Semantic Ranking Research Articles

A New Technique for Ranking Web Pages and Adwords

Web mining is an active research area which mainly deals with the application on data mining techniques on the data that is provided by the internet, the World Wide Web(WWW).The Information provided by the internet could be in either webpages, links structure of WWW or Web server logs. Web content mining, Web usage mining and Web structure mining are the three categories by which web mining is classified. This paper proposes a technique by which the search results can be refined in such a way that the results provided to the user are unique and the best suited result. This is achieved by using a new technique known as the Semantic Rank (SR) algorithm. The SR algorithm ranks the webpages in a more efficient way than the PageRank algorithm used by Google. Key terms Web Mining, Page rank, Search engine, Semantic rank.

A topic-specific crawling strategy based on semantics similarity

With the Internet growing exponentially, search engines are encountering unprecedented challenges. A focused search engine selectively seeks out web pages that are relevant to user topics. Determining the best strategy to utilize a focused search is a crucial and popular research topic. At present, the rank values of unvisited web pages are computed by considering the hyperlinks (as in the PageRank algorithm), a Vector Space Model and a combination of them, and not by considering the semantic relations between the user topic and unvisited web pages. In this paper, we propose a concept context graph to store the knowledge context based on the user's history of clicked web pages and to guide a focused crawler for the next crawling. The concept context graph provides a novel semantic ranking to guide the web crawler in order to retrieve highly relevant web pages on the user's topic. By computing the concept distance and concept similarity among the concepts of the concept context graph and by matching unvisited web pages with the concept context graph, we compute the rank values of the unvisited web pages to pick out the relevant hyperlinks. Additionally, we constitute the focused crawling system, and we retrieve the precision, recall, average harvest rate, and F-measure of our proposed approach, using Breadth First, Cosine Similarity, the Link Context Graph and the Relevancy Context Graph. The results show that our proposed method outperforms other methods.

Genetic Optimization for Associative Semantic Ranking Models of Satellite Images by Land Cover

Associative methods for content-based image ranking by semantics are attractive due to the similarity of generated models to human models of understanding. Although they tend to return results that are better understood by image analysts, the induction of these models is difficult to build due to factors that affect training complexity, such as coexistence of visual patterns in same images, over-fitting or under-fitting and semantic representation differences among image analysts. This article proposes a methodology to reduce the complexity of ranking satellite images for associative methods. Our approach employs genetic operations to provide faster and more accurate models for ranking by semantic using low level features. The added accuracy is provided by a reduction in the likelihood to reach local minima or to overfit. The experiments show that, using genetic optimization, associative methods perform better or at similar levels as state-of-the-art ensemble methods for ranking. The mean average precision (MAP) of ranking by semantic was improved by 14% over similar associative methods that use other optimization techniques while maintaining smaller size for each semantic model.

Top-k best probability queries and semantics ranking properties on probabilistic databases

There has been much interest in answering top-k queries on probabilistic data in various applications such as market analysis, personalized services, and decision making. In probabilistic relational databases, the most common problem in answering top-k queries (ranking queries) is selecting the top-k result based on scores and top-k probabilities. In this paper, we firstly propose novel answers to top-k best probability queries by selecting the probabilistic tuples which have not only the best top-k scores but also the best top-k probabilities. An efficient algorithm for top-k best probability queries is introduced without requiring users to define a threshold. The top-k best probability approach is more efficient and effective than the probability threshold approach (PT-k) [1,2]. Second, we add the “k-best ranking score” into the set of semantic properties for ranking queries on uncertain data proposed by [3,4]. Then, our proposed method is analyzed, which meets the semantic ranking properties on uncertain data. In addition, it proves that the answers to the top-k best probability queries overcome drawbacks of previous definitions of the top-k queries on probabilistic data in terms of semantic ranking properties. Lastly, we conduct an extensive experimental study verifying the effectiveness of answers to the top-k best probability queries compared to PT-k queries on uncertain data and the efficiency of our algorithm against the state-of-the-art execution of the PT-k algorithm using both real and synthetic data sets.

Candidate Link Generation Using Semantic Phermone Swarm

Requirements tracing of natural Language artifacts consists of document parsing, Candidate Link Generation, evaluation and analysis. Candidate Link Generation deals with checking if the high -level artifact has been fulfilled by the low-level artifact. Requirements traceability is an important activity undertaken as part of ensuring the quality of software in the early stages of the Software Development Life Cycle (SDLC). The Semantic Relatedness between the terms is not considered in the existing system; hence the Candidate Link Generation is not effective. In the proposed system, a hybrid technique combining both the Semantic Ranking and Pheromone Swarm is implemented. Simple swarm agents are given freedom to operate on their own, determining the search path randomly based on the e nvironment. Pheromone swarm agent decides on what term to select or what path to take is influenced by presence of pheromone markings on the inspected object. Semantic Graph is constructed using semantic relatedness between two terms, computed based on highest value path connecting any pair of the terms.The performance is evaluated with Simple, Pheromone and Semantic Pheromone Swarm techniques. The Semantic Pheromone Swarm provides better results when compared to Simple and Pheromone Swarm Techniques.

Semantic ranking of web pages based on formal concept analysis

A web crawler is an important research component in a search engine. In this paper, a new method for measuring the similarity of formal concept analysis (FCA) concepts and a new notion of a web page's rank are proposed that use an information content approach based on users’ web logs. First, an extension similarity and an intension similarity that analyze a user's browsing pattern and their hyperlinks are proposed. Second, the information content similarity between two nouns is computed automatically by examining their ISA and Part-Of hierarchy and using a user's web log. A method for computing the semantic similarity between two concepts in two different concept lattices (the base concept lattice and the current concept lattice) and finding the semantic ranking of web pages is proposed. Last, our experiment demonstrates that our crawler is more suitable for crawling focused web pages. It proves that the semantic ranking of web pages is useful and efficient for making a web crawler's choice of a web page for continuing work.

Semantic Searching and Ranking of Documents using Hybrid Learning System and WordNet

Semantic searching seeks to improve search accuracy of the search engine by understanding searcher’s intent and the contextual meaning of the terms present in the query to retrieve more relevant results. To find out the semantic similarity between the query terms, WordNet is used as the underlying reference database. Various approaches of Learning to Rank are compared. A new hybrid learning system is introduced which combines learning using Neural Network and Support Vector Machine. As the size of the training set highly affects the performance of the Neural Network, we have used Support Vector Machine to reduce the size of the data set by extracting support vectors that are critical for the learning. The data set containing support vectors is then used for learning a ranking function using Neural Network. The proposed system is compared with RankNet. The experimental results demonstrated very promising performance improvements. For experiments, we have used English-Hindi parallel corpus, Gyannidhi from CDAC. F-measure and Average Interpolated Precision are used for evaluation.

Semantic relevance ranking for XML keyword search

Keyword search is a user-friendly mechanism used to retrieve XML data for web and scientific applications. Unlike text data, XML data contain rich semantics, which are obviously useful for information retrieval. It is observed that most existing approaches for XML keyword search either do not consider relevance ranking or perform relevance ranking using traditional text IR techniques. Based on an in-depth analysis of user information need and XML structural semantics, we propose to rank the relevance between a keyword query and an XML fragment by their semantic similarity. We first present a formula to quantify the concept of semantic similarity and then introduce a novel semantic ranking scheme for XML keyword search. Our extensive experiments demonstrate that the proposed scheme outperforms existing approaches in terms of search quality and achieve high efficiency and scalability.

Gaussian mixture models for semantic ranking in domain specific databases with application in radiology

Abstract With recent advances in imaging techniques, huge quantities of domain-specific images, such as medical or geospatial images, are produced and stored daily in computer-based image repositories. Size of databases and limited time at hand makes manual evaluation and annotation by domain experts difficult. In such cases computer based methods can be used to enrich the process of decision making while eliciting previously unknown information. For example, in the medical domain, query by image methods can be used by medical experts for differential diagnosis by displaying previously evaluated cases that contain similar visual patterns. Also, less experienced practitioners can benefit from query-by-semantic methods in training processes, especially for difficult-to-interpret cases with multiple pathologies. In this article we develop a methodology for ranking medical images using customized mixture models. The regions of interest are determined using Dirichlet process to determine natural groupings of images in a content-based feature space. These natural groupings of images are then evaluated for relevance to mixtures of associative semantic mappings. We evaluate and compare the performance of our method on two medical datasets using mean average precision and precision-recall charts.

Semantic ranking of lecture slides based on conceptual relationship and presentational structure

Abstract We describe a presentation content retrieval method involving the semantic ranking of target slides based on the relations between slides related to a user query. This method uses a keyword conceptual structure of the conceptual relationship implicitly existing between keywords extracted from the slide text of the presentation content and the presentational structure of indents in the slide text. At present, many presentation files are shared over the Web by many universities through their own public sites. Although these files are useful and valuable to many potential students, the fact that such files have to be retrieved for selflearning purposes means that there is still a lack of support for self-learners to find the desired slides on a priority basis, i.e, on the basis of importance and urgency of requirement of the desired file. Our noble semantic ranking method helps a user to easily learn through slides, focusing on either highly detailed slides or introductory slides in an order related to the user query. We also present a prototype system supported by our method for slide ranking and evaluate its effectiveness through experiments.

XSemantic: An Extension of LCA Based XML Semantic Search

One of the most convenient ways to query XML data is a keyword search because it does not require any knowledge of XML structure or learning a new user interface. However, the keyword search is ambiguous. The users may use different terms to search for the same information. Furthermore, it is difficult for a system to decide which node is likely to be chosen as a return node and how much information should be included in the result. To address these challenges, we propose an XML semantic search based on keywords called XSemantic. On the one hand, we give three definitions to complete in terms of semantics. Firstly, the semantic term expansion, our system is robust from the ambiguous keywords by using the domain ontology. Secondly, to return semantic meaningful answers, we automatically infer the return information from the user queries and take advantage of the shortest path to return meaningful connections between keywords. Thirdly, we present the semantic ranking that reflects the degree of similarity as well as the semantic relationship so that the search results with the higher relevance are presented to the users first. On the other hand, in the LCA and the proximity search approaches, we investigated the problem of information included in the search results. Therefore, we introduce the notion of the Lowest Common Element Ancestor (LCEA) and define our simple rule without any requirement on the schema information such as the DTD or XML Schema. The first experiment indicated that XSemantic not only properly infers the return information but also generates compact meaningful results. Additionally, the benefits of our proposed semantics are demonstrated by the second experiment.

A Top-K Keyword Search for Supporting Semantics in Relational Databases

为了增强关系数据库中的关键字搜索查询结果,考虑了多表之间以及元组之间的语义关系,提出了一种语义评分函数.该语义评分函数不仅涵盖了当前的评分思想,并且加入新指标来衡量查询结果与查询关键字之间的相关性.基于该评分函数,提出两种以数据块为处理单位的Top-K搜索算法,分别为BA(blocking algorithm)算法和EBA(early-stopping blocking algorithm)算法.EBA在BA基础上引入了过滤域值,以便尽早终止算法的迭代次数.最后实验结果显示语义评分函数保证了;In order to enhance the search results of keyword search in relational databases, semantic relationship among relations and tuples is employed and a semantic ranking function is proposed. In addition to considering current ranking principles, the proposed semantic ranking function provides new metrics to measure query relevance. Based on it, two Top-k search algorithms BA (blocking algorithm) and EBA (early-stopping blocking algorithm) are presented. EBA improves BA by providing a filtering threshold to terminate iterations as early as possible. Finally, experimental results show the semantic ranking function guarantees a search result with high precision and recall, and the proposed BA and EBA algorithms improve query performance of existing approaches.

Semantic content ranking through collaborative and content clustering

COSMOS-7 models semantic content in MPEG-7 such as objects and events and their spatio-temporality. When a user queries a COSMOS-7 model, the output is usually presented as a sequence of relevant, albeit unranked, video segments through which the user must sift. In this paper, we report how we use Self-Organising Neural Networks (SONNs) to cluster and rank the video segments through consideration of user preferences and knowledge gained from usage of the same content by similar users and of similar content by the same user.

On the interaction of structural and semantic case

We consider two variants of morphological case: structural case (such as accusative, ergative, or dative), which is encoded by abstract case features reflecting the semantic ranking of arguments, and semantic case (such as instrumental or directional), which encodes an additional semantic relation to be licensed by the meaning of the verb. Individual verbs may be lexically marked for either structural or semantic case. We show how a correspondence-theoretic approach can successfully describe the various patterns of structural case found in German. We then discuss instances of double object where structural case and semantic case compete with each other: under certain circumstances semantic case is favored, while under other circumstances structural case is favored. We mainly consider two scenarios, described by the respective rankings of S tructural C ase (which requires all objects to be realized by a structural case) and U niqueness (which restricts a structural case to occur at most once in a clause). In particular, we analyze the different options for structural vs. semantic case in the dialects of Quechua.