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This book features a selection of papers presented at the Third IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2015, held in Buenos Aires, Argentina, in July 2015, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2015.
The 9 revised and extended papers were carefully reviewed and selected from 15 submissions. They present new research and innovative aspects in the field of knowledge management such as knowledge models, KM and Web, knowledge capturing and learning, and KM and AI intersections.



Knowledge-Grid Modelling for Academic Purposes

Nowadays, we face a huge amount of data and information sharing on the Web by different users worldwide. A multidimensional perspective in describing a university ontology seems to be very important for the modelling of higher education resources. This paper proposes a multi-dimensional knowledge model, designed to distribute and manage knowledge resources efficiently. We propose our model as the foundation of an advanced knowledge platform including the following dimensions: time, area and social. Three crucial domains should be considered in this model: educational, research and managerial. The ontology including the mentioned knowledge management aspects is prepared using Ontorion Fluent Editor.
Mieczysław Owoc, Krzysztof Hauke, Paweł Weichbroth

A Knowledge Engineering Perspective of Knowledge Management: How to Manage Project Meeting Knowledge

Knowledge management is a research topic that encompasses various domains. It is proved that both knowledge codification and social interaction can improve knowledge sharing in an organization, and IT has always been a powerful technology to support knowledge management cycle, especially on knowledge capturing, storage or even sharing. Knowledge engineering is an engineering science that represent knowledge into computable forms, in order to solve a problem in a specific domain, it offers useful approaches to obtain and model domain knowledge. In this paper, the knowledge produced in project meetings will be studied, and a semantic network based classification model will be proposed to support knowledge management, followed by two detailed case studies.
Xinghang Dai, Nada Matta

Web of Data Evolution by Exploiting Agent Based-Argumentation

Sharing knowledge and data coming from different sources is one of the biggest advantage of linked data. Keeping this knowledge graph up to date may take in account both ontology vocabularies and data since they should be consistent. Our general problem is to deal with web of data evolution in particular: We aim at assisting user in a such complex process. In this research work, we propose an agent based-argumentation framework to help user linked data changes. We assign for each agent a goal to acheive as process step. We rely upon communication-argumentation potential of the agent to harmonise changes with consistency.
Fatma Chamekh, Danielle Boulanger, Guilaine Talens

Content Management Systems Based on GNU GPL License as a Support of Knowledge Management in Organizations and Business

Content Management Systems are an increasingly popular form of publishing content on the Internet. Its popularity gained by a large intuitive, rich diversity of graphical representations and great functionality. The author focused on a characterization of free systems based on GNU GPL license which have high prospects for growth and development. The main goal of this article is to find common ground between knowledge management and content management systems. Author believes that area of content management is not sufficiently exploited and would greatly assist the process of knowledge management in organizations. The article includes possible scenarios and development steps of Content Management Systems implementation in area of Knowledge Management. There were also defined the different points of view of knowledge management depending on the assumed perspective. This chapter is divided into 4 areas, which were finally assembled: content management, content management systems, GNU GPL license, knowledge management and implementation of content management systems in area of knowledge management.
Łukasz Przysucha

Context Aware Knowledge Zoning: Traceability and Business Emails

Even if immaterial capital represents an increasingly important part of the value of our enterprises, it’s not always possible to store, trace or capture knowledge and expertise, for instance in middle sized projects. Email it still widely used in professional projects especially among geographically distributed teams. In this paper we present a novel approach to detect zones inside business emails where elements of knowledge are likely to be found. We define an enhanced context taking into account not only the email content and metadata but also the competencies of the users and their roles. Also linguistic pragmatic analysis is added to usual NLP technics. After describing our model and method, we apply it to a real life corpus and evaluate the results based on machine learning experiments and filtering algorithm.
François Rauscher, Nada Matta, Hassan Atifi

From Knowledge to Sign Management: A Co-design Methodology for Biodiversity and Music Enhancement

When using Artificial Intelligence techniques for Knowledge management in decision support systems, the enhancement of knowledge should be based both on artificial machine learning methods and a natural human learning approach. Indeed, knowledge representation with ontologies and Case-Based Reasoning (CBR) is not enough for gaining qualitative results in decision support systems. We need to manage know-how, i.e. living knowledge. For example, enhancing biodiversity and music means teaching and learning the effectiveness of individual and living interpretations (how to observe a natural specimen, how to play a music sheet). The quality of descriptions is thus very important to correctly classify or identify marine or terrestrial organisms, or learn adequately an instrument such as the guitar or the piano. This paper introduces Sign management to tackle this qualitative learning problem in AI. Then, a Co-design methodology and a cooking method on a Creativity Platform are proposed: when dealing with such complex domains, we need to focus on the signification of knowledge construction that operates in co-designing an e-service that should be useful for reaching a more robust knowledge base. Our finding is that due to different interpretations of domain objects from subjects (persons), we need sign bases to move from written expert knowledge transmission to multimedia know-how sharing in the community for getting better results.
Noël Conruyt, Véronique Sébastien, Olivier Sébastien, Didier Sébastien, David Grosser

Learning from Daily Knowledge: How to Keep Track and to Represent Design Projects Knowledge

From the beginning, human being is interesting in knowledge. A lot of questions are discussed: what is knowledge? How knowledge is built? How is it represented in mind? How can it be kept? How can it be learned? … The notion of Knowledge is defined from the antiquity. Platon, for instance, define the thought as the intellectual model of objects. Heraclite went towards the definition of the logos as a triangle in which thought is distinguished from expression and from reality. Saussure defined the basic principle of the semiotic as: a representation of knowledge embedded in an activity and related to a specific symbol. Currently these representations are more and more used to enhance learning from expertise and past experience. So, human does making sense by recognizing concepts from his references (memory). Based on this theory, knowledge engineering approaches provide techniques that help to represent expertise as references and enhance learning from these references. We study how to capture and represent knowledge produced in daily work. This type of knowledge belongs to episodic memory. Experiences must be repeated in order to represent epistemic and semantic knowledge (references) that can be applied to face new problems. We develop techniques to capture daily knowledge in order to develop semantic classifications and enhance learning in an organization.
Nada Matta, Guillaume Ducellier, Hassan Atifi

Building Time-Affordable Cultural Ontologies Using an Emic Approach

Recently, studies about culturally-aware systems have arisen to address digitized culture. Among these systems those enculturated driven by cultural knowledge embed culture in their design. To deal with the specifics of cultural groups, the development of machine-readable cultural knowledge representations can provide a substantial help. In this research we present a process to build time-affordable, emic, conceptually-sound and machine-readable cultural representations. These representations originate from Cognitive Anthropology. They follow a three steps methodology: ethnographic sampling, individuals’ personal knowledge elicitation and cultural consensus analysis. We use lexico-semantic relation extraction as a mean to automatically elicit knowledge structures. Their formalisation is achieved through Ontology Engineering.
We conducted experiments to build three cultural ontologies in order to assess the whole process. It came out that with the lexico-semantic relation extraction technique, the best representations we can obtain are consensually-limited, incomplete and contain some errors. However, many clues indicate that these problems should be solved by using higher quality elicitation techniques.
Jean Petit, Jean-Charles Boisson, Francis Rousseaux

Artificial Intelligence for Successful Kflow

Since over twenty years now organizations have been tried various approaches of Knowledge Management beginning mostly with not always adequate tools. The feedback from AI experience shows that it helps elaborate a knowledge flow from building blocks of applications. These blocks providing various KM components should communicate to serve given organization and its stakeholders ensuring sustainable success of all participants. This paper discusses some selected experiences and performance of the flow using two main KM approaches. The influence of the knowledge flow on the capacity to innovate is also discussed. Finally, some perspectives on the impact of AI for improving Knowledge Management in organizations are given.
Eunika Mercier-Laurent

Erratum to: Knowledge-Grid Modelling for Academic Purposes

Without Abstract
Mieczysław Owoc, Krzysztof Hauke, Paweł Weichbroth


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