Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at IJCAI 2013)
This is the second edition of the FCA4AI workshop, the first edition being associated to the ECAI 2012 Conference, held in Montpellier, in August 2012 (see http://www.fca4ai.hse.ru/). In particular, the first edition of the workshop showed that there are many AI researchers interested in FCA. Based on that, the three co-editors decided to organize a second edition of the FCA4AI workshop at the IJCAI 2013 Conference in Beijing.
Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification. FCA allows one to build a concept lattice and a system of dependencies (implications) which can be used for many AI needs, e.g. knowledge processing involving learning, knowledge discovery, knowledge representation and reasoning, ontology engineering, as well as information retrieval and text processing. Thus, there exist many “natural links” between FCA and AI.
Recent years have been witnessing increased scientific activity around FCA, in particular a strand of work emerged that is aimed at extending the possibilities of FCA w.r.t. knowledge processing, such as work on pattern structures and relational context analysis. These extensions are aimed at allowing FCA to deal with more complex than just binary data, both from the data analysis and knowledge discovery points of view and from the knowledge representation point of view, including, e.g., ontology engineering. All these works extend the capabilities of FCA and other new possibilities for AI activities in the framework of FCA. Accordingly, in this workshop, we are interested in two main issues:
- How can FCA support AI activities such as knowledge processing (knowledge discovery, knowledge representation and reasoning), learning (clustering, pattern and data mining), natural language processing, and information retrieval.
- How can FCA be extended in order to help AI researchers to solve new and complex problems in their domains.
The workshop is dedicated to discuss such issues.
The papers submitted to the workshop were carefully peer-reviewed by two members of the program committee and 11 papers with the highest scores were selected. We thank all the PC members for their reviews and all the authors for their contributions. We also thank the organizing committee of ECAI-2012 and especially workshop chairs Jerome Lang and Michele Sebag for the support of the workshop.
In this paper, we are interested in the analysis of sequential data and we propose an original framework based on Formal Concept Analysis (FCA). For that, we introduce sequential pattern structures, an original specification of pattern structures for dealing with sequential data. Pattern structures are used in FCA for dealing with complex data such as intervals or graphs. Here they are adapted to sequences. For that, we introduce a subsumption operation for sequence comparison, based on subsequence matching. Then, a projection, i.e. a kind of data reduction of sequential pattern structures, is suggested in order to increase the efficiency of the approach. Finally, we discuss an application to a dataset including patient trajectories (the motivation of this work), which is a sequential dataset and can be processed with the introduced framework. This research work provides a new and efficient extension of FCA to deal with complex (not binary) data, which can be an alternative to the analysis of sequential datasets.