Current Search Engines Research Articles

PENGUKUR PARAMETER SEARCH ENGINE OPTIMIZATION (SEO) SECARA ON PAGE PADA TOKO ONLINE UNTUK MENINGKATKAN PENJUALAN

The current search engine is not only for searching the pages of the website you want to visit but also as the main source of visitors on the internet. With the development of technology several ways that can be used to improve website ranking on search engines, one effective way by utilizing Search Engine Optimization (SEO) can help some websites to get SERP rankings (Search Engine Results Pages) on search engines, by a good SERP ranking will then be determined on the number of visitors on the website. An application is needed to calculate SEO applications on a web site, with the application of Online SEO measurement parameters can help business users of online stores to increase visitors and invite visitors who become buyers. In the Complete SEO Parameter Measurement Application on this page has 6 important parameters in SEO on the page provided, Title Tag, Title Tag, Meta Description, Content, Alt Tag and Keyword Density, with an explanation of each - each parameter where The accuracy of the application results in the calculation can be obtained Kappa value of 0.57..

Wallflower (Erysimum cheiri (L.) Crantz) from Past to Future

Wallflower (Erysimum cheiri (L.) Crantz) is a common medicinal plant in Persian medicine and nowadays some traditional products from wallflower are consumed on global markets. The aim of the present study was to study the phytochemical constituents of wallflower and discuss safety evaluations related to the traditional wallflower preparations. Major Persian scholars (e.g. Avicenna) books, Persian manuscripts (e.g. Makhzan-al-advia) and Arabic medical manuscripts (e.g Alshamel-fi alsanaat altebya) of the medieval Islamic era as well as current search engines including Pubmed, Scopus, Siencedirect, and Google Scholar were included in the study from 1700 up to 2018 A.D. In traditional medicine manuscripts, various topical and oral dosage forms of wallflower were administered in low doses. After renaissance, phytochemical investigations reported cardiac steroids in wallflower and it might be the reason that next medical investigations on the herb have been interrupted. According to in vivo studies, topical indications of cardiac steroids in doses lower than their inhibitory concentration 50 (IC50) should be safe and effective in some cutaneous disorders. Wallflower is reported to have several different classes of compounds including: 11 types of cardenolides (such as strophanthidin, bipindogenin, uzarigenin, cannogenol and digitoxygenin derivatives), two flavonoids, a cyanidin and two glucosinolates. Therefore, for safety guarantee, wallflower products require dose adjustment based on IC50 and probable cardenolide soluble content in that dosage forms.

Multi-Reference Spectral Library Yields Almost Complete Coverage of Heterogeneous LC-MS/MS Data Sets.

Spectral libraries play a central role in the analysis of data-independent-acquisition (DIA) proteomics experiments. A main assumption in current spectral library tools is that a single characteristic intensity pattern (CIP) suffices to describe the fragmentation of a peptide in a particular charge state (peptide charge pair). However, we find that this is often not the case. We carry out a systematic evaluation of spectral variability over public repositories and in-house data sets. We show that spectral variability is widespread and partly occurs under fixed experimental conditions. Using clustering of preprocessed spectra, we derive a limited number of multiple characteristic intensity patterns (MCIPs) for each peptide charge pair, which allow almost complete coverage of our heterogeneous data set without affecting the false discovery rate. We show that a MCIP library derived from public repositories performs in most cases similar to a "custom-made" spectral library, which has been acquired under identical experimental conditions as the query spectra. We apply the MCIP approach to a DIA data set and observe a significant increase in peptide recognition. We propose the MCIP approach as an easy-to-implement addition to current spectral library search engines and as a new way to utilize the data stored in spectral repositories.

A Visualization Technique to Support Searching Filtering

This paper discusses the need for an integrative literature review on Information Visualization for exploratory search particularly in handling data overload. The paper analyses many applications and web sites across disciplines. Certain search engines incorporate visualization to allow for better understanding of the information and at the same time reduce information overload. Current search engines use the query and response (lookup) process. Exploratory search allows for open-ended search. Visual representation is one feature in exploratory search that can be used to improve the overall search. The main contribution of this paper is the review of previous exploratory-search-based works, the utilised features as well as its existing applications, visualizations as the mechanism for developing filters to narrow down the results of searching. Many studies have shown that replacing traditional search engines with exploratory search by using the features of exploratory search can reduce the data overload.

Harnessing Twitter for Answering Opinion List Queries

Opinion list (OL) queries like “valentines day gift ideas” and “best anniversary messages for your parents” are quite popular on web search engines. Users expect instant answers comprising of a list of relevant items (OL) for such a query. Surprisingly, current search engines do not provide any crisp instant answers for queries in this critical query segment. To the best of our knowledge, we present the first system that tackles such queries. Although such social factors are heavily discussed on online social networks like Twitter, extracting such lists from tweets is quite challenging. The challenges lie in discovering such lists from tweets, rank the discovered list items as well as handle lists with very low cardinality (tail OLs). We present an end-to-end system that: 1) identifies these “OLs” from a large number of Twitter hashtags using a classifier trained using novel task-specific features; 2) extracts suitable list answers from relevant tweets using carefully designed regex patterns; 3) uses the learning to rank framework to present a ranked list of these items; and 4) handles tail lists using a novel algorithm to borrow list items from similar lists. Crowd-sourced evaluation shows that the proposed system can extract OLs with a good accuracy.

A novel product recommendation model consolidating price, trust and online reviews

PurposeThe purpose of this paper is to propose a model for product recommendation to improve the accuracy of recommendation based on the current search engines used in e-commerce platforms like Tmall.com.Design/methodology/approachFirst, the proposed model comprehensively considers price, trust and online reviews, which all represent critical factors in consumers’ purchasing decisions. Second, the model introduces the quantization methods for these criteria incorporating fuzzy theory. Third, the model uses a distance measure between two single valued neutrosophic sets based on the prioritized average operator to consolidate the influences of positive, neutral and negative comments. Finally, the model uses multi-criteria decision-making methods to integrate the influences of price, trust and online reviews on purchasing decisions to generate recommendations.FindingsTo demonstrate the feasibility and efficiency of the proposed model, a case study is conducted based on Tmall.com. The results of case study indicate that the recommendations of our model perform better than those of current search engines of Tmall.com. The proposed model can significantly improve the accuracy of product recommendations based on search engines.Originality/valueThe product recommendation method can meet the critical challenge from the search engines on e-commerce platforms. In addition, the proposed method could be used in practice to develop a new application for e-commerce platforms.

Mixed language queries in online searches

PurposeWith the increasing number of online multilingual resources, cross-language information retrieval (CLIR) has drawn much attention from the information retrieval (IR) research community. However, few studies have examined how and why multilingual searchers seek information in two or more languages, specifically how they switch and mix language in queries to get satisfying results. The purpose of this paper is to focus on Chinese–English bilinguals’ intra-sentential code-switching behaviors in online searches. The scenarios and reasons of code-switching, factors that may affect code-switching, the patterns of mixed language query formulation and reformulation and how current IR systems and other search tools can facilitate such information needs were examined.Design/methodology/approachIn-depth semi-structured interviews were used as the research method. In total, 30 participants were recruited based on their English proficiency, location and profession, using a purposive sampling method.FindingsFour scenarios and four reasons for using Chinese–English mixed language queries to cover information needs were identified, and results suggest that linguistic and cultural/social factors are of equivalent importance in code-switching behaviors. English terms and Chinese terms in queries play different roles in searches, and mixed language queries are irreplaceable by either single language queries or other search facilitating features. Findings also suggest current search engines and tools need greater emphasis in the user interface and more user education is required.Originality/valueThis study presents a qualitative analysis of bilinguals’ code-switching behaviors in online searches. Findings are expected to advance the theoretical understanding of bilingual users’ search strategies and interactions with IR systems, and provide insights for designing more effective IR systems and tools to discover multilingual online resources, including cross-language controlled vocabularies, personalized CLIR tools and mixed language query assistants.

Search engine optimization: Experience of State Public Library for Science and Technology of the Russian Academy of Sciences Siberian Branch

The author examines the methods of search engine optimization applicable to library www-sites. The experience of the RAS SB State Public Library for Science and Technology in internal SEO technology implementation to increase the relevance of the resources is discussed. The main focus was made on writing texts for the Library’s www-site (in particular, “The Procedure for presenting information to be loaded to RAS SB SPLS&T’s www-site”). As a result of current search engine optimization, the errors were revealed and eliminated. The author points to the fact that after the optimization process was launched, the steady increase in search engine use has been observed. This evidences that promotion through search engine means cannot bring immediate effect and is rather long-term oriented. In building a new version of the RAS SB SPLS&T www-site, the optimization is scheduled for the design and development stage. It will be based on the accumulated knowledge and experience and is aimed at avoiding inefficient ranging by search engines. The author proves that SEO technologies can be applied to Russian library www-sites to promote library resources and to attract users. The author also concludes that SEO technologies increase www-sites rating and number of their users.

Semantic annotation of consumer health questions

BackgroundConsumers increasingly use online resources for their health information needs. While current search engines can address these needs to some extent, they generally do not take into account that most health information needs are complex and can only fully be expressed in natural language. Consumer health question answering (QA) systems aim to fill this gap. A major challenge in developing consumer health QA systems is extracting relevant semantic content from the natural language questions (question understanding). To develop effective question understanding tools, question corpora semantically annotated for relevant question elements are needed. In this paper, we present a two-part consumer health question corpus annotated with several semantic categories: named entities, question triggers/types, question frames, and question topic. The first part (CHQA-email) consists of relatively long email requests received by the U.S. National Library of Medicine (NLM) customer service, while the second part (CHQA-web) consists of shorter questions posed to MedlinePlus search engine as queries. Each question has been annotated by two annotators. The annotation methodology is largely the same between the two parts of the corpus; however, we also explain and justify the differences between them. Additionally, we provide information about corpus characteristics, inter-annotator agreement, and our attempts to measure annotation confidence in the absence of adjudication of annotations.ResultsThe resulting corpus consists of 2614 questions (CHQA-email: 1740, CHQA-web: 874). Problems are the most frequent named entities, while treatment and general information questions are the most common question types. Inter-annotator agreement was generally modest: question types and topics yielded highest agreement, while the agreement for more complex frame annotations was lower. Agreement in CHQA-web was consistently higher than that in CHQA-email. Pairwise inter-annotator agreement proved most useful in estimating annotation confidence.ConclusionsTo our knowledge, our corpus is the first focusing on annotation of uncurated consumer health questions. It is currently used to develop machine learning-based methods for question understanding. We make the corpus publicly available to stimulate further research on consumer health QA.

CRAWLING AJAX-BASED WEB APPLICATIONS: EVOLUTION AND STATE-OF-THE-ART

The innovation of AJAX resulted in more responsive, interactive and faster web applications due to the clever amalgamation of JavaScript, HTML, and Cascading Style Sheets (CSS). However, from the user’s perspective, this achievement places many challenges before web search engines. One major challenge is due to the complexities in crawling such web applications because multiple states are associated with one uniform resource locator (URL) that cause a mismatch with search model of web search engines, where a web document is uniquely identified by a single unique URL with a single state. Crawling AJAX-based web applications means giving strength and capability to web search engines so that information produced in these highly-interactive web applications is downloaded and indexed. The need here is to investigate the technicalities of AJAX that shatter the metaphor of a web page which the current web search engine utilize during crawling in order to improve the capabilities of web search engines. Although some academic tools have been developed, they produce some false positives which greatly affect the performance of web search engine. We aim to investigate AJAX and AJAX-based web applications as well as the state-of-the-art in crawling these applications along with some prominent issues, challenges and recommendations

A Personalized Group-Based Recommendation Approach for Web Search in E-Learning

The unprecedented growth of the Internet, its pervasive accessibility, and ease of use have increased students’ dependencies on the Web for quick search and retrieval of learning resources. However, current search engines tend to rely on the correct keywords. This excludes other characteristics, such as the individual’s learning capability and readiness for specific learning materials. As a result, the same set of search-keywords delivers the same search results. This situation hinders the optimization of the Web search engines in supporting the heterogeneity of its users in their learning endeavors. This paper aims to address the issue. It attempts to augment Web search engines with personalized recommendations of search results which match students’ learning competencies and behaviors. The results drawn from our experiments suggest that our novel approach can provide a notable improvement in terms of performance and satisfaction for the students.

Analysis of character network heat based on web retrieval technology

Information technology is now developing rapidly, the Internet has also obtained widespread popularization. The amount of information on the network is increasing exponentially, whose information sources are widely distributed and varied. If the information can’t be managed in an orderly manner, it will be difficult for the user to extract the information they need from such a massive amount of information. Although the current search engine give people a lot of convenience in searching for information, but the search engine can’t reflect the user’s personalized information demand with facing a wide variety of users with different information needs, knowledge background and interest. In this paper, a recommendation method based on search results with java as a technology is implemented. This method takes Baidu hot people ranking as an example for verifying.

Survey and evaluation of web search engine hit counts as research tools in computational linguistics

In recent years, many studies on computational linguistics have employed the Web as source for research. Specifically, the distribution of textual data in the Web is used to drive linguistic analyses in tasks such as information extraction, knowledge acquisition or natural language processing. For these purposes, commercial Web search engines are commonly used as the low-entry-cost way to access Web data and, more specifically, to estimate the distribution of the entity(ies) of interest from the hit count the search engines provide when querying such entities. Even though several studies have evaluated the effectiveness of Web search engines as information retrieval tools from the perspective of the end users, few authors have assessed the suitability of hit counts as research tools in computational linguistics; moreover, studies so far have focused on the most well-known search engines (typically Google, Bing and Yahoo!), and neglected potentially interesting alternatives that have recently surfaced. To fill this gap, in this work, we first compile and survey the general-purpose search engines that are currently available. Then, we evaluate the suitability of the hit counts they provide under several perspectives that are relevant for computational linguistics: flexibility of the query language, linguistic coherence, mathematical coherence and temporal consistency. The results of our survey show that, even though the choice of a particular search engine has been generally ignored by researchers relying on Web data, there are significant quality differences between the hit counts of current search engines, and that the most well-known and widely-used search engines do not provide the best results. In this respect, we also identify the search engines whose hit counts are best suited for research.

Deep vs. Shallow Learning-based Filters of MSMS Spectra in Support of Protein Search Engines.

Despite the linear relation between the number of observed spectra and the searching time, the current protein search engines, even the parallel versions, could take several hours to search a large amount of MSMS spectra, which can be generated in a short time. After a laborious searching process, some (and at times, majority) of the observed spectra are labeled as non-identifiable. We evaluate the role of machine learning in building an efficient MSMS filter to remove non-identifiable spectra. We compare and evaluate the deep learning algorithm using 9 shallow learning algorithms with different configurations. Using 10 different datasets generated from two different search engines, different instruments, different sizes and from different species, we experimentally show that deep learning models are powerful in filtering MSMS spectra. We also show that our simple features list is significant where other shallow learning algorithms showed encouraging results in filtering the MSMS spectra. Our deep learning model can exclude around 50% of the non-identifiable spectra while losing, on average, only 9% of the identifiable ones. As for shallow learning, algorithms of: Random Forest, Support Vector Machine and Neural Networks showed encouraging results, eliminating, on average, 70% of the non-identifiable spectra while losing around 25% of the identifiable ones. The deep learning algorithm may be especially more useful in instances where the protein(s) of interest are in lower cellular or tissue concentration, while the other algorithms may be more useful for concentrated or more highly expressed proteins.

Sistemas de recuperação da informação e o conceito de relevância nos mecanismos de busca: semântica e significação

As concepções dos Sistemas de Recuperação da Informação são retomadas para contextualizar tanto os atuais mecanismos de busca, considerados Sistemas Digitais ou de Significação, quanto a questão da relevância. A partir de uma pesquisa documental, de uma revisão de literatura e da compreensão dos resultados de uma interface de busca, discute-se, a partir do algoritmo de busca e do Knowledge Graph do Google, a correlação entre otimização semântica e a relevância. A ligação entre a noção de Semântica com o conceito de relevância, nos mecanismos de busca, pode ser estabelecida entre a relação do significado e a informação e, dessa maneira, a relevância é uma propriedade da informação.

SERNOTATE: An automated approach for business service description annotation for efficient service retrieval and composition

SummaryBusiness service advertisements are today published online to convey essential information about services to customers. However, current Web search engines are unable to search and combine online service advertisements. Semantic service annotation is important for its ability to enable machines to understand the meaning of services and support in effective service retrieval and service composition. Existing research in the area of semantic service annotation has focused on the annotation of Web services in a semi‐automated approach. It cannot be applied to business service information as it is not in the form of Web Services Description Language but in free text format. Moreover, semi‐automated approaches are inappropriate for annotating a large amount of online service information which changes dynamically and they are therefore not suitable for the timely dissemination of service information to customers. To solve these issues, we propose SERNOTATE, which is an automated approach for business service description annotation for efficient service retrieval and composition. We propose new semantic‐based linking approaches, namely, Extended Case‐based Reasoning, vector‐based, and classification‐based, that automatically annotate business services to relevant service concepts. Each approach assists in the single‐label and multi‐label annotation of service terms to concept terms to provide a better representation of services. The experimental results test and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real‐world dataset.

Identifying top relevant dates for implicit time sensitive queries

Despite a clear improvement of search and retrieval temporal applications, current search engines are still mostly unaware of the temporal dimension. Indeed, in most cases, systems are limited to offering the user the chance to restrict the search to a particular time period or to simply rely on an explicitly specified time span. If the user is not explicit in his/her search intents (e.g., “philip seymour hoffman”) search engines may likely fail to present an overall historic perspective of the topic. In most such cases, they are limited to retrieving the most recent results. One possible solution to this shortcoming is to understand the different time periods of the query. In this context, most state-of-the-art methodologies consider any occurrence of temporal expressions in web documents and other web data as equally relevant to an implicit time sensitive query. To approach this problem in a more adequate manner, we propose in this paper the detection of relevant temporal expressions to the query. Unlike previous metadata and query log-based approaches, we show how to achieve this goal based on information extracted from document content. However, instead of simply focusing on the detection of the most obvious date we are also interested in retrieving the set of dates that are relevant to the query. Towards this goal, we define a general similarity measure that makes use of co-occurrences of words and years based on corpus statistics and a classification methodology that is able to identify the set of top relevant dates for a given implicit time sensitive query, while filtering out the non-relevant ones. Through extensive experimental evaluation, we mean to demonstrate that our approach offers promising results in the field of temporal information retrieval (T-IR), as demonstrated by the experiments conducted over several baselines on web corpora collections.

PRIOR ONTOLOGY SELECTION AND QUERY TRANSLATION FOR INFORMATION SEARCH

Objective: Most of the current search engines follow informal keyword based search. Finding the user intention and improving the relevancy of results are the major issues faced by the current traditional keyword based search. Targeting to solve the problems of traditional search and to boost the retrieval process, a framework for semantic based information retrieval is planned. Methods: Social and wine ontologies are used to find the user intention and retrieving it. User’s natural language queries are translated into SPARQL (SPARQL Protocol and Resource Description Framework query language) query for finding related items from those ontologies.Results: The proposed method makes a significant improvement over traditional search in terms of some searches required for searching a particular number of pages using performance graph.Conclusion: Semantic based search can understand the user intention and gives better results than traditional search.

Poe in Cyberspace: The “Mobile-Friendly” Sites

Poe in Cyberspace: The “Mobile-Friendly” Sites

PREFCA: A portal retrieval engine based on formal concept analysis

The web is a network of linked sites whereby each site either forms a physical portal or a standalone page. In the former case, the portal presents an access point to its embedded web pages that coherently present a specific topic. In the latter case, there are millions of standalone web pages, that are scattered throughout the web, having the same topic and could be conceptually linked together to form virtual portals. Search engines have been developed to help users in reaching the adequate pages in an efficient and effective manner. All the known current search engine techniques rely on the web page as the basic atomic search unit. They ignore the conceptual links, that reveal the implicit web related meanings, among the retrieved pages. However, building a semantic model for the whole portal may contain more semantic information than a model of scattered individual pages. In addition, user queries can be poor and contain imprecise terms that do not reflect the real user intention. Consequently, retrieving the standalone individual pages that are directly related to the query may not satisfy the user’s need. In this paper, we propose PREFCA, a Portal Retrieval Engine based on Formal Concept Analysis that relies on the portal as the main search unit. PREFCA consists of three phases: First, the information extraction phase that is concerned with extracting portal’s semantic data. Second, the formal concept analysis phase that utilizes formal concept analysis to discover the conceptual links among portal and attributes. Finally, the information retrieval phase where we propose a portal ranking method to retrieve ranked pairs of portals and embedded pages. Additionally, we apply the network analysis rules to output some portal characteristics. We evaluated PREFCA using two data sets, namely the Forum for Information Retrieval Evaluation 2010 and ClueWeb09 (category B) test data, for physical and virtual portals respectively. PREFCA proves higher F-measure accuracy, better Mean Average Precision ranking and comparable network analysis and efficiency results than other search engine approaches, namely Term Frequency Inverse Document Frequency (TF-IDF), Latent Semantic Analysis (LSA), and BM25 techniques. As well, it gains high Mean Average Precision in comparison with learning to rank techniques. Moreover, PREFCA also gains better reach time than Carrot as a well-known topic-based search engine.