Emmanuel Nauer

Bio: Emmanuel Nauer is an academic researcher from University of Lorraine. The author has contributed to research in topics: Case-based reasoning & Knowledge extraction. The author has an hindex of 10, co-authored 40 publications receiving 328 citations. Previous affiliations of Emmanuel Nauer include French Institute for Research in Computer Science and Automation.

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

Abstract: 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

Taaable: A Case-Based System for Personalized Cooking

Abstract: Taaable is a Case-Based Reasoning (CBR) system that uses a recipe book as a case base to answer cooking queries. Taaable participates in the Computer Cooking Contest since 2008. Its success is due, in particular, to a smart combination of various methods and techniques from knowledge-based systems: CBR, knowledge representation, knowledge acquisition and discovery, knowledge management, and natural language processing. In this chapter, we describe Taaable and its modules. We first present the CBR engine and features such as the retrieval process based on minimal generalization of a query and the different adaptation processes available. Next, we focus on the knowledge containers used by the system. We report on our experiences in building and managing these containers. The Taaable system has been operational for several years and is constantly evolving. To conclude, we discuss the future developments: the lessons that we learned and the possible extensions.

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

Abstract: 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.

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

Abstract: 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.

Mise en Place: Unsupervised Interpretation of Instructional Recipes

Abstract: We present an unsupervised hard EM approach to automatically mapping instructional recipes to action graphs, which define what actions should be performed on which objects and in what order. Recovering such structures can be challenging, due to unique properties of procedural language where, for example, verbal arguments are commonly elided when they can be inferred from context and disambiguation often requires world knowledge. Our probabilistic model incorporates aspects of procedural semantics and world knowledge, such as likely locations and selectional preferences for different actions. Experiments with cooking recipes demonstrate the ability to recover high quality action graphs, outperforming a strong sequential baseline by 8 points in F1, while also discovering general-purpose knowledge about cooking.

Semantic Measures for the Comparison of Units of Language, Concepts or Entities from Text and Knowledge Base Analysis

Abstract: Semantic measures are widely used today to estimate the strength of the semantic relationship between elements of various types: units of language (e.g., words, sentences, documents), concepts or even instances semantically characterized (e.g., diseases, genes, geographical locations). Semantic measures play an important role to compare such elements according to semantic proxies: texts and knowledge representations, which support their meaning or describe their nature. Semantic measures are therefore essential for designing intelligent agents which will for example take advantage of semantic analysis to mimic human ability to compare abstract or concrete objects. This paper proposes a comprehensive survey of the broad notion of semantic measure for the comparison of units of language, concepts or instances based on semantic proxy analyses. Semantic measures generalize the well-known notions of semantic similarity, semantic relatedness and semantic distance, which have been extensively studied by various communities over the last decades (e.g., Cognitive Sciences, Linguistics, and Artificial Intelligence to mention a few).

Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)

Abstract: Recently, a groundswell of research has identified the use of counterfactual explanations as a potentially significant solution to the Explainable AI (XAI) problem. It is argued that (a) technically, these counterfactual cases can be generated by permuting problem-features until a class change is found, (b) psychologically, they are much more causally informative than factual explanations, (c) legally, they are GDPR-compliant. However, there are issues around the finding of good counterfactuals using current techniques (e.g. sparsity and plausibility). We show that many commonly-used datasets appear to have few good counterfactuals for explanation purposes. So, we propose a new case based approach for generating counterfactuals using novel ideas about the counterfactual potential and explanatory coverage of a case-base. The new technique reuses patterns of good counterfactuals, present in a case-base, to generate analogous counterfactuals that can explain new problems and their solutions. Several experiments show how this technique can improve the counterfactual potential and explanatory coverage of case-bases that were previously found wanting.

Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields

Abstract: This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuSSIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.