeling Language - SML), a qual se baseia parcialmente em ricas teorias ontológicas de modelagem conceitual, além de fornecer uma plataforma de detecção de situação em tempo de execução. Apesar do benefício da existência dessa infraestrutura, a tarefa de definir tipos de situação é ainda não-trivial, podendo carregar problemas que dificilmente são detectados por modeladores via inspeções manuais. Esta dissertação tem o propósito de melhorar e facilitar ainda mais a definição de tipos de situação em SML propondo: (i) uma maior integração da linguagem com as teorias ontológicas de modelagem conceitual pelo uso da linguagem OntoUML, visando aumentar a expressividade dos modelos de situação; e (ii) uma abordagem para validação de tipos de situação usando um método formal, visando garantir que os modelos criados correspondam à intenção do modelador. Tanto a integração quanto a validação são implementadas em uma ferramenta para especificação, verificação e validação de tipos de situação ontologicamente enriquecidos.The modeling of situation types for context-aware applications, also called situationaware applications, is, on the one hand, a key task to the proper functioning of those applications. On the other hand, it is also a hard task given the complexity and the wide range of possible situation types. Aiming at facilitating the representation of those types of situations at design-time, the Situation Modeling Language (SML) was created. This language is based partially on rich ontological theories of conceptual modeling and is accompanied by a platform for situation-detection at runtime. Despite the benefits of the availability of this suitable infrastructure, the definition of situation types, being a non-trivial task, can still pose problems that are hardly detected by modelers by manual model inspection. This thesis aims at improving and facilitating the definition of situation types in SML by proposing: (i) the integration between the language and the ontological theories of conceptual modeling by using the OntoUML language, with the purpose of increasing the expressivity of situation type models; and (ii) an approach for the validation of situation type models using a lightweight formal method, aiming at increasing the correspondence between the created models’ instances and the modeler’s intentions. Both the integration and the validation are implemented in a tool for specification, verification and validation of ontologically-enriched situation types.CAPE

On the symbiosis between conceptual modeling and ontology engineering : recommendation-based conceptual modeling

Within an enterprise, different conceptual models, such as process, data, and goal models, are created by various stakeholders. These models are fundamentally based on similar underlying enterprise (domain) concepts, but they have a different focus, are represented using different modeling languages, take different viewpoints, utilize different terminology, and are used to develop different enterprise artefacts (such as documents, software, databases, etc.); therefore, they typically lack consistency and alignment. Another issue is that modelers have different vocabulary selections and different modeling styles. As a result, the enterprise can find itself accumulating a pile of models which cover similar aspects in different manners. Those models are not machine-readable and cannot be processed automatically. Enterprise-Specific Ontologies (ESOs) aim to solve this problem by serving as a reference during the conceptual model creation. Using such a shared semantic repository makes conceptual models semantically aligned and facilitates model integration. However, managing those ontologies is complicated; an enterprise is an evolving entity, and as it changes, the ESO might become outdated. During the years of research dedicated to this dissertation, the Recommendation-Based Conceptual Modeling and Ontology Evolution (CMOE+) framework was developed. This framework establishes a symbiotic relationship between the Ontology engineering and the Conceptual modeling fields. CMOE+ consists of two cycles: the Ontology Evolution cycle and the Conceptual Modeling cycle. The Ontology Evolution cycle is responsible for setting up the initial version of the ESO and updating it as the knowledge within the enterprise evolves. Additionally, this cycle encapsulates recommendation services to perform ontology look-up and to present the most relevant ESO concepts in support of the modeler. The Conceptual Modeling cycle is responsible for the creation of conceptual models in different modeling languages based on the ESO. This cycle is also concerned with the quality evaluation of the created models. CMOE+ was developed based on requirements identified as a result of a literature review and a case study. The development process follows the Design Science Research Methodology (DSRM). After the initial version of CMOE+ was put forward, our focus was narrowed towards the recommendation-based conceptual modeling part of CMOE+, and we continued to gradually improve the framework in iterations until it reached its current state. The Ontology Evolution Cycle is not fully addressed within the scope of this dissertation. In order to demonstrate the performance and usability of CMOE+, it was exemplified for process modeling using BPMN and goal modeling using i*. This thesis presents a detailed instantiation, and explains steps to be performed in order to instantiate CMOE+ for other modeling languages. In order to evaluate the process modeling instance of CMOE+, a CMOE+BPMN tool was implemented. This tool incorporates a BPMN modeler, facilitates storage and access of the ESO, and includes all algorithms functioning within CMOE+ for the BPMN modeling language (as some of the algorithms are language dependent). Next, CMOE+ was exemplified using the i* goal modeling language. Finally, we demonstrated the ability of CMOE+ to perform alignment between i* and BPMN models, in order to show that CMOE+ is indeed beneficial in achieving interoperability among models created in different modeling languages and covering distinct aspects of the enterprise

Investigating business process elements: a journey from the field of Business Process Management to ontological analysis, and back

Business process modelling languages (BPMLs) typically enable the representation of business processes via the creation of process models, which are constructed using the elements and graphical symbols of the BPML itself. Despite the wide literature on business process modelling languages, on the comparison between graphical components of different languages, on the development and enrichment of new and existing notations, and the numerous definitions of what a business process is, the BPM community still lacks a robust (ontological) characterisation of the elements involved in business process models and, even more importantly, of the very notion of business process. While some efforts have been done towards this direction, the majority of works in this area focuses on the analysis of the behavioural (control flow) aspects of process models only, thus neglecting other central modelling elements, such as those denoting process participants (e.g., data objects, actors), relationships among activities, goals, values, and so on. The overall purpose of this PhD thesis is to provide a systematic study of the elements that constitute a business process, based on ontological analysis, and to apply these results back to the Business Process Management field. The major contributions that were achieved in pursuing our overall purpose are: (i) a first comprehensive and systematic investigation of what constitutes a business process meta-model in literature, and a definition of what we call a literature-based business process meta-model starting from the different business process meta-models proposed in the literature; (ii) the ontological analysis of four business process elements (event, participant, relationship among activities, and goal), which were identified as missing or problematic in the literature and in the literature-based meta-model; (iii) the revision of the literature-based business process meta-model that incorporates the analysis of the four investigated business process elements - event, participant, relationship among activities and goal; and (iv) the definition and evaluation of a notation that enriches the relationships between activities by including the notions of occurrence dependences and rationales