Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

This is the second part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003 and 2011 mentioning terms related to Formal Concept Analysis in the title, abstract and keywords. We developed a knowledge browsing environment to support our literature analysis process. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. In this second part, we zoom in on and give an extensive overview of the papers published between 2003 and 2011 which applied FCA-based methods for knowledge discovery and ontology engineering in various application domains. These domains include software mining, web analytics, medicine, biology and chemistry data.

https://publications.hse.ru/mirror/pubs/share/folder/utyhyy55j6/direct/114480266.pdf

Review: Formal concept analysis in knowledge processing: A survey on applications

The popularity of the Web and the large number of documents available in electronic form has motivated the search for hidden knowledge in text collections. Consequently, there is growing research interest in the general topic of text mining. In this dissertation, we develop a text-mining system by integrating methods from Information Extraction (IE) and Data Mining (Knowledge Discovery from Databases or KDD). By utilizing existing IE and KDD techniques, text-mining systems can be developed relatively rapidly and evaluated on existing text corpora for testing IE systems. 
We present a general text-mining framework called DISCOTEX which employs an IE module for transforming natural-language documents into structured data and a KDD module for discovering prediction rules from the extracted data. When discovering patterns in extracted text, strict matching of strings is inadequate because textual database entries generally exhibit variations due to typographical errors, misspellings, abbreviations, and other sources. We introduce the notion of discovering “soft-matching” rules from text and present two new learning algorithms. TEXTRISE is an inductive method for learning soft-matching prediction rules that integrates rule-based and instance-based learning methods. Simple, interpretable rules are discovered using rule induction, while a nearest-neighbor algorithm provides soft matching. SOFTAPRIORI is a text-mining algorithm for discovering association rules from texts that uses a similarity measure to allow flexible matching to variable database items. We present experimental results on inducing prediction and association rules from natural-language texts demonstrating that TEXTRISE and SOFTA PRIORI learn more accurate rules than previous methods for these tasks. We also present an approach to using rules mined from extracted data to improve the accuracy of information extraction. Experimental results demonstrate that such discovered patterns can be used to effectively improve the underlying IE method.

/pdf/text-mining-with-information-extraction-7g2sbtyl7d.pdf

Text mining with information extraction

Building a domain ontology usually requires several resources of different types, e.g. thesaurus, object taxonomies, terminologies, databases, sets of documents, etc, where objects are described in terms of attributes and relations with other objects. One important and hard problem is to be able to combine and merge knowledge units extracted from these different resources within an homogeneous formal representation (such as a description logic or OWL). The purpose of this article is to show which kinds of resources should be available for designing a real-world ontology in a given application domain, and then how Formal Concept Analysis and its extension - Relational Concept Analysis- can be used for materializing an associated ontology. This resulting target ontology can then be encoded within OWL or a description logic formalism, allowing classification-based reasoning. A real-world example in microbiology is detailed. Finally, an evaluation including tests on recall and precision shows how source resources can be completed with other existing domain resources using a semi-automatic analysis process.

/pdf/formal-concept-analysis-a-unified-framework-for-building-and-4br1drek2b.pdf

Formal Concept Analysis: A Unified Framework for Building and Refining Ontologies

This paper presents how the Taaable project addresses the textual case-based reasoning challenge of the CCC, thanks to a combination of principles, methods, and technologies of various fields of knowledge-based system technologies, namely CBR, ontology engineering manual and semi-automatic), data and text-mining using textual resources of the Web, text annotation (used as an indexing technique), knowledge representation, and hierarchical classification Indeed, to be able to reason on textual cases, indexing them by a formal representation language using a formal vocabulary has proven to be useful

https://hal.inria.fr/inria-00337666/document

TAAABLE: Text Mining, Ontology Engineering, and Hierarchical Classification for Textual Case-Based Cooking

This paper addresses the issue of adapting cases represented by plain text with the help of formal concept analysis and natural language processing technologies. The actual cases represent recipes in which we classify ingredients according to culinary techniques applied to them. The complex nature of linguistic anaphoras in recipe texts make usual text mining techniques inefficient so a stronger approach, using syntactic and dynamic semantic analysis to build a formal representation of a recipe, had to be used. This representation is useful for various applications but, in this paper, we show how one can extract ingredient–action relations from it in order to use formal concept analysis and select an appropriate replacement sequence of culinary actions to use in adapting the recipe text.

/pdf/text-adaptation-using-formal-concept-analysis-2lgc9d69zz.pdf

Text adaptation using formal concept analysis

Semantic wikis enable a community of users to produce formalized knowledge readable and usable by machines. To take one step further, one can use a semantic wiki as a blackboard allowing humans and machines to interact in order to build knowledge that is useful for both humans and machines. In this paper, we present a case study of the use of a semantic wiki (Semantic Media Wiki) as a blackboard to manage culinary data and knowledge. This case study is performed within the context of the TAAABLE application, a case-based reasoning web system aiming at solving cooking problems on the basis of existing recipes. With WIKITAAABLE, an evolution of TAAABLE based on a semantic wiki, we show how a semantic wiki assists users in their knowledge management tasks by taking into account user feedback. The issues related to the integration of several knowledge management mechanisms in a single application are discussed at the end of the paper.

/pdf/wikitaaable-a-semantic-wiki-as-a-blackboard-for-a-textual-282l40n5iz.pdf

WIKITAAABLE: A semantic wiki as a blackboard for a textual case-base reasoning system.

The textual case-based cooking systemWIKITAAABLE participates to the second Computer cooking contest (CCC). It is an extension of the TAAABLE system that has participated to the first CCC. WIKITAAABLE's architecture is composed of a semantic wiki used for the collaborative acquisition of knowledge (recipe, ontology, adaptation knowledge) and of a case-based inference engine using this knowledge for retrieving and adapting recipes. This architecture allows various modes of knowledge acquisition for case-based reasoning that are studied within the TAAABLE project. In particular, opportunistic adaptation knowledge discovery is an approach for interactive and semi-automatic learning of adaptation knowledge triggered by a feedback from the user.

https://hal.inria.fr/inria-00411508/document

Yannick Toussaint

Papers

Formal Concept Analysis: A Unified Framework for Building and Refining Ontologies

TAAABLE: Text Mining, Ontology Engineering, and Hierarchical Classification for Textual Case-Based Cooking

Text adaptation using formal concept analysis

WIKITAAABLE: A semantic wiki as a blackboard for a textual case-base reasoning system.

Knowledge Acquisition and Discovery for the Textual Case-Based Cooking system WIKITAAABLE