Thesaurus Performance with Information Retrieval: Schema Matching as a Case Study

Thesaurus is used in many Information Retrieval (IR) applications such as data integration, data warehousing, semantic query processing and classifiers. It was also utilized to solve the problem of schema matching. Considering the fact of existence of many thesauri for a certain area of knowledge, the quality of schema matching results when using different thesauri in the same field is not predictable. In this paper, we propose a methodology to study the performance of the thesaurus in solving schema matching. The paper also presents results of experiments using different thesauri. Precision, recall, F-measure, and similarity average were calculated to show that the quality of matching changed according to the used thesaurus.

Effect of thesaurus size on schema matching quality

Term mismatch is a common limitation of traditional information retrieval (IR) models where relevance scores are estimated based on exact matching of documents and queries. Typically, good IR model should consider distinct but semantically similar words in the matching process. In this paper, we propose a method to incorporate word embedding (WE) semantic similarities into existing probabilistic IR models for Arabic in order to deal with term mismatch. Experiments are performed on the standard Arabic TREC collection using three neural word embedding models. The results show that extending the existing IR models improves significantly baseline bag-of-words models. Although the proposed extensions significantly outperform their baseline bag-of-words, the difference between the evaluated neural word embedding models is not statistically significant. Moreover, the overall comparison results show that our extensions significantly improve the Arabic WordNet based semantic indexing approach and three recent WE-based IR language models.

Not many XML information retrieval (IR) systems exist that allow easy addition of and switching between different IR models. Especially for the scientific environment where building a system takes a lot of time and keeps researchers away from the real work, i.e., investigating what is the most effective IR model, a platform that would provide this functionality would be ideal. For this reason we developed such an XML IR system. It is centered around a logical algebra, named score region algebra (SRA), that enables transparent specification of IR models for XML databases (see [1] for more details). The transparency is achieved by a possibility to instantiate various retrieval models, using abstract score functions within algebra operators, while logical query plan and operator definitions remain unchanged. Our algebra operators model three important aspects of XML IR: element relevance score computation, element score propagation, and element score combination. To implement a new IR model, one only needs to provide definitions for these abstract function classes. To illustrate the usefulness of our algebra our demo system supports several, well known IR scoring models (e.g., Language Models, Okapi, and tf.idf), combined with different score propagation and combination functions. The user can select which model to use at run time. Following good practice in database systems design, our prototype system has a typical three-layered architecture. (1) The conceptual layer takes a NEXI [3] query expression as input, e.g.,

Retrieval Augmented Generations (RAGs) is an emerging field of computing that has exceptional capabilities inherited from the strengths of both Large Language Models (LLMs) and Information Retrieval (IR) models. By integrating external, domain-specific knowledge into the response-generation process, RAG models overcome critical limitations of standalone LLMs, such as hallucinations, outdated knowledge, and limited domain specificity. RAGs bring novelty to its applications to wide range of domains, including healthcare where the need for accurate, transparent, and up-to-date information is critical. This paper presents a scholarly review of this cutting-edge field, with a focus on its adoption in healthcare. It synthesizes recent literature across various use cases, provides a comparative analysis of existing studies, identifies critical gaps in the current research, outlines the limitations in them, and suggests potential directions for future work. Unlike prior reviews, this work provides a structured comparative framework categorizing RAG healthcare applications by task, data source, model type, and evaluation strategy, thereby offering a roadmap for both researchers and practitioners.

Schema matching is widely used in many database applications, such as, data integration, data warehouse, data spaces, and ontology merging. In this paper, we propose multi-schema matching based on web structured information sources. There are two meanings at this point. Traditional matching techniques mainly address matching tasks between two attributes, namely pair wise-attribute correspondence. Thus, the first is that we will focus on find the semantic correspondence among multiple attributes, which is more difficult than pair wise-attribute correspondence. The main idea is to regard each attribute as a point in vector space and partition attributes into different set by clustering techniques. The attributes in the same cluster have the similar semantic. Second, we will use web sources that contain ample structured information to improve the quality of schema matching. We validate our approach with an experimental study, the results of which demonstrate that our approach is effective and has good performance.

The Information Retrieval (IR) problem can be described as the quest to find the set of relevant information objects corresponding to a given information need, which is represented by a request. IR begins with a user who wishes to satisfy an information need. The information need is typically formulated in the form of a request, denoted by q. The intention is that the request q be as good as possible description of the information need N. The information being queried is modelled as a set of information carriers, or documents. An often used premise in IR is the following: if a given document d is about the request q, then there is a high likelihood that d is relevant with respect to the associated information need. Thus, the IR problem is reduced to deciding the aboutness relation between documents and requests. Many IR models have been developed, and there is a wide variation in how they determine aboutness. A common approach is to equate aboutness with overlap, meaning if there is sufficient overlap between the document and the query, the document is deemed to be about the query. The degree of overlap determined by the retrieval model is usually expressed as a real number between zero (no overlap) and unity (complete overlap). In most models, “sufficient” overlap is not prescribed by the model but by experimentation within a given IR setting. Such experimentation may lead to the conclusion, say, that sufficient overlap is apparent when the overlap measure is greater than α, 0 < α ≤ 1. The value α varies from setting to setting.

Although transportation practitioners nowadays have an unprecedented level of access to massive information, there still exist substantial gaps in their ability to efficiently and reliably find the right information, at the right time, and for the task/decision at hand. To address this gap, this paper proposes a context-aware information retrieval (IR) approach, which can capture and exploit the conceptualization of user needs, decision context, and content meanings to support the retrieval of information that is more relevant to decision making. The proposed IR approach includes three primary components: semantic annotation (SA), semantic query processing (SQP), and semantic document ranking (SDR). This paper focuses on SA for IR for supporting the Transportation Project Environmental Review (TPER) decision-making process. The paper proposes an epistemology-based SA algorithm for automatically annotating webpages in the TPER domain with contextual concepts from an epistemological model. The TPER epistemology is a semantic model for representing and reasoning about information and information retrieval in the TPER domain. In developing the proposed algorithm, a number of syntactic-based and semantic-based annotation methods/algorithms were developed and tested. For the syntactic-based algorithms, the effect of syntactic expansion and filtering was investigated. For the semantic-based algorithms, different semantic similarity calculation methods were evaluated. All the algorithms were tested on a testing data set of 1,328 webpages, which were collected from the FHWA Environmental Review Toolkit Website, and evaluated in terms of Mean Average Precision (MAP) and average precision. The final, proposed SA algorithm achieved 84.07% MAP and 90.67% average precision at top 50 documents, on the testing data.

We report data fusion experiments carried out on the four best‐performing retrieval models from TREC 5. Three were conceptually/algorithmically very different from one another; one was algorithmically similar to one of the former. The objective of the test was to observe the performance of the 11 logical data fusion combinations compared to the performance of the four individual models and their intermediate fusions when following the principle of polyrepresentation. This principle is based on cognitive IR perspective (Ingwersen &amp; Järvelin, 2005) and implies that each retrieval model is regarded as a representation of a unique interpretation of information retrieval (IR). It predicts that only fusions of very different, but equally good, IR models may outperform each constituent as well as their intermediate fusions. Two kinds of experiments were carried out. One tested restricted fusions, which entails that only the inner disjoint overlap documents between fused models are ranked. The second set of experiments was based on traditional data fusion methods. The experiments involved the 30 TREC 5 topics that contain more than 44 relevant documents. In all tests, the Borda and CombSUM scoring methods were used. Performance was measured by precision and recall, with document cutoff values (DCVs) at 100 and 15 documents, respectively. Results show that restricted fusions made of two, three, or four cognitively/algorithmically very different retrieval models perform significantly better than do the individual models at DCV100. At DCV15, however, the results of polyrepresentative fusion were less predictable. The traditional fusion method based on polyrepresentation principles demonstrates a clear picture of performance at both DCV levels and verifies the polyrepresentation predictions for data fusion in IR. Data fusion improves retrieval performance over their constituent IR models only if the models all are quite conceptually/algorithmically dissimilar and equally and well performing, in that order of importance.

We present an efficient intelligent information retrieval model using reduction of domain-specific expert knowledge, demonstrating its use in the pathology medical domain. We created an information retrieval model that incorporates domain-specific to provide knowledgeable answers to users. This model converts domain-specific to a relationship of terms represented as quantitative values, which gives improved efficiency. The conversion technology, called knowledge reduction, enables the off-line calculation of separate from the information retrieval (IR) process. This results in the real-time processing of retrieval results. We performed a simulation of the developed Intelligent IR model in the Pathology medical domain. Our approach resulted in an approximately 30% performance gain measured by average precision at 11 standard recall levels metrics when compared with the vector space model based IR method.

Recent work have shown the potential of collaboration for solving complex or exploratory search tasks allowing to achieve synergic effects with respect to individual search, which is the prevalent information retrieval (IR) setting this last decade. This interactive multiuser context gives rise to several challenges in IR. One main challenge relies on the adaptation of IR techniques or models [8] in order to build algo-rithmic supports of collaboration distributing documents among users. The second challenge is related to the design of Collaborative Information Retrieval (CIR) models and their effectiveness evaluation since individual IR frameworks and measures do not totally fit with the collaboration paradigms. In this tutorial, we address the second challenge and present first a general overview of collaborative search introducing the main underlying notions. Then, we focus on related work dealing with collaborative ranking models and their effectiveness evaluation. Our primary objective is to introduce these notions by highlighting how and why they should be different from individual IR in order to give participants the main clues for investigating new research directions in this domain with a deep understanding of current CIR work.

No abstract available.

The domain of user-oriented and cognitive information retrieval (IR) is first discussed, followed by a discussion on the dimensions and types of models one may build for the domain. The focus of the present entry is on the models of user-oriented and cognitive IR, not on their empirical applications. Several models with different emphases on user-oriented and cognitive IR are presented�ranging from overall approaches and relevance models to procedural models, cognitive models, and task-based models. The present entry does not discuss empirical findings based on the models

Measuring the semantic similarity between phrases and sentences is an important task in natural language processing (NLP) and information retrieval (IR). We compare the quality of the distributional semantic NLP models against phrase-based semantic IR. The evaluation is based on the correlation between human judgements and model scores on a distributional phrase similarity task. We experiment with four NLP and two IR model variants. On the NLP side, models vary over normalization schemes and composition operators. On the IR side, models vary with respect to estimation of the probability of a term being in a document, namely P(t|d) where only term co-occurrence information is used and P(t|d, sim) which incorporates term distributional similarity. A mixture of the two methods is presented and evaluated. For both methods, word meanings are derived from large corpora of data: the BNC and ukWaC. One of the main findings is that grammatical distributional models give better scores than the IR models. This suggests that an IR model enriched with distributional linguistic information performs better in the long standing problem in IR of document retrieval where there is no direct symbolic relationship between query and document concepts.

Modern cities are increasingly becoming smart where a digital knowledge infrastructure is deployed by local authorities (e.g. City councils and municipalities) to better serve the information needs of their citizens, and to ensure the sustainability and efficient use of power and resources. This knowledge infrastructure consists of a wide range of systems from lowlevel physical sensors to advanced sensing devices through social sensors. The i-ASC 2014 workshop was the first international event, within the Information Retrieval (IR) community, that is dedicated to research on smart/future cities. In particular, the workshop was a venue for research on digesting the city's data streams and knowledge databases in order to serve the information needs of citizens and support decision making for local authorities. Possible use cases include helping tourists to find interesting places to go or activities to do while visiting a city, or assisting journalists in reporting local incidents. Indeed, the workshop was intended to foster the development of new information access and retrieval models that can harness effectively and efficiently the large number of heterogeneous big data streams in a city to provide a new generation of information services. The workshop was well attended, where more than 45 participants were officially registered. It featured two keynote talks from industry (IBM andWaag Society) and two invited talks from academia (Pisa and Edinburgh). In addition, seven refereed papers were presented before breakout groups considered questions and issues identified from a panel discussion.