Annotation Of Web Pages Research Articles

Using Machine Learning in Web Page Categorization for Search Engine optimization.

This research introduces an innovative approach to classifying websites based on their compliance with SEO standards. By merging expert insights with machine learning algorithms, the study develops classifiers capable of accurately sorting web pages into three categories. These classifiers pinpoint key factors that impact the level of page optimization. The training phase entails experts manually labeling data. Experimental findings underscore the efficacy of machine learning in gauging a web page's adherence to SEO guidelines. This method holds significance as it automates the identification of pages needing optimization to enhance search engine rankings. Moreover, the research sheds light on the optimal arrangement of ranking variables utilized by search engines, reinforcing previous research. Additionally, the establishment of a new dataset comprising manually annotated web pages proves invaluable for future research initiatives. KEYWORDS: Machine learning, on-page optimization, classification, SEO optimization, Search engine optimization.

Pancreatlas: Applying an Adaptable Framework to Map the Human Pancreas in Health and Disease

SummaryHuman tissue phenotyping generates complex spatial information from numerous imaging modalities, yet images typically become static figures for publication, and original data and metadata are rarely available. While comprehensive image maps exist for some organs, most resources have limited support for multiplexed imaging or have non-intuitive user interfaces. Therefore, we built a Pancreatlas resource that integrates several technologies into a unique interface, allowing users to access richly annotated web pages, drill down to individual images, and deeply explore data online. The current version of Pancreatlas contains over 800 unique images acquired by whole-slide scanning, confocal microscopy, and imaging mass cytometry, and is available at https://www.pancreatlas.org. To create this human pancreas-specific biological imaging resource, we developed a React-based web application and Python-based application programming interface, collectively called Flexible Framework for Integrating and Navigating Data (FFIND), which can be adapted beyond Pancreatlas to meet countless imaging or other structured data-management needs.

Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embedding

In this paper, we propose a novel semi-supervised multi-label feature selection algorithm and apply it to three different applications: natural scene classification, web page annotation, and yeast gene functional classification. Compared with the previous works, there are two advantages of our algorithm: (1) Manifold learning which leverages the underlying geometric structure of the training data is imposed to utilize both labeled and unlabeled data. Besides, the underlying manifold structure is guaranteed to be clear by using the l1-norm regularization. (2) Shared subspace learning which has shown its efficiency in multi-label learning scenarios, is also considered in our feature learning algorithm. The proposed objective function involves l2,1-norm and l1-norm, making it non-smooth and difficult to solve. We also design an efficient iterative algorithm to optimize it. Experimental results demonstrate the effectiveness of our algorithm compared with sate-of-the-art algorithms on different tasks.

Filtering Method for the Annotated and Non-Annotated Web Pages

With the great mass of the pages managed through the world, and especially with the advent of the Web, it has become more difficult to find the relevant pages after an interrogation. Furthermore, the manual filtering of the indexed Web pages is a laborious task. A new filtering method of the annotated Web pages (by our semantic annotation process) and the non-annotated Web pages (retrieved from search engine “Google”) is then necessary to group the relevant Web pages for the user. In this paper, the authors will first synthesize their previous work of the semantic annotation of Web pages. Then, they will define a new filtering method based on three activities. The authors will also present their querying and filtering component of Web pages; their purpose is to demonstrate the feasibility of the filtering method. Finally, the authors will present an evaluation of this component, which has proved its performance for multiple domains.

Semantic Annotation for Supporting Context-Aware Information Retrieval in the Transportation Project Environmental Review Domain

AbstractAlthough transportation practitioners nowadays have an unprecedented level of access to information, substantial gaps still exist in their ability to efficiently and reliably find the right information, at the right time, for the task or decision at hand. To address this gap, this paper proposes a context-aware information retrieval (IR) approach that can capture and exploit the conceptualization of user needs, decision context, and content meanings in order 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. It proposes an epistemology-based SA algorithm for automatically annotating Web pages in the TPER domain with contextual concepts from an epistemological model. The TPER epistemology i...

A Mobile Query Service for Integrated Access to Large Numbers of Online Semantic Web Data Sources

From the Semantic Web’s inception, a number of concurrent initiatives have given rise to multiple segments: large semantic datasets, exposed by query endpoints; online Semantic Web documents, in the form of RDF files; and semantically annotated web content (e.g., using RDFa), semantic sources in their own right. In various mobile application scenarios, online semantic data has proven to be useful. While query endpoints are most commonly exploited, they are mainly useful to expose large semantic datasets. Alternatively, mobile RDF stores are utilized to query local semantic data, but this requires the design-time identification & replication of relevant data. Instead, we present a mobile query service that supports on the-fly and integrated querying of semantic data, originating from a largely unused portion of the Semantic Web, comprising online RDF files and semantics embedded in annotated webpages. To that end, our solution performs dynamic identification, retrieval and caching of query-relevant semantic data. We explore several data identification and caching alternatives, and investigate the utility of source metadata in optimizing these tasks. Further, we introduce a novel cache replacement strategy, fine tuned to the described query dataset, and include explicit support for the Open World Assumption. An extensive experimental validation evaluates the query service and its alternative components.

A mobile query service for integrated access to large numbers of online semantic web data sources

From the Semantic Web’s inception, a number of concurrent initiatives have given rise to multiple segments: large semantic datasets, exposed by query endpoints; online Semantic Web documents, in the form of RDF files; and semantically annotated web content (e.g., using RDFa), semantic sources in their own right. In various mobile application scenarios, online semantic data has proven to be useful. While query endpoints are most commonly exploited, they are mainly useful to expose large semantic datasets. Alternatively, mobile RDF stores are utilized to query local semantic data, but this requires the design-time identification and replication of relevant data. Instead, we present a mobile query service that supports on-the-fly and integrated querying of semantic data, originating from a largely unused portion of the Semantic Web, comprising online RDF files and semantics embedded in annotated webpages. To that end, our solution performs dynamic identification, retrieval and caching of query-relevant semantic data. We explore several data identification and caching alternatives, and investigate the utility of source metadata in optimizing these tasks. Further, we introduce a novel cache replacement strategy, fine-tuned to the described query dataset, and include explicit support for the Open World Assumption. An extensive experimental validation evaluates the query service and its alternative components.

The Influence of Documents, Users and Tasks on the Relevance and Comprehension of Health Web Documents

Abstract Search engines typically estimate relevance using features of the documents. We believe that several features from the user and task can also contribute to this process. In the health domain there are specific characteristics of web documents that can also add value to this estimation. In the present work, using a dataset composed by set of annotated web pages and their assessment by a set of users regarding their relevance and comprehension, we analyse what characteristics affect documents’ relevance and what characteristics influence how well users comprehend them. We have conducted a bivariate analysis using characteristics of the above data collection. The strongest relations we have found are linked to the task features, suggesting a direct association between tasks’ clarity and easiness and both the relevance and the comprehension of the content. The language of the document, its medical certification, the update status, the content in pathology definitions, the content in prevention, prognosis and treatment information, are other characteristics valued by consumers in terms of relevance. Users’ previous experience on health searches and, particularly, on the topic being searched, their gender, the language and terminology of their queries were shown to be related to their success in the search tasks. We have also found that lay terminology, knowledge about the medico-scientific terms and the language of the documents are good indicators of comprehension. Documents containing links and testimonies, and the ones recently updated were observed to be better understood by users, as well as blog posts and comments.

Semi-Automatic Online Tagging with K-Medoid Clustering

Online tagging is crucial for the acquisition and organization of web knowledge. We present TYG (Tag-as-You-Go) in this paper, a web browser extension for online tagging of personal knowledge on standard web pages. We investigate an approach to combine a K-Medoid-style clustering algorithm with the user input to achieve semi-automatic web page annotation. The annotation process supports user-defined tagging schema and comprises an automatic mechanism that is built upon clustering techniques, which can automatically group similar HTML DOM nodes into clusters corresponding to the user specification. TYG is a prototype system illustrating the proposed approach. Experiments with TYG show that our approach can achieve both efficiency and effectiveness in real world annotation scenarios.

Automation of the semantic annotation of web resources

The annotation of a web page allows us to associate a semantic to the content of this page. But with the great mass of pages managed through the world, and especially with the advent of the web, their manual annotation is not feasible. In this paper, we will focus on the semiautomatic annotation of the web pages. We will propose an approach and a component (entitled 'Querying Web') for semantic annotation of web pages. The proposal of this component aims at improving the interrogation process in the semantic web environment. Our solution is an enhancement of the first result of annotation done by the 'Semantic Radar' plug-in on the web resources, by new annotations using an enriched domain ontology. Finally, we will present in this paper an evaluation of the automation made by our component.

Faceted product search powered by the Semantic Web

This paper presents a platform for multifaceted product search using Semantic Web technology. Online shops can use a ping service to submit their RDFa annotated Web pages for processing. The platform is able to process these RDFa annotated (X)HTML pages and aggregate product information coming from different Web stores. We propose solutions for the identification of products and the mapping of the categories in this process. Furthermore, when a loose vocabulary such as the Google RDFa vocabulary is used, the platform deals with the issue of heterogeneous information (e.g., currencies, rating scales, etc.).

A reverse engineering approach for automatic annotation of Web pages

The Semantic Web is gaining increasing interest to fulfill the need of sharing, retrieving, and reusing information. Since Web pages are designed to be read by people, not machines, searching and reusing information on the Web is a difficult task without human participation. To this aim adding semantics (i.e meaning) to a Web page would help the machines to understand Web contents and better support the Web search process. One of the latest developments in this field is Google's Rich Snippets, a service for Web site owners to add semantics to their Web pages. In this paper we provide a structured approach to automatically annotate a Web page with Rich Snippets RDFa tags. Exploiting a data reverse engineering method, combined with several heuristics, and a named entity recognition technique, our method is capable of recognizing and annotating a subset of Rich Snippets' vocabulary, i.e., all the attributes of its Review concept, and the names of the Person and Organization concepts. We implemented tools and services and evaluated the accuracy of the approach on real E-commerce Web sites.

A Fuzzy Geospatial Ontology Model and Its Application in Geospatial Semantic Retrieval

Geospatial ontology plays a key role in Geospatial Semantic Retrieval (GSR), which provides support for query expansion and disambiguation, relevance ranking and web page annotation. However, existing ontologies are deficient in representing and reasoning fuzzy geospatial information, an important type of information that distributes widely in user’s queries and web pages. This paper proposes a novel fuzzy geospatial ontology model, FGSO. In FGSO, the complex semantic of fuzzy spatial relations are described explicitly by a fuzzy spatial relation ontology which also carries out fuzzy spatial reasoning; a fuzzy geospatial ontology based on our new ontology language, FR-OWL, is developed to represent a variety of geospatial information besides spatial relations in a uniform format; a converting algorithm is developed to integrate these two ontologies to mine more information. Based on FGSO, a fuzzy geospatial semantic retrieval system, FGSRS, is constructed. Experiment results show that it outperforms keyword-based search tools on both Recall and Precision, and supports more types of fuzzy spatial query than existing GSR systems.

Social trend tracking by time series based social tagging clustering

Social tagging is widely practiced in the Web 2.0 era. Users can annotate useful or interesting Web resources with keywords for future reference. Social tagging also facilitates sharing of Web resources. This study reviews the chronological variation of social tagging data and tracks social trends by clustering tag time series. The data corpus in this study is collected from Hemidemi.com. A tag is represented in a time series form according to its annotating Web pages. Then time series clustering is applied to group tag time series with similar patterns and trends in the same time period. Finally, the similarities between clusters in different time periods are calculated to determine which clusters have similar themes, and the trend variation of a specific tag in different time periods is also analyzed. The evaluation shows the recommendation accuracy of the proposed approach is about 75%. Besides, the case discussion also proves the proposed approach can track the social trends.

Automatic web pages hierarchical classification using dynamic domain ontologies

The use of ontologies for knowledge representation has had a fast increase in the last years and they are used in several application context. One of these challenging applications is the web. Managing large amount of information on internet needs more efficient and effective methods and techniques for mining and representing information. In this article, we present a methodology for automatic topic annotation of web pages. We describe an algorithm for words disambiguation using an apposite metric for measuring the semantic relatedness and we show a technique which allows to detect the topic of the analysed document using ontologies extracted from a knowledge base. The strategy is implemented in a system where these information are used to build a topic hierarchy automatically created and not a priori defined for classifying web pages. Experimental results are presented and discussed in order to measure the effectiveness of our approach.

Reliability and type of consumer health documents on the World Wide Web: an annotation study

BackgroundIn this paper we present a detailed scheme for annotating medical web pages designed for health care consumers. The annotation is along two axes: first, by reliability (the extent to which the medical information on the page can be trusted), second, by the type of page (patient leaflet, commercial, link, medical article, testimonial, or support).ResultsWe analyze inter-rater agreement among three judges for each axis. Inter-rater agreement was moderate (0.77 accuracy, 0.62 F-measure, 0.49 Kappa) on the page reliability axis and good (0.81 accuracy, 0.72 F-measure, 0.73 Kappa) along the page type axis.ConclusionsWe have shown promising results in this study that appropriate classes of pages can be developed and used by human annotators to annotate web pages with reasonable to good agreement.AvailabilityNo.

Ontology based Similarity Measure in Document Ranking

This paper presents a methodology for the ontology based semantic annotation of web pages with annotation weighting scheme that takes advantage of the different relevance of structured document fields. The retrieval model is based on the importance factors of the structural elements, which are used to re-rank the documents retrieval by the ontology based distance measure. The relevance concept similarity are combined with the annotation-weighting scheme to improve the relevance measures. The proposed method has been evaluated on USGS Science directory collection. Preliminary experiments results show that our method may generate relevant document in the top rank.

Facilitating interaction and retrieval for annotated documents

Digital annotation of web pages presents new problems connected to the dynamics and the openness of the web. First, the variety of available browsers may require the use of proprietary solutions, once one goes beyond traditional interaction with hyperlinks. Second, documents are replicated over multiple sites and can be retrieved at different URLs or with different queries. Hence, annotations on a web content must be retrieved even if they were created while accessing the same content under a different URL. Moreover, when pages are modified, annotations related to fragments that have moved within the page itself should be retrieved and shown to the user. We have improved the MADCOW annotation system with a uniform interaction paradigm and incorporated two algorithms: one to assess the identity of two pages under two different URLs and the other to identify variations in two versions of a page under the same URL.

Collaborative management of web ontology data with flexible access control

The creation and management of ontology data on web sites (e.g. instance data that is used to annotate web pages) is important technical support for the growth of the semantic web. This study identifies some key issues for web ontology data management and describes an ontology data management system, called robinet, to perform the management. This paper presents the structure of the system and introduces a Web ontology data management model that enables a flexible access control mechanism. This model adds rules into the robinet system to utilize the semantics of ontology for controlling the access to ontology data. The implementation of the rule-based access control mechanism and related testing are also discussed.

AnnotatEd: A social navigation and annotation service for web-based educational resources

Web page annotation and adaptive navigation support are two active, but independent research directions focused on the same goal: expanding the functionality of the Web as a hypertext system. The goal of the AnnotatEd system presented in this paper has been to integrate annotation and adaptive navigation support into a single value-added service where the components can reinforce each other and create new unique attributes. This paper describes the implementation of AnnotatEd from early prototypes to the current version, which has been explored in several contexts. We summarize some lessons we learned during the development process and which defined the current functionality of the system. We also present the results of several classroom studies of the system. These results demonstrate the importance of the browsing-based information access supported by AnnotatEd and the value of both the annotation and navigation support functionalities offered by the system.