An Ontology-Based Approach to Information Retrieval

In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

Measuring the similarity between implicit semantic relations is an important task in information retrieval and natural language processing. For example, consider the situation where you know an entity-pair (e.g. Google, YouTube), between which a particular relation holds (e.g. acquisition), and you are interested in retrieving other entity-pairs for which the same relation holds (e.g. Yahoo, Inktomi). Existing keyword-based search engines cannot be directly applied in this case because in keyword-based search, the goal is to retrieve documents that are relevant to the words used in the query -- not necessarily to the relations implied by a pair of words. Accurate measurement of relational similarity is an important step in numerous natural language processing tasks such as identification of word analogies, and classification of noun-modifier pairs. We propose a method that uses Web search engines to efficiently compute the relational similarity between two pairs of words. Our method consists of three components: representing the various semantic relations that exist between a pair of words using automatically extracted lexical patterns, clustering the extracted lexical patterns to identify the different semantic relations implied by them, and measuring the similarity between different semantic relations using an inter-cluster correlation matrix. We propose a pattern extraction algorithm to extract a large number of lexical patterns that express numerous semantic relations. We then present an efficient clustering algorithm to cluster the extracted lexical patterns. Finally, we measure the relational similarity between word-pairs using inter-cluster correlation. We evaluate the proposed method in a relation classification task. Experimental results on a dataset covering multiple relation types show a statistically significant improvement over the current state-of-the-art relational similarity measures.

Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe.

Natural language processing (NLP) tasks such as word sense disambiguation, machine translation (MT) and part-of-speech tagging etc. require large scale lexical resources. These lexical resources have already been developed for digitally advanced languages, such as English, but yet to be developed for widely spoken but digitally young languages, such as Punjabi. WordNet is one such lexical resource that can be used for variety of NLP tasks ranging from digital dictionary to automated MT. The basic building block of WordNet is synset, a word sense with which one or more synonymous words are associated. Each synset in WordNet is linked to other synsets using lexical and semantic relations. Lexical relations are between word forms, whereas semantic relations exist between two whole synsets. This paper presents lexical and semantic relations of Punjabi WordNet, including synonymy, hypernymy/hyponymy, meronymy/holonomy, entailment and troponymy etc. It also illustrates the process of creation of Punjabi synsets from Hindi synsets, design of synsets and semantic databases for implementation of semantic relations for Punjabi WordNet.

Analyzing and modeling user behavior in the Information Retrieval (IR) process is important for IR research. In this paper, we propose a new research schema in IR research. In this schema, we first investigate humans' cognitive process when completing a specific IR task and build cognitive models for that task. The findings in the investigation and the proposed cognitive models are then utilized to improve machines' performance in the IR task. Through this research schema, we revisit three IR tasks. In the first study, we carefully analyze users' clicking behavior in mobile search and propose a mobile click model to extract relevance feedback from the mobile search logs. In the second study, we conduct an eye-tracking study to investigate how human assessors read a document during relevance judgment task and adopt the findings in building a novel retrieval model that can better approximate humans' relevance judgment. In the last study, we conduct another eye-tracking study to investigate humans' reading behavior when completing the reading comprehension task. We build a prediction model for user attention and leverage the predicted attention signals to improve the machine reading comprehension model. By successfully adopting this research schema to three different IR tasks, we demonstrate its effectiveness and generalizability.

Ontology is essential in the formalization of domain knowledge for effective human-computer interactions (i.e., expert-finding). Many researchers have proposed approaches to measure the similarity between concepts by accessing fuzzy domain ontology. However, engineering of the construction of domain ontologies turns out to be labor intensive and tedious. In this paper, we propose an approach to mine domain concepts from Wikipedia Category Network, and to generate the fuzzy relation based on a concept vector extraction method to measure the relatedness between a single term and a concept. Our methodology can conceptualize domain knowledge by mining Wikipedia Category Network. An empirical experiment is conducted to evaluate the robustness by using TREC dataset. Experiment results show the constructed fuzzy domain ontology derived by proposed approach can discover robust fuzzy domain ontology with satisfactory accuracy in information retrieval tasks.

Today, the widespread use of mobile devices allows users to search information "on the go»», whenever and wherever they want, no longer confining Information Retrieval to classic desktop interfaces. We believe that technical advances in Augmented Reality will allow Information Retrieval to go even further, making use of both the users» surroundings and their abilities to interact with the physical world. In this paper, we present the fundamental concept of Reality-Based Information Retrieval, which combines the classic Information Retrieval process with Augmented Reality technologies to provide context-dependent search cues and situated visualizations of the query and the results. With information needs often stemming from real-world experiences, this novel combination has the potential to better support both Just-in-time Information Retrieval and serendipity. Based on extensive literature research, we propose a conceptual framework for Reality-Based Information Retrieval. We illustrate and discuss this framework and present two prototypical implementations, which we tested in small user studies. They demonstrate the feasibility of our concepts and inspired our discussion of notable challenges for further research in this novel and promising area.

A well-designed information retrieval (IR) framework should provide powerful abstractions for common IR concepts and techniques. Central to the success of such a framework is whether these abstractions can be implemented to satisfy users' demands of functionality, performance, flexibility, and ease of use. This paper discusses implementation issues for a multimedia IR framework in the context of the recently proposed FIRE design. We present the software architecture of the framework, and outline the cornerstone components of its implementation. Issues of reusability, ease of use, and performance are discussed with references to solutions adopted in FIRE.

Previous research has shown that spatial ability is associated with performance on information retrieval tasks. Age-related changes in spatial ability may thus explain older adults' tendency to have lower performance on such tasks. However, previous studies have emphasized hierarchical information organization. The current study will examine the extent to which a different information retrieval interface can accommodate age-related changes in cognitive abilities. Younger and older adult performance in two information retrieval interfaces will be compared. One interface is meant to simulate hierarchically organized information systems while the second interface is specifically designed to reduce demands on age-related abilities while increasing demands on age-invariant abilities. It is expected that the results of this study will identify potential methods for improving information retrieval task performance for older adults.

Text Categorization is one of the most important tasks in information retrieval and data mining. This paper aims at investigating different variations of vector space models (VSMs) using KNN algorithm. we used 242 Arabic abstract documents that were used by (Hmeidi & Kanaan, 1997). The bases of our comparison are the most popular text evaluation measures; we use Recall measure, Precision measure, and F1 measure. The Experimental results against the Saudi data sets reveal that Cosine outperformed over of the Dice and Jaccard coefficients.

In the era of information explosion, efficiently retrieving relevant information from vast datasets has become a paramount challenge, with deep learning offering new avenues for enhancing information retrieval (IR) systems. This study introduces an innovative Recurrent Neural Network (RNN)-based approach to significantly improve the efficiency and accuracy of IR tasks, surpassing the limitations of traditional methods. By designing a specialized RNN architecture and comparing it against traditional and existing deep learning-based IR models across diverse datasets and various IR tasks—including document retrieval, question answering, and semantic search—our methodology demonstrates remarkable improvements in retrieval efficiency and accuracy. Specifically, the proposed model achieved substantial increases in precision, recall, and F1 score, highlighting the potential of RNNs in mastering the sequential and contextual nuances inherent in IR processes. This advancement offers a robust and efficient solution to the challenges of traditional IR systems, with future work aiming to integrate this model into broader IR applications and explore further enhancements using advanced neural network techniques.

In this paper, we propose a multilevel searching and re-ranking framework (MLSRF) for information retrieval. In this framework, indexes are constructed at multi-level such as document level and passage level. For each topic, we search against each index independently and generate a sub-result for corresponding index. The final result is generated by re-ranking and integrating the sub-results for all the indexes. Experiments on 2004 and 2005 HARD data sets show that the proposed framework can make a positive contribution on both document level retrieval and passage level retrieval. We also investigate the effect of the blind feedback method on TREC HARD data sets. The results show that the performance of blind feedback material depends on the topic it works on.

Examining the properties of representation spaces for documents or words in Information Retrieval (IR) – typically R n with n large– brings precious insights to help the retrieval process. Recently, several authors have studied the real dimensionality of the datasets, called intrinsic dimensionality, in specific parts of these spaces (Houle et al., 2012). They have shown that this dimensionality is chiefly tied with the notion of indiscriminability among neighbors of a query point in the vector space. In this paper, we propose to revisit this notion in the specific case of IR and to study its use in IR tasks. More precisely, we show how to estimate α from IR similarities and to use it in representtion spaces used for documents and words (Mikolov et al., 2013 ; Claveau et al., 2014). Indeed, we prove that α may be used to characterize difficult queries; moreover we show that this indiscriminability notion, applied to words, can help to choose terms to use for query expansion.

Description The question of terminology harmonization within the framework of data exchange and information retrieval is likely to dominate concerns in the field well beyond the turn of the next century. STP 1223 follows in a series of publications resulting from symposia that has been recognized in the world wide terminology community for its excellence, timeliness and relevance. 16 peer-reviewed papers in 5 sections: • Concept Congruence--Theory and Practice of Terminology • Design and Management of Terminologies--Varieties and Types • Harmonization in a Multilingual Environment • Computer Aided Terminology Work • Roundtable Discussion.

Doc'CISMeF (DC) is a semantic search engine used to find resources in CISMeF-BP, a quality controlled health gateway, which gathers guidelines available on the internet in French. Visualization of Concepts in Medicine (VCM) is an iconic language that may ease information retrieval tasks. This study aimed to describe the creation and evaluation of an interface integrating VCM in DC in order to make this search engine much easier to use. Focus groups were organized to suggest ways to enhance information retrieval tasks using VCM in DC. A VCM interface was created and improved using the ergonomic evaluation approach. 20 physicians were recruited to compare the VCM interface with the non-VCM one. Each evaluator answered two different clinical scenarios in each interface. The ability and time taken to select a relevant resource were recorded and compared. A usability analysis was performed using the System Usability Scale (SUS). The VCM interface contains a filter based on icons, and icons describing each resource according to focus group recommendations. Some ergonomic issues were resolved before evaluation. Use of VCM significantly increased the success of information retrieval tasks (OR=11; 95% CI 1.4 to 507). Nonetheless, it took significantly more time to find a relevant resource with VCM interface (101 vs 65 s; p=0.02). SUS revealed 'good' usability with an average score of 74/100. VCM was successfully implemented in DC as an option. It increased the success rate of information retrieval tasks, despite requiring slightly more time, and was well accepted by end-users.