Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management – (Volume 1)

KDIR is part of IC3K, the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management.
Registration to KDIR allows free access to all other IC3K conferences. 

IC3K 2016 will be held in conjunction with IJCCI 2016
Registration to IC3K allows free access to the IJCCI conference (as a non-speaker).

Knowledge Discovery is an interdisciplinary area focusing upon methodologies for identifying valid, novel, potentially useful and meaningful patterns from data, often based on underlying large data sets. A major aspect of Knowledge Discovery is data mining, i.e. applying data analysis and discovery algorithms that produce a particular enumeration of patterns (or models) over the data. Knowledge Discovery also includes the evaluation of patterns and identification of which add to knowledge.
Information retrieval (IR) is concerned with gathering relevant information from unstructured and semantically fuzzy data in texts and other media, searching for information within documents and for metadata about documents, as well as searching relational databases and the Web. Automation of information retrieval enables the reduction of what has been called “information overload”.
Information retrieval can be combined with knowledge discovery to create software tools that empower users of decision support systems to better understand and use the knowledge underlying large data sets.

CONFERENCE CHAIR

Joaquim Filipe, Polytechnic Institute of Setúbal / INSTICC, Portugal

PROGRAM CHAIR

Ana Fred, Instituto de Telecomunicações and Instituto Superior Técnico – Lisbon University, Portugal

KEYNOTE SPEAKERS

Dieter FenselUniversity Innsbruck, Austria
Frans CoenenUniversity of Liverpool, United Kingdom
Una-May O’ReillyMIT Computer Science and Artificial Intelligence Laboratory, United States
Marijn JanssenDelft University of Technology, Netherlands

INVITED SPEAKER

Daniel ChirtesEASME – Executive Agency for SMEs – European Commission, Romania

Grammar and Dictionary based Named-entity Linking for Knowledge Extraction of Evidence-based Dietary Recommendations

In order to help people to follow the new knowledge about healthy diet that comes rapidly each day with the new published scientific reports, a grammar and dictionary based named-entity linking method is presented that can be used for knowledge extraction of evidence-based dietary recommendations. The method consists of two phases. The first one is a mix of entity detection and determination of a set of candidates for each entity, and the second one is a candidate selection. We evaluate our method using a corpus from dietary recommendations presented in one sentence provided by the World Health Organization and the U.S. National Library of Medicine. The corpus consists of 50 dietary recommendations and 10 sentences that are not related with dietary recommendations. For 47 out of 50 dietary recommendations the proposed method extract all the useful knowledge, and for remaining 3 only the information for one entity is missing. Due to the 10 sentences that are not dietary recommendation the method does not extract any entities, as expected.

A Conceptual Model of Actors and Interactions for the Knowledge Discovery Process

The knowledge discovery process is traditionally viewed as a sequence of operations to be applied to data; the human aspect of the process is seldom taken into account, and when it is, it is mainly the roles and actions of domain and technology experts that are considered. However, non-experts can also play an important role in knowledge discovery, and furthermore, the role of technology in the process may also be substantially expanded from what it traditionally has been, with special software facilitating interactions among human actors and even operating as an actor in its own right. This diversification of the knowledge discovery process is helpful in finding tenable solutions to the new problems presented by the current deluge of digital data, but only if the process model used to manage the process adequately represents the variety of forms that the process can take. The paper addresses this requirement by presenting a conceptual model that can be used to describe different type s of knowledge discovery processes in terms of the actors involved and the interactions they have with one another. Additionally, the paper discusses how the interactions can be facilitated to provide effective support for each different type of process. As a future perspective, the paper considers the implications of intelligent software taking on responsibilities traditionally reserved for human actors.