Jean-Pierre Chevallet

About retrieval models and logic

In this paper we investigate some aspects of the logical approach for retrieval models. We develop here the idea that the intrinsic expressive power of logic has already brought its benefits in information retrieval through the design of the first logical retrieval model which has proved more general, or powerful, than any existing model. This benefit lies in the expressive power, or generality of logic on one side, and in its very close fitness with the fundamentals of information retrieval. We mainly develop this second point in giving an informal interpretation of the semantics of the logical model as it was first introduced by C. J. Van Rijsbergen in 1986. This discussion is also for us an occasion to investigate in more details some less known aspects of information retrieval, as for example the impact of new applications which already induce a complete revision of the notion of document. Then the logical model is presented as the one the expressive power of which may provide a much needed common framework for a coherent integration of recent approaches in information retrieval involving the use of Natural Language Processing techniques (NLP) or Artificial Intelligence (AI) techniques. This idea is further developed in showing that the logical approach may also help in giving new insights into classical retrieval models and even help in improving them in a coherent way. An example of this later idea is developed with the Boolean model.

Model Fusion in Conceptual Language Modeling

We study in this paper the combination of different concept detection methods for conceptual indexing. Conceptual indexing shows effective results when large knowledge bases are available. But concept detection is not always accurate and errors limit interest of concept usage. A solution to solve this problem is to combine different concept detection methods. In this paper, we investigate several ways to combine concept detection methods, both on queries and documents, within the framework of the language modeling approach to IR. Our experiments show that our model fusion improves the standard language model by up to 17% on mean average precision.

Wikipedia-based semantic query enrichment

We deal, in this paper, with the short queries (containing one or two words) problem. Short queries have no sufficient information to express their semantics in a non ambiguous way. Pseudo-relevance feedback (PRF) approach for query expansion is useful in many Information Retrieval (IR) tasks. However, this approach does not work well in the case of very short queries. Therefore, we present instead of PRF a semantic query enrichment method based on Wikipedia. This method expands short queries by semantically related terms extracted from Wikipedia. Our experiments on cultural heritage corpora show significant improvement in the retrieval performance.

The Effective Relevance Link between a Document and a Query

This paper proposes to understand the retrieval process of relevant documents against a query as a two-stage process: at first an identification of the reason why a document is relevant to a query that we called the Effective Relevance Link, and second the valuation of this link, known as the Relevance Status Value (RSV). We present a formal definition of this semantic link between d and q. In addition, we clarify how an existing IR model, like Vector Space model, could be used for realizing and integrating this formal notion to build new effective IR methods. Our proposal is validated against three corpuses and using three types of indexing terms. The experimental results showed that the effective link between d and q is very important and should be more taken into consideration when setting up an Information Retrieval (IR) Model or System. Finally, our work shows that taking into account this effective link in a more explicit and direct way into existing IR models does improve their retrieval performance.

Experiences in Information Retrieval Modelling Using Structured Formalisms and Modal Logic

During the year 1978, we began to work on Information Retrieval (IR) with the PIAF prototype and MISTRAL project. PIAF was a French natural language analyser based on morphological techniques. The goal was the integration of the PIAF system to the MISTRAL IR system. The CONCERTO project in 1980 was an indexing project of French technical documents. We realised at that time that some improvements were needed to obtain better results in terms of recall and precision; a more effective natural language analysis, and the use of thesauri and user profiles were advanced.

Revisiting Exhaustivity and Specificity Using Propositional Logic and Lattice Theory

Exhaustivity and Specificity in logical Information Retrieval framework were introduced by Nie [16]. However, even with some attempts, they are still theoretical notions without a clear idea of how to be implemented. In this study, we present a new approach to deal with them. We use propositional logic and lattice theory in order to redefine the two implications and their uncertainty P(d → q) and P(q → d). We also show how to integrate the two notions into a concrete IR model for building a new effective model. Our proposal is validated against six corpora, and using two types of terms (words and concepts). The experimental results showed the validity of our viewpoint, which state: the explicit integration of Exhaustivity and Specificity into IR models will improve the retrieval performance of these models. Moreover, there should be a type of balance between the two notions.

Logics, Lattices and Probability: The Missing Links to Information Retrieval

Logic-based information retrieval (IR) models represent the retrieval decision as an implication d→q between a document d and a query q, where d and q are logical sentences. However, d→q is generally a binary decision, thus we need a measurement to estimate the degree to which d implies q, denoted U(d→q). Most of the existing logic-based IR models either do not precisely define the implication d→q or use non-classical definitions. Some models also define the uncertainty U in informal ways. More importantly, they use two non-related frameworks to define d→q and its uncertainty U, even though the two notions are intrinsically related. The goal of this study is to propose a new logic-based IR model, which overcome these shortcomings. To this end, we first propose to replace the implication d→q by the validity of material implication ⊨d⊃q. Second, we redefine and adapt the mathematical relationship between logics, lattices and probability. Our new IR model presents a possible formalism for van Rijsbergens intuition about replacing U(d→q) by P(q∣d).

Enhancing semantic relation quality of UMLS knowledge sources

The quality of semantic tuples (semantic triples forming subject-predicate-object) has significant impact in most text mining and knowledge discovery applications. The practical success and usability of these applications momentously depends on the quality of the extracted semantic triples. Most biomedical semantic resources have been developed for different contexts focusing on the structural representation but with less attention on the acceptability and naturalness of the individual semantic triples. In this article, we presented an integrated approach for enhancing the quality of semantic tuples in the UMLS knowledge sources. The approach is based on the integration of three existing auditing techniques: avoiding redundant classifications of semantic concepts, reducing hierarchical and associative relationship inconsistencies. We evaluated the approach based on the number of identified wrongly assigned concepts and inconsistent relationships obtained. The quality of each semantic triple is evaluated based on the acceptability and naturalness of the semantic tuples. The evaluation shows promising results. In the evaluation, we have extracted 10,082 semantic triples randomly from UMLS and obtained 5646 taxonomically and 4436 non-taxonomically related semantic triples. 826 concepts are found redundantly classified and 352 are found hierarchically inconsistent. In non-taxonomic semantic triples, out of 4436, 726 are found to be inconsistent. The quality (acceptability and naturalness) of each semantic triples of the first 100 are also evaluated using domain experts. The Cohens kappa coefficient is used to measure the degree of agreement between the annotators and the result is promising (0.8).

MRIM-LIG at ImageCLEF 2009: robotvision, image annotation and retrieval tasks

This paper describes mainly the experiments that have been conducted by the MRIM group at the LIG in Grenoble for the the ImageCLEF 2009 campaign, focusing on the work done for the Robotvision task. The proposal for this task is to study the behaviour of a generative approach inspired by the language model of information retrieval. To fit with the specificity of the Robotvision task, we added post-processing in a way to tackle with the fact that images do belong only to several classes (rooms) and that image are not independent from each others (i.e., the robot cannot in one second be in three different rooms). The results obtained still need improvement, but the use of such language model in the case of Robotvision is showed. Some results related to the Image Retrieval task and the Image annotation task are also presented.