A LEXICAL SIMPLIFICATION APPLICATION AND EFFECT IN INFORMATION RETRIEVAL

In information retrieval, vocabulary mismatch between the search vocabulary and documents vocabulary has been a common challenge for web users, hindering information access and retrieval. This issue is often attributed to the ambiguous representation of user information needs as queries, leading to the retrieval of many irrelevant documents, particularly for non-skilled web users. This paper aims to improve user query representation for effective retrieval of relevant documents from the web. To achieve this, a query expansion strategy was employed to identify terms with similar meanings to the user’s initial query term. The similarities between the expanded terms and the initial user query term were determined by calculating the cosine angle between the vector resenting document vocabulary and the vector representing query term. Thereafter, Formal Concept Analysis (FCA) was employed to analyze and present the results. Findings from the analysis revealed that concatenating similar terms with the initial user query terms resulted in a 0.16% improvement, as evident in the retrieval precision of 0.64% with the initial query and 0.80% with the expanded query terms.

Social network contains large amounts of user interests and preferences which may therefore help improve techniques for information retrieval, such as user profile construction and query expansion. By the manual annotation, we find that query intention normally consists of several retrieval behaviors and a certain number of social network behaviors. This paper argues that a user's query intention has a strong correlation with the corresponding social network contents, which focuses on two aspects. First, we analyze the proportion of social network websites in users' retrieval records, and evaluate the degree of relationship between information retrieval behavior and social network behavior from a statistic perspective. Second, a similarity between the two components of query intention, retrieval content and social network content, is calculated employing a topic model to measure the correlation of them from a semantic perspective. The evaluation on a ground-truth data of retrieval records indicates that a long-term retrieval procedure is usually accompanied with accessing social network websites. More importantly, the experimental results show that more than 54% of the content from social network has a strong correlation with users' query intention.

The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to the extraction of dependencies between terms. In this paper, we present a novel approach for mining knowledge supporting query expansion that is based on association rules. The key feature of our approach is a better trade-off between the size of the mining result and the conveyed knowledge. Thus, our association rules mining method implements results from Galois connection theory and compact representations of rules sets in order to reduce the huge number of potentially useful associations. An experimental study has examined the application of our approach to some real collections, whereby automatic query expansion has been performed. The results of the study show a significant improvement in the performances of the information retrieval system, both in terms of recall and precision, as highlighted by the carried out significance testing using the Wilcoxon test.

Research and experimentation using Arabic WordNet in the field of information retrieval are relatively new. It is limited compared to the research that has been done using Princeton WordNet. This work attempts to study the impact of Arabic WordNet on the performance of Arabic information retrieval. We extend Lucene with Arabic WordNet to expand user's queries. The major contribution of this study is to propose an interactive query expansion (IQE) methodology using the word's part-of-speech, according to the part it plays in a query. First, the user selects the appropriate part of speech for each term in the original query, and then he reselects the appropriate synonyms. Experimental results show that our IQE strategy produces a good Mean Average Precision (MAP), it is able to improve MAP by 12.6%, but no variant of automatic query expansion (AQE) strategies did. Nevertheless, the experiments allow us to conclude that with an appropriate use of Arabic WordNet as a source of linguistic information for AQE can improve effectiveness for Arabic information retrieval.

Extracting keywords and keyphrases mainly for identifying content of a document, has an importance role in text processing tasks such as text summarization, information retrieval, and query expansion. In this research, we introduce a new keyword/keyphrase extraction approach in which both single and multi-document keyword/keyphrase extraction techniques are considered. The proposed approach is specifically practical when a user is interested in additional data such as keywords/keyphrases related to a topic or query. In the proposed approach, first a set of documents are retrieved based on user's query, then a single document keyword extraction method is applied to extract candidate keyword/keyphrases from each retrieved document. Finally, a new re-scoring scheme is introduced to extract final keywords/keyphrases. We have evaluated the proposed method based on the relationship between the final keyword/keyphrases with the initial user query, and based user's satisfaction. Our experimental results show how much the extracted keywords/keyphrases are relevant and wellmatched with user's need.

Contextualized query expansion via unsupervised chunk selection for text retrieval

Information retrieval has become an important field of study and research under computer science due to explosive growth of information available in the form of full text, hypertext, administrative text, directory, numeric or bibliographic text. The research work is going on various aspects of information retrieval systems so as to improve its efficiency and reliability. This paper presents a comprehensive study, which discusses not only emergence and evolution of information retrieval but also includes different information retrieval models and some important aspects such as document representation, similarity measure and query expansion. Keywords—Information Retrieval, query expansion, similarity measure, query expansion, vector space model.

Pseudo-relevance feedback (PRF) is a very effective query expansion approach, which reformulates queries by selecting expansion terms from top k pseudo-relevant documents. Although standard PRF models have been proven effective to deal with vocabulary mismatch between users’ queries and relevant documents, expansion terms are selected without considering their similarity to the original query terms. In this article, we propose a method to incorporate word embedding (WE) similarity into PRF models for Arabic information retrieval (IR). The main idea is to select expansion terms using their distribution in the set of top pseudo-relevant documents along with their similarity to the original query terms. Experiments are conducted on the standard Arabic TREC 2001/2002 collection using three neural WE models. The obtained results show that our PRF extensions significantly outperform their baseline PRF models. Moreover, they enhanced the baseline IR model by 22% and 68% for the mean average precision (MAP) and the robustness index (RI), respectively.

Information Retrieval (IR) systems aim to retrieve data that satisfies certain requirements and constitute an important service in many types of networks, including Delay/Disruption Tolerant Networks (DTNs). In current DTN based IR systems, the data that satisfies a query is assumed to be stored on a single node. Therefore, once a node receives a query in which it has the corresponding data, the query can be resolved completely. However, in scenarios where a query requires data from multiple nodes, these IR systems may fail. Henceforth, in this paper, we propose Distributed Data-Centric Information Retrieval (DDC-IR), a data centric IR system that supports all query types; e.g., continuous and complex. More importantly, it is designed specifically to operate in DTNs. It also incorporates a new packet, aka Query Reply Packet, that includes both a query and one or more replies. We show how this packet facilitates efficient query resolution and enables data centric routing. In addition, it uses caching so that nodes store popular queries that has the effect of speeding up query resolution. We have conducted an extensive simulation study to compare DDC-IR to state of the art IR systems using the popular Random Waypoint model and a trace-file containing student movements on a campus. The results show that DDC-IR is able to resolve 50 % more queries and has an 80 % lower buffer occupancy level than existing IR systems. We also tested DDC-IR in networks with varying sizes. For networks with 100 nodes, DDC-IR is able to resolve queries while current IR systems fail to resolve any queries. In particular, when the number of nodes increases, current IR systems fail to resolve any queries, whilst DDC-IR is able to resolve complex and continuous queries. The influence of the number of sub-queries on query resolution time is also studied. Specifically, when the number of sub-queries in a complex query increases from five to nine, DDC-IR uses 50 % more time to resolve a query. In comparison, prior IR systems fail to resolve any queries.

This thesis investigates the question whether and how ontologies such as the ones currently evolving in the Semantic Web can serve as knowledge structures for the generation of query expansion terms in information retrieval systems. This issue is examined using a specific example domain, namely educational research. Initial results support the already well-researched finding that query expansion can increase recall. Subsequent experiments will focus on comparing the effectiveness of thesaurus and ontology-based query expansion, on identifying ontological relationships which are especially useful for the generation of query expansion terms in the domain of educational research, and on evaluating the usefulness of different ontological relationships as expansion terms for different types of queries, as well as for different query expansion modes.

To satisfy the user information needs, the use of his/her profile is adopted in the information retrieval process. Our goal is to build and exploit the user profile in search process. To achieve this goal, we adopt a Bayesian network as a strategy for personalized search since it provides a clear formalism to model qualitatively and quantitatively information types, namely, a user query, documents, index terms, and user's interests. In this paper, we present a Bayesian network model that captures the relationships between this information to evaluate the relevance as matching relationships between documents, user's interest and query concepts. An experimental evaluation was designed to appraise the exploitation impact of the user profile defined by his/her interests on the search results relevance.

Query-document vocabulary mismatch, the lack of query expressiveness for user needs and the phenomenon of short queries are the main issues associated with information retrieval systems. Query Expansion (QE) is one of the well-known alternative for overcoming these problems. It mainly involves finding synonyms or related words for the query terms. There are several approaches in the query expansion field such as statistical and semantic approaches; they focus on expanding the individual query terms rather than the entire query during the expansion process. An other category of approaches deals with the whole query by using a neural approach based on Pseudo Relevance feedback (PRF) documents. In this work, we carried out an ablation study to measure the impact of the classical and semantic (word embedding, order, context) based query expansion on the retrieval performance. The experiments conducted on the Arabic EveTAR dataset reveal that our hybrid proposed approach combining classical (PRF) and transformer (AraBERT) is competitive with the state-of-the-art methods. In fact, the obtained result in terms of the Mean Average Precision (MAP) is up to 0.72.

The Web has become a large repository of documents (or pages) written in many different languages. In this context, traditional information retrieval (IR) techniques cannot be used whenever the user query and the documents being retrieved are in different languages. To address this problem, new cross‐language information retrieval (CLIR) techniques have been proposed. In this work, we describe a method for cross‐language retrieval of medical information. This method combines query terms and related medical concepts obtained automatically through a categorization procedure. The medical concepts are used to create a linguistic abstraction that allows retrieval of information in a language‐independent way, minimizing linguistic problems such as polysemy. To evaluate our method, we carried out experiments using the OHSUMED test collection, whose documents are written in English, with queries expressed in Portuguese, Spanish, and French. The results indicate that our cross‐language retrieval method is as effective as a standard vector space model algorithm operating on queries and documents in the same language. Further, our results are better than previous results in the literature.

Query Expansion is commonly used in Information Retrieval to overcome vocabulary mismatch issues, such as synonymy between the original query terms and a relevant document. In general, query expansion experiments exhibit mixed results. Overall TREC Genomics Track results are also mixed; however, results from the top performing systems provide strong evidence supporting the need for expansion. In this paper, we examine the conditions necessary for optimal query expansion performance with respect to two system design issues: IR framework and knowledge source used for expansion. We present a query expansion framework that improves Okapi baseline passage MAP performance by 185%. Using this framework, we compare and contrast the effectiveness of a variety of biomedical knowledge sources used by TREC 2006 Genomics Track participants for expansion. Based on the outcome of these experiments, we discuss the success factors required for effective query expansion with respect to various sources of term expansion, such as corpus-based cooccurrence statistics, pseudo-relevance feedback methods, and domain-specific and domain-independent ontologies and databases. Our results show that choice of document ranking algorithm is the most important factor affecting retrieval performance on this dataset. In addition, when an appropriate ranking algorithm is used, we find that query expansion with domain-specific knowledge sources provides an equally substantive gain in performance over a baseline system.

A digital library is a type of information retrieval (IR) system. The existing information retrieval methodologies generally have problems on keyword-searching. We proposed a model to solve the problem by using concept-based approach (ontology) and metadata case base. This model consists of identifying domain concepts in user’s query and applying expansion to them. The system aims at contributing to an improved relevance of results retrieved from digital libraries by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to import the concept of ontology, making use of its advantage of abundant semantics and standard concept. Domain specific ontology can be used to improve information retrieval from traditional level based on keyword to the lay based on knowledge (or concept) and change the process of retrieval from traditional keyword matching to semantics matching. One approach is query expansion techniques using domain ontology and the other would be introducing a case based similarity measure for metadata information retrieval using Case Based Reasoning (CBR) approach. Results show improvements over classic method, query expansion using general purpose ontology and a number of other approaches.