Mathieu Lafourcade

Metamodel Matching for Automatic Model Transformation Generation

Applying Model-Driven Engineering (MDE) leads to the creation of a large number of metamodels, since MDE recommends an intensive use of models defined by metamodels. Metamodels with similar objectives are then inescapably created. A recurrent issue is thus to turn compatible models conforming to similar metamodels, for example to use them in the same tool. The issue is classically solved developing ad hoc model transformations. In this paper, we propose an approach that automatically detects mappings between two metamodels and uses them to generate an alignment between those metamodels. This alignment needs to be manually checked and can then be used to generate a model transformation. Our approach is built on the Similarity Flooding algorithm used in the fields of schema matching and ontology alignment. Experimental results comparing the effectiveness of the application of various implementations of this approach on real-world metamodels are given.

Automatic Extraction of a WordNet-Like Identifier Network from Software

A large part of the time allocated to software maintenance is dedicated to the program comprehension. Many approaches that uses the program structure or the external documentation have been created to assist program comprehension. However, the identifiers of the program are an important source of information that is still not widely used for this purpose. In this article, we propose an approach, based upon Natural Language Processing techniques, that automatically extracts and organizes concepts from software identifiers in a WordNet-like structure that we call textit{lexical views}. These lexical views give useful insight on an overall software architecture and can be used to improve results of many software engineering tasks. The proposal is evaluated against a corpus of 24 open source programs.

Using Games to Create Language Resources: Successes and Limitations of the Approach

One of the more novel approaches to collaboratively creating language resources in recent years is to use online games to collect and validate data. The most significant challenges collaborative systems face are how to train users with the necessary expertise and how to encourage participation on a scale required to produce high quality data comparable with data produced by “traditional” experts. In this chapter we provide a brief overview of collaborative creation and the different approaches that have been used to create language resources, before analysing games used for this purpose. We discuss some key issues in using a gaming approach, including task design, player motivation and data quality, and compare the costs of each approach in terms of development, distribution and ongoing administration. In conclusion, we summarise the benefits and limitations of using a gaming approach to resource creation and suggest key considerations for evaluating its utility in different research scenarios.

Antonymy and conceptual vectors

For meaning representations in NLP, we focus our attention on thematic aspects and conceptual vectors. The learning strategy of conceptual vectors relies on a morphosyntaxic analysis of human usage dictionary definitions linked to vector propagation. This analysis currently doesnt take into account negation phenomena. This work aims at studying the antonymy aspects of negation, in the larger goal of its integration into the thematic analysis. We present a model based on the idea of symmetry compatible with conceptual vectors. Then, we define antonymy functions which allows the construction of an antonymous vector and the enumeration of its potentially antinomic lexical items. Finally, we introduce a measure which evaluates how a given word is an acceptable antonym for a term.

Computing trees of named word usages from a crowdsourced lexical network

Thanks to the participation of a large number of persons via web-based games, a large-sized evolutionary lexical network is available for French. With this resource, we approached the question of the determination of the word usages of a term, and then we introduced the notion of similarity between these various word usages. So, we were able to build for a term its word usage tree: the root groups together all possible usages of this term and a search in the tree corresponds to a refinement of these word usages. The labelling of the various nodes of the word usage tree of a term is made during a width-first search: the root is labelled by the term itself and each node of the tree is labelled by a term stemming from the clique or quasi-clique this node represents. We show on a precise example that it is possible that some nodes of the tree, often leaves, cannot be labelled without ambiguity. This paper ends with an evaluation about word usages detected in our lexical network.

Mixing semantic networks and conceptual vectors: the case hyperonymy

In this paper, we focus on lexical semantics, a key issue in natural language processing (NLP) that tends to converge with conceptual knowledge representation (KR) and ontologies. When ontological representation is needed, hyperonymy, the closest approximation to the is-a relation, is at stake. In this paper we describe the principles of our vector model (CVM: conceptual vector model), and show how to account for hyperonymy within the vector-based frame for semantics. We show how hyperonymy diverges from is-a and what measures are more accurate for hyperonymy representation. Our demonstration results in initiating a cooperation process between semantic networks and conceptual vectors. Text automatic rewriting or enhancing, ontology mapping with natural language expressions, are examples of applications that can be derived from the function we define in this paper.

About Inferences in a Crowdsourced Lexical-Semantic Network

Automatically inferring new relations from already existing ones is a way to improve the quality of a lexical network by relation densification and error detection. In this paper, we devise such an approach for the JeuxDeMots lexical network, which is a freely avalaible lexical network for French. We first present deduction (generic to specific) and induction (specific to generic) which are two inference schemes ontologically founded. We then propose abduction as a third form of inference scheme, which exploits examples similar to a target term.

Software understanding: Automatic classification of software identifiers

Identifier names (e.g., packages, classes, methods, variables) are one of most important software comprehension sources. Identifier names need to be analyzed in order to support collaborative software engineering and to reuse source codes. Indeed, they convey domain concept of softwares. For instance, getMinimumSupport would be associated with association rule concept in data mining softwares, while some are difficult to recognize such as the case of mixing parts of words (e.g., initFeatSet). We thus propose methods for assisting automatic software understanding by classifying identifier names into domain concept categories. An innovative solution based on data mining algorithms is proposed. Our approach aims to learn character patterns of identifier names. The main challenges are (1) to automatically split identifier names into relevant constituent subnames (2) to build a model associating such a set of subnames to predefined domain concepts. For this purpose, we propose a novel manner for splitting such identifiers into their constituent words and use N-grams based text classification to predict the related domain concept. In this article, we report the theoretical method and the algorithms we propose, together with the experiments run on real software source codes that show the interest of our approach.

TOTAKI: A Help for Lexical Access on the TOT Problem

The JDM lexical network has been built thanks to on-line games the main of which, JeuxDeMots (JDM), was launched in 2007. It is currently a large lexical network, in constant evolution, containing more than 310,000 terms connected by more than 6.5 million relations. The riddle game Totaki (Tip Of the Tongue with Automated Knowledge Inferences), the initial version of which was elaborated with Michael Zock, was launched in a first version in 2010. The initial aim of this project is to cross validate the JDM lexical network. Totaki uses this lexical network to make proposals from user given clues, and in case of failure players can supply new information, hence enriching the network. Endogenous processes of inference, by deduction, induction, abduction, also allow to find new information not directly available in the network and hence lead to a densification of the network. The assumption about the validation is that if Totaki is able to guess proper terms from user clues, then the lexical network contains appropriate relations between words. Currently, Totaki achieves a 75 % success rate, to be compared to less than 50 % if the guessing is done by human users. One serious application of Totaki is to be viewed as a tool for lexical access and a possible remedy for the tip of the tongue problem. The Wikipedia encyclopaedia, built in a collaborative way, represents a very important volume of knowledge (about 1.5 million articles in its French version). The idea developed in this chapter consists in benefiting from Wikipedia to enrich the JDM network and evaluate the impact on Totaki performance. Instead of relying only on the JDM network, Totaki also makes use of information extracted from Wikipedia. The overall process is then both endogenous and exogenous. In a first part, we shall remind the reader the basic principles of a lexical network, then the aims and the underlying principles of the Totaki game. We shall see on examples Totaki may be used as a game to evaluate and enrich the JDM network, but also it may be considered as a tool for the Tip Of the Tongue problem; partial syntactic or morphologic information may be added to semantic information to help the user. In a second part, we shall show the results of the evaluation of the JDM network, results we obtained playing Totaki. We shall clarify the process allowing the introduction in the Totaki game of data extracted from Wikipedia as a complement in the information from the JDM network, and we shall briefly present the results provided by the first experiments.

Spreading Relation Annotations in a Lexical Semantic Network Applied to Radiology

Domain specific ontologies are invaluable but their development faces many challenges. In most cases, domain knowledge bases are built with very limited scope without considering the benefits of including domain knowledge to a general ontology. Furthermore, most existing resources lack meta-information about association strength weights and annotations frequency information like frequent, rare ... or relevance information like pertinent or irrelevant. In this paper, we are presenting a semantic resource for radiology built over an existing general semantic lexical network JeuxDeMots. This network combines weight and annotations on typed relations between terms and concepts. Some inference mechanisms are applied to the network to improve its quality and coverage. We extend this mechanism to relation annotation. We describe how annotations are handled and how they improve the network by imposing new constraints especially those founded on medical knowledge.