Lucene Research Articles

Building a searchable online corpus of Australian and New Zealand aligned speech

ABSTRACT Advances in automatic speech recognition technology, increases in bandwidth availability, and the widespread use of video streaming and sharing platforms have opened new horizons for corpus phonetics. CoANZSE Audio, a searchable online version of the Corpus of Australian and New Zealand Spoken English, provides access to over 195 million words of transcribed speech from transcripts of videos uploaded to YouTube by councils and other local government entities in Australia and New Zealand. Audio and forced alignment files are also available, making the resource suitable for the investigation of a range of research questions pertaining to morphosyntax, phonetics, and discourse. The resource, which is freely available via login through CLARIN, Europe’s main language resources infrastructure network, was created through the use of open-source tools and software: yt-dlp, a Python library for collecting data from video and streaming websites; the Montreal Forced Aligner, a recent neural network alignment suite; and Parselmouth-Praat, Python bindings for the Praat acoustic analysis software. The website is powered by BlackLab, which combines a powerful search engine based on Apache Lucene with an intuitive web frontend. CoANZSE Audio may be useful for the investigation of regional differentiation of language features, and with additional annotation, differences in feature use according to social or demographic groups. Recent applications have included studies of double modals, a rare syntactic feature, and apology sequences. The nature of the audio and alignment data may make the resource especially suitable for the study of regional phonetic variation. Furthermore, the methods used to create the resource may be of interest to researchers seeking to adopt a pipeline approach for the creation of specialized corpora from publicly available online content.

Innovative Approaches to Full-Text Search with Solr and Lucene

Full-text search engines help efficiently process large volumes of textual material and provide appropriate results. Apache Solr and Apache Lucene are popular full-text search tools for indexing and querying huge datasets. This research study examines Solr and Lucene's strengths, weaknesses, and unique methods for improving full-text search efficiency and accuracy. Apache Lucene, the fundamental full-text search framework, has extensive indexing and querying features. Developers may tailor the search process using its flexible and extendable framework. Advanced indexing algorithms like inverted indices and tokenization underpin Lucene's search capabilities. However, complicated query needs and efficient large-scale data management remain problems. Solr, founded on Lucene, adds faceting, distributed searching, and rich text analysis to its search engine. Enterprise applications may use Solr's high availability, fault tolerance, and large-scale deployments. Solr has performance tuning and setup complexity issues despite these benefits. This study explores novel solutions to these issues and improves full-text search. Advanced tokenization and normalization may improve indexing tactics. Machine learning algorithms increase search relevancy, providing more accurate and contextual results. Query processing optimization is another invention. Caching, query rewriting, and parallel processing may minimize query latency and boost throughput. GPUs are also used to improve query execution. The article also discusses integrating Solr and Lucene with big data platforms and cloud services. Distributed computing frameworks and cloud storage may improve scalability and real-time search. How Solr and Lucene may incorporate AI and NLP to improve search accuracy and user experience is also investigated.

SCIPIS: Scalable and concurrent persistent indexing and search in high-end computing systems

While it is now routine to search for data on a personal computer or discover data online, there is no such equivalent method for discovering data on large parallel and distributed file systems commonly deployed on HPC systems. In contrast to web search, which has to deal with a larger number of relatively small files, in HPC applications there is a need to also support efficient indexing of large files. We propose SCIPIS, an indexing and search framework, that can exploit the properties of modern high-end computing systems, with many-core architectures, multiple NUMA nodes and multiple NVMe storage devices. SCIPIS supports building and searching TFIDF persistent indexes, and can deliver orders of magnitude better performance than state-of-the-art approaches. We achieve scalability and performance of indexing by decomposing the indexing process into separate components that can be optimized independently, by building disk-friendly data structures in-memory that can be persisted in long sequential writes, and by avoiding communication between indexing threads that collaboratively build an index over a collection of large files. We evaluated SCIPIS with three types of datasets (logs, scientific data, and metadata), on systems with configurations up to 192-cores, 768 GiB of RAM, 8 NUMA nodes, and up to 16 NVMe drives, and achieved up to 29x better indexing while maintaining similar search latency when compared to Apache Lucene.

Document Retrieval System for Biomedical Question Answering

In this paper, we describe our biomedical document retrieval system and answers extraction module, which is part of the biomedical question answering system. Approximately 26.5 million PubMed articles are indexed as a corpus with the Apache Lucene text search engine. Our proposed system consists of three parts. The first part is the question analysis module, which analyzes the question and enriches it with biomedical concepts related to its wording. The second part of the system is the document retrieval module. In this step, the proposed system is tested using different information retrieval models, like the Vector Space Model, Okapi BM25, and Query Likelihood. The third part is the document re-ranking module, which is responsible for re-arranging the documents retrieved in the previous step. For this study, we tested our proposed system with 6B training questions from the BioASQ challenge task. We obtained the best MAP score on the document retrieval phase when we used Query Likelihood with the Dirichlet Smoothing model. We used the sequential dependence model at the re-rank phase, but this model produced a worse MAP score than the previous phase. In similarity calculation, we included the Named Entity Recognition (NER), UMLS Concept Unique Identifiers (CUI), and UMLS Semantic Types of the words in the question to find the sentences containing the answer. Using this approach, we observed a performance enhancement of roughly 25% for the top 20 outcomes, surpassing another method employed in this study, which relies solely on textual similarity.

SSD In-Storage Computing for Search Engines

SSD-based in-storage computing (called ”Smart SSDs”) allows application-specific codes to execute inside SSDs to exploit the high internal bandwidth and energy-efficient processors. As a result, Smart SSDs have been successfully deployed in many industry settings, e.g., Samsung, IBM, Teradata, and Oracle. Moreover, researchers have also demonstrated their potential opportunities in database systems, data mining, and big data processing. However, it remains unknown whether search engine systems can benefit from Smart SSDs. This work takes a first step to answer this question. The major research issue is what search engine query processing operations can be cost-effectively offloaded to SSDs. For this, we carefully identified the five most commonly used search engine operations that could potentially benefit from Smart SSDs: intersection, ranked intersection, ranked union, difference, and ranked difference. With close collaboration with Samsung, we offloaded the above five operations of Apache Lucene (a widely used open-source search engine) to Samsungs Smart SSD. Finally, we conducted extensive experiments to evaluate the system performance and tradeoffs by using both synthetic datasets and real datasets. The experimental results show that Smart SSDs significantly reduce the query latency by a factor of 2-3 and energy consumption by 6-10 for most of the aforementioned operations.

IMPLEMENTATION OF TEXT INDEXING SYSTEM IN WEB-BASED DOCUMENT SEARCH APPLICATION USING MONGODB

The rapid growth of information technology has led to an increase in the amount of data stored in databases every day. Relational databases (SQL) that have been in use for a long time are now being developed with the emergence of NoSQL databases such as MongoDB. MongoDB stores data in BSON format and has a Text Indexes feature that is useful for speeding up text search on string content. This feature is particularly useful in searching for data in the form of texts or strings in large quantities. MongoDB's Text Indexes have a flexible schema that does not require a strict schema structure to index text data, unlike SQL databases that require columns with the appropriate data type to perform indexing. MongoDB's Text Indexes support more languages than SQL because they use an open-source text search engine called Apache Lucene. In this study, the researcher will implement Text Indexing on document data (PDF) that has been converted into text, then inserted into MongoDB before indexing. Afterward, the researcher will compare the performance of search queries between indexed and non-indexed data in MongoDB in terms of speed. The comparison results will be presented in tables and graphs to facilitate understanding. Based on the research conducted, it can be concluded that the use of the text indexing feature in MongoDB can speed up keyword or string search time. In the experiment conducted using 5000 data records, the results showed that the use of text indexing for searching 1 keyword resulted in a search speed improvement of 11705,88%, for searching 2 keywords it was 60833,33%, and for searching 3 keywords it was 44320%.

Towards "Biodiversity PMC"

The Swiss Institute of Bioinformatics (SIB) Literature services (SIBiLS, Gobeill et al. 2020) provides powerful search capabilities to explore the life and health sciences literature by mirroring the United States National Institute of Health's National Library of Medicine (NIH/NLM) (MEDLINE) and National Center for Biotechnology Information (NCBI) PubMed Central® contents. In the course of the BiCIKL project, SIBiLS started indexing a larger set of biodiversity-related contents in the broad sense including environmental sciences and ecology, to build a new literature database called "Biodiversity PMC". In addition to MEDLINE and PubMed Central, SIBiLS is now providing a unique entry point to half a million taxonomic treatments extracted by Plazi, as well as to a growing set of full-text article XMLs from Pensoft, which were not included into the original PubMed Central. The services can be accessed via a new Graphic User Inteface and an OpenAPI. In addition to usual search operators (using the Apache Lucene syntax), the contents are normalized using a large collection of life sciences terminologies and ontologies. Each instance of a term (or its synonym) is normalized with a unique accession number to support a semantically richer search experience. Of particular interest for the biodiversity communities, SIBiLS contents are normalized using ENVO (Environmental Ontology). Further, taxonomic names are normalized using both the NCBI Taxonomy and the Open Tree of Life, which include names from the Catalogue of Life. The resulting data graph contains 12 billion normalized descriptors and supports access via keyword search, as well as via an original question answering interface, which can help provide new perspectives when navigating the life and health sciences. The data (Journal Publishing Tag Set, JATS, and BioC) are fully available under CC-BY 4.0 licences.

RECOMMENDING JAVA API METHODS BASED ON PROGRAMMING TASK DESCRIPTIONS BY NOVICE PROGRAMMERS

The overwhelming number of Application Programming Interfaces (APIs) and the lexical gap between novices’ programming task descriptions in their search queries and API documentations deter novice programmers from finding suitable API methods to be used in their code. To address the lexical gap, this study investigated novice programmers’ descriptions of their programming tasks and used the found insights in a novel approach (APIFind) for recommending relevant API methods for the programming tasks. Queries written by novice programmers were collected and analysed using term frequency and constituency parsing. Four common patterns related to the return type of an API method and/or API class that provides an implementation for the API method were found and captured in the Novice Programming Task Description Model (NPTDM). APIFind uses NPTDM that was operationalised in a rule-based module, a WordNet map of API word-synonyms, a programming task dataset comprising the collected queries, a Java API class and method repository, a Stack Overflow Q&A thread repository, and the BM25 model in Apache Lucene, to produce the top-5 API methods relevant to a search query. Benchmarking results using mean average precision @ 5 and mean reciprocal rank @ 5 as the evaluation metrics show that APIFind outperformed BIKER and CROKAGE when the novice queries test dataset was used. It performed slightly better than BIKER but slightly worse than CROKAGE when the reduced BIKER test dataset was used. In conclusion, common patterns exist in novice programmers’ search queries and can be used in API recommendations for novice programmers.

The Analysis of Open Source Search Engines

In this information era of high-speed Internet development, the volume of information has grown exponentially along with the expansion in Internet usage by the general people and the steady strengthening of business and institutional information technology. How to find the needed information quickly and accurately from the vast amount of digital information has become an urgent demand of the people of the whole country. The term "open source search engine" simply denotes that anyone may use and alter the source code as they see fit. Open source search engines provide an excellent way and material for people to learn, study and master search technology. This study aims to compare different open source search engines. In this paper compares MeiliSearch and Apache Solr; ElasticSearch and Typesense. Introduce how these engines work and the advantage and disadvantage of theses open source search engines. The section four gives a case study of Apache Lucene.

An Introduction to Cordra

Cordra is a digital object server that can function as a key infrastructural piece in FAIR DO (findable, accessible, interoperable and reusable digital object) implementations. Cordra manages JSON records and payloads as typed digital objects identified by handles. Cordra is neither a database nor an indexer, but it integrates the two and provides a unified interface. Cordra is intended to support both quick prototyping as well as production systems. For prototyping, Cordra makes it easy to get up and running rapidly with a digital object server. A potential Cordra administrator can download Cordra and very quickly have a server which supports creation, search, and retrieval of digital objects with resolvable identifiers. The server supports Digital Object Interface Protocol (DOIP) and HTTP APIs out of the box, as well as an immediately usable prototype user interface. Cordra saves substantial development time as it comes with ready-made functionality ranging from user authentication and access control to information validation, enrichment, storing, and indexing. By default, Cordra is configured to store objects on the local file system of the machine and use embedded Apache Lucene for indexing. Simply by editing type definitions in Cordra's user interface, the administrator can start changing the behavior of the APIs and user interface in real time for experimentation, including adding custom operations. For production use, Cordra allows intensive extension and customization of the processes underlying the digital object server: how digital objects are stored and indexed, how they are validated and enriched, how users authenticate, when and to whom to give access to objects, and what custom operations can be performed. In production Cordra is run at scale, supporting high reliability and performance; among other options Cordra supports MongoDB and Amazon S3 for storage, and Elasticsearch and Apache Solr for indexing. By definition of the underlying types and operations, Cordra is intended to serve directly as the API backend for a production application. This talk will cover basic Cordra features as well as customization/configuration basics. Examples of current use will be shown, including the use of the Digital Object Interface Protocol (DOIP), for which Cordra serves as a reference implementation. Current users of Cordra include the Derivatives Service Bureau (DSB), which uses Cordra as part of its backend to manage the automated generation of International Securities Identification Numbers (ISINs) for OTC derivatives in the financial services sector; and the British Standard Institute (BSI) whose Identify service for construction product manufacturers aims to assign a Universal Persistent Identification Number (UPIN) "for every product that is specified and incorporated in a building structure". The DSB, Cordra users since 2017, has a production system with over 80 million identified digital objects which receives millions of searches each month. BSI.Identify has a system where Cordra's DOIP interface is directly accessible as the service's public API.

Vovel metrics-novel coupling metrics for improved software fault prediction.

Software is a complex entity, and its development needs careful planning and a high amount of time and cost. To assess quality of program, software measures are very helpful. Amongst the existing measures, coupling is an important design measure, which computes the degree of interdependence among the entities of a software system. Higher coupling leads to cognitive complexity and thus a higher probability occurrence of faults. Well in time prediction of fault-prone modules assists in saving time and cost of testing. This paper aims to capture important aspects of coupling and then assess the effectiveness of these aspects in determining fault-prone entities in the software system. We propose two coupling metrics, i.e., Vovel-in and Vovel-out, that capture the level of coupling and the volume of information flow. We empirically evaluate the effectiveness of the Vovel metrics in determining the fault-prone classes using five projects, i.e., Eclipse JDT, Equinox framework, Apache Lucene, Mylyn, and Eclipse PDE UI. Model building is done using univariate logistic regression and later Spearman correlation coefficient is computed with the existing coupling metrics to assess the coverage of unique information. Finally, the least correlated metrics are used for building multivariate logistic regression with and without the use of Vovel metrics, to assess the effectiveness of Vovel metrics. The results show the proposed metrics significantly improve the predicting of fault prone classes. Moreover, the proposed metrics cover a significant amount of unique information which is not covered by the existing well-known coupling metrics, i.e., CBO, RFC, Fan-in, and Fan-out. This paper, empirically evaluates the impact of coupling metrics, and more specifically the importance of level and volume of coupling in software fault prediction. The results advocate the prudent addition of proposed metrics due to their unique information coverage and significant predictive ability.

Two-Way Refinement Approach For Extra Corrupted Shard Removal In Elastic Search With Lucene And Translog

Elasticsearch is most popular search engine which is based on Apache Lucene. Many advantages are identified with the Elasticsearch. For every inserted document or record the Elasticsearch’s auto-generated id values are created. But this may leads to increasing the duplicate values. To overcome this various duplicate methods are introduced by various researchers. Indexing is very important for the elastic search removing duplicates in elastic is based on indexing. For this Lucene Index and Translog are used. This can be used for all types of data in Elastic search. Many researchers working on removing duplicates and shards from the data. But still there is lot of corrupted shards is present in output. To overcome this, A Two way Refining Algorithm (TWRA) is introduced to remove the extra corrupted shards for extra refinement of data. The TWRA consists of two refinements of data such as Advanced Advanced Data Cleaning and Advanced Data Filtering Algorithm. Experimental results show the performance of the proposed methodology.

CLUSTERING AND INDEXING OF MULTIPLE DOCUMENTS USING FEATURE EXTRACTION THROUGH APACHE HADOOP ON BIG DATA

Bigdata is a challenging field in data processing since the information is retrieved from various search engines through internet. A number of large organizations, that use document clustering,fails in arranging the documents sequentially in their machines. Across the globe, advanced technologyhas contributed to the high speed internet access. But the consequences of useful yet unorganized information in machine files seemto be confused in the retrieval process. Manual ordering of files has its own complications. In this paper, application software like Apache Lucene and Hadoop have taken a lead towards text mining for indexing and parallel implementation of document clustering. In organizations, it identifies the structure of the text data in computer files and its arrangement from files to folders, folders to subfolders, and to higher folders. A deeper analysis of document clustering was performed by considering various efficient algorithms like LSI, SVD and was compared with the newly proposed updated model of Non-Negative Matrix Factorization. The parallel implementation of hadoopdevelopedautomatic clusters for similar documents. MapReduce framework enforced its approach using K-means algorithm for all the incoming documents. The final clusters were automatically organized in folders using Apache Lucene in machines. This model was tested by considering the dataset of Newsgroup20 text documents. Thus this paper determines the implementation of large scale documents using parallel performance of MapReduce and Lucenethat generate automatic arrangement of documents, which reduces the computational time and improves the quick retrieval of documents in any scenario.

CIDACS-RL: a novel indexing search and scoring-based record linkage system for huge datasets with high accuracy and scalability

BackgroundRecord linkage is the process of identifying and combining records about the same individual from two or more different datasets. While there are many open source and commercial data linkage tools, the volume and complexity of currently available datasets for linkage pose a huge challenge; hence, designing an efficient linkage tool with reasonable accuracy and scalability is required.MethodsWe developed CIDACS-RL (Centre for Data and Knowledge Integration for Health – Record Linkage), a novel iterative deterministic record linkage algorithm based on a combination of indexing search and scoring algorithms (provided by Apache Lucene). We described how the algorithm works and compared its performance with four open source linkage tools (AtyImo, Febrl, FRIL and RecLink) in terms of sensitivity and positive predictive value using gold standard dataset. We also evaluated its accuracy and scalability using a case-study and its scalability and execution time using a simulated cohort in serial (single core) and multi-core (eight core) computation settings.ResultsOverall, CIDACS-RL algorithm had a superior performance: positive predictive value (99.93% versus AtyImo 99.30%, RecLink 99.5%, Febrl 98.86%, and FRIL 96.17%) and sensitivity (99.87% versus AtyImo 98.91%, RecLink 73.75%, Febrl 90.58%, and FRIL 74.66%). In the case study, using a ROC curve to choose the most appropriate cut-off value (0.896), the obtained metrics were: sensitivity = 92.5% (95% CI 92.07–92.99), specificity = 93.5% (95% CI 93.08–93.8) and area under the curve (AUC) = 97% (95% CI 96.97–97.35). The multi-core computation was about four times faster (150 seconds) than the serial setting (550 seconds) when using a dataset of 20 million records.ConclusionCIDACS-RL algorithm is an innovative linkage tool for huge datasets, with higher accuracy, improved scalability, and substantially shorter execution time compared to other existing linkage tools. In addition, CIDACS-RL can be deployed on standard computers without the need for high-speed processors and distributed infrastructures.

Search Engine for Halal Linked Open Data Using Entity Ranking Approach

Halal concept is an essential aspect of Muslim daily life. Currently, many organizations around the world provide halal certification services as known as halal certification bodies. In the majority, these organizations provide halal product information on their website. However, information is presented in different formats, such as pdf, table, and text. As a result, the user is difficult to search for information on these websites. Therefore, we develop search engine on halal linked open data to facilitate users for searching halal products. We use an entity ranking approach to retrieve relevant items based on user queries that consist of an independent-ranking and dependent-ranking method. Independent ranking employs a link-count approach to indicate the information richness of the entity. Dependent ranking employs term frequency-inverse entity frequency (TF-IEF) to measure the similarity of an entity based on terms. We use Apache Lucene to perform indexing and search process. Also, we use the Neo4j graph database to save entity ranking computation results. The results show that the system delivers excellent results. The Mean Average Precision (MAP) for top-5 results is 91,2%.

PiNET: a versatile web platform for downstream analysis and visualization of proteomics data.

Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

Indexing documents with reliable indexing techniques using Apache Lucene in Hadoop

Mostly 85% of the data is presented in the form of text, which is the human-readable format. Present educational, business, medical organisations, etc. making use of big data analytics for storage of data and processing that stored data by using information retrieval. Often time's text documents have been transferred from one system to another system without any restrictions like, structured, unstructured and semi-structured data. Systems are well performed with high speed and less complexity only when it has all the data arranged in an orderly way. This paper describes how documents of text data are being Indexed using Apache Lucene with approaches in Hadoop. Most of the applications that deal with huge data over the internet are completely lacking. Use of effective analysis and techniques allow users in resulting high-performance and a challenging option in leading big data analytics.

Lucene-P2: A Distributed Platform for Privacy-Preserving Text-based Search.

Information retrieval (IR) plays an essential role in daily life. However, currently deployed IR technologies, e.g., Apache Lucene - open-source search software, are insufficient when the information is protected or deemed to be private. For example, submitting a query to a publicly available search engine (e.g., Bing or Google) requires disclosing potentially delicate facts (e.g., thoughts about abortion), as well as the websites the user considers interesting. Similarly, when a private database contains sensitive information needed by the user, it cannot be searched freely. Over the past decade, various approaches, generally referred to as private information retrieval, have been proposed to obfuscate queries and responses, but they are limited in that the retrieved information is inadequate to compute relevancy. To address such limitations, this paper introduces the necessary techniques to build Lucene-P2 that allows one party to discover whether a second party harbors any relevant textual information without either party disclosing any information.

Design and Develop CMS for Sindhi E-News Papers

Objective: To design and Develop CMS for Sindhi e-newspaper by using the searching techniques. Methods/analysis: Existing e-newspapers are not implementing any CMS with searching techniques. The research methodology is supposed to b experimental. A web CMS is developed for Sindhi e-newspaper by using the following tools/technologies: JavaServer Faces, MYSQL and Apache Tomcat. Findings: All news material is set at the server in the Sindhi language and information is rendered precisely on account of the Unicode support. Novelty/improvement: Structured Web Content Management System is easy to understand that gives the news thing classifications. The web-based interface is accessible to peruse neighborhood news like Kawish, Express, and Dawn that are not much easy to understand and every substance is accessible in the picture group just as a composed substance that is the reason there is no scanning module to look for specific news things. We have organized and built up an application for neighborhood newspapers and provided a checking module to look for a required substance of the news content. The looking is unfathomably astoundingly smart since Apache Lucene is working at rear close during interest.Keywords: CMS, Apache Lucene, HTML, API, JCP, JVM

Medical Terminology Server for the Hospital of Clinics of Paraguay using Apache Lucene and the UMLS Metathesaurus

The current process of medical terminology coding in health standards at the Hospital of Clinics of Paraguay is performed by physicians, normally interns or residents. They search the medical terminology code in impressed coding manuals or in internet by using their cell phones. This search process takes a lot time to be done during the medical consultation. This work proposes and evaluates a friendly medical terminology server thought web services, using Apache Lucene, as a search engine library, and the Metathesaurus of Unified System of Medical Language (UMLS), as an information source. The server is developed for Spanish speakers. Results show that physicians can find medical terminology code with the terminology server, using friendly or familiar terms, 18 times faster than with the current search process. The user satisfaction degree is ``Good” according to an adjective rating of the System Usability Scale (SUS). In addition, a comparison with a search engine of medical terminology called Metamorphosys shows that the implemented terminology server is quite competitive and it responses in similar average time