Ranking Query Results Research Articles

On the Complexity of Query Result Diversification

Query result diversification is a bi-criteria optimization problem for ranking query results. Given a database D , a query Q , and a positive integer k , it is to find a set of k tuples from Q ( D ) such that the tuples are as relevant as possible to the query, and at the same time, as diverse as possible to each other. Subsets of Q ( D ) are ranked by an objective function defined in terms of relevance and diversity. Query result diversification has found a variety of applications in databases, information retrieval, and operations research. This article investigates the complexity of result diversification for relational queries. (1) We identify three problems in connection with query result diversification, to determine whether there exists a set of k tuples that is ranked above a bound with respect to relevance and diversity, to assess the rank of a given k -element set, and to count how many k -element sets are ranked above a given bound based on an objective function. (2) We study these problems for a variety of query languages and for the three objective functions proposed in Gollapudi and Sharma [2009]. We establish the upper and lower bounds of these problems, all matching , for both combined complexity and data complexity. (3) We also investigate several special settings of these problems, identifying tractable cases. Moreover, (4) we reinvestigate these problems in the presence of compatibility constraints commonly found in practice, and provide their complexity in all these settings.

Feature identification for topical relevance assessment in feed search engines1

Feed has become a popular way to effectively distribute and acquire information on the web. The explosive growth of feeds demands a search engine that can help users quickly discover feeds of their interests. Retrieval effectiveness of feed search engine highly depends on a relevance assessment method that determines candidates for ranking query results. However, existing relevance assessment approaches proposed for web page retrieval may produce unsatisfactory result due to the different characteristics of feeds from traditional web pages. Compared to web pages, feed is a dynamic document since it continually generates information on some specific topics. In addition, it is a structured document that consists of several data elements such as title and description. Accordingly, the relevance assessment method for feed retrieval needs to effectively address these unique characteristics of feeds. This paper considers a problem of identifying significant features which are a feature set created from feed data elements, with the aim of improving effectiveness of feed retrieval while at the same time reducing computational cost. We conducted extensive experiments to investigate the problem using support vector machine on real-world data sets, and found the significant features that can be employed for feed search services.

On the complexity of query result diversification

Query result diversification is a bi-criteria optimization problem for ranking query results. Given a databaseD, a queryQand a positive integerk, it is to find a set ofktuples fromQ(D)such that the tuples are as relevant as possible to the query, and at the same time, as diverse as possible to each other. Subsets ofQ(D)are ranked by an objective function defined in terms of relevance and diversity. Query result diversification has found a variety of applications in databases, information retrieval and operations research.This paper studies the complexity of result diversification for relational queries. We identify three problems in connection with query result diversification, to determine whether there exists a set ofktuples that is ranked above a bound with respect to relevance and diversity, to assess the rank of a givenk-element set, and to count how manyk-element sets are ranked above a given bound. We study these problems for a variety of query languages and for three objective functions. We establish the upper and lower bounds of these problems, all matching, for both combined complexity and data complexity. We also investigate several special settings of these problems, identifying tractable cases.

Query- And User-Dependent Approach for Ranking Query Results in Web Databases

Internet has paved the way for the emergence of web databases. Querying such databases for required information has become a common task. Ranking such query results is an open problem to be addressed. The existing solutions such as user profiles, query logs, and database values perform ranking in user independent and/or query independent fashion. This can't provide efficient ranking. This paper presents a new approach known as Query and User Dependent Ranking for giving ranking to query results of deep web. The proposed ranking framework is based on two fundamental aspects to the problem of ranking query results. They are query similarity and user similarity. These similarities are exploited to make efficient ranking of query results. A prototype application is built to test the efficiency of our model. The empirical results revealed that our approach is efficient and can be used in real world applications. Index Terms: Deep web, ranking query results, user similarity, query similarity

Automated Ranking of Relaxing Query Results Based on XML Structure and Content Preferences

This paper proposes a contextual preference query method of XML structural relaxation and content scoring to resolve the problem of empty or too many answers returned by XML. This paper proposes a XML contextual preference (XCP) model, where all the possible relaxing queries are determined by the users’ preferences. The XCP model allows users to express their interests on XML tree nodes, and then users assign interest scores to their interesting nodes for providing the best answers. A preference query results ranking method is proposed based on the XCP model, which includes: a Clusters_Merging algorithm to merge clusters based on the similarity of the context states, a Finding_Orders algorithm to find representative orders of the clusters, and a Top-k ranking algorithm to deal with the many answers problem. Results of preliminary user studies demonstrate that the method can provide users with most relevant and ranked query results. The efficiency and effectiveness of the approach are also demonstrated by experimental results.

Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data.

The Resource Description Framework (RDF) format is being used by a large number of scientific applications to store and disseminate their datasets. The provenance information, describing the source or lineage of the datasets, is playing an increasingly significant role in ensuring data quality, computing trust value of the datasets, and ranking query results. Current provenance tracking approaches using the RDF reification vocabulary suffer from a number of known issues, including lack of formal semantics, use of blank nodes, and application-dependent interpretation of reified RDF triples. In this paper, we introduce a new approach called Provenance Context Entity (PaCE) that uses the notion of provenance context to create provenance-aware RDF triples. We also define the formal semantics of PaCE through a simple extension of the existing RDF(S) semantics that ensures compatibility of PaCE with existing Semantic Web tools and implementations. We have implemented the PaCE approach in the Biomedical Knowledge Repository (BKR) project at the US National Library of Medicine. The evaluations demonstrate a minimum of 49% reduction in total number of provenance-specific RDF triples generated using the PaCE approach as compared to RDF reification. In addition, performance for complex queries improves by three orders of magnitude and remains comparable to the RDF reification approach for simpler provenance queries.

Experience versus talent shapes the structure of the Web

We use sequential large-scale crawl data to empirically investigate and validate the dynamics that underlie the evolution of the structure of the web. We find that the overall structure of the web is defined by an intricate interplay between experience or entitlement of the pages (as measured by the number of inbound hyperlinks a page already has), inherent talent or fitness of the pages (as measured by the likelihood that someone visiting the page would give a hyperlink to it), and the continual high rates of birth and death of pages on the web. We find that the web is conservative in judging talent and the overall fitness distribution is exponential, showing low variability. The small variance in talent, however, is enough to lead to experience distributions with high variance: The preferential attachment mechanism amplifies these small biases and leads to heavy-tailed power-law (PL) inbound degree distributions over all pages, as well as over pages that are of the same age. The balancing act between experience and talent on the web allows newly introduced pages with novel and interesting content to grow quickly and surpass older pages. In this regard, it is much like what we observe in high-mobility and meritocratic societies: People with entitlement continue to have access to the best resources, but there is just enough screening for fitness that allows for talented winners to emerge and join the ranks of the leaders. Finally, we show that the fitness estimates have potential practical applications in ranking query results.

XML search

The development of approaches to access XML content has generated a wealth of issues in information retrieval (IR) and database (DB) (e.g., [2, 15, 17, 20, 19, 47, 26, 32, 24]). While the IR community has traditionally focused on searching unstructured content, and has developed various techniques for ranking query results and evaluating their effectiveness, the DB community has focused on developing query languages and efficient evaluation algorithms for highly structured content. Recent trends in DB and IR research demonstrate a growing interest in merging IR and DB techniques for accessing XML content. Support for a combination of "structured" and full-text search for effectively querying XML documents was unanimous in a recent panel at SIGMOD 2005 [3], and is being widely studied in the IR community [20].

Time-Aware Authority Ranking

The link structure of the web is analyzed to measure the authority of pages, which can be taken into account for ranking query results. Due to the enormous dynamics of the web, with millions of pages created, updated, deleted, and linked to every day, temporal aspects of web pages and links are crucial factors for their evaluation. Users are interested in important pages (i.e., pages with high authority score) but are equally interested in the recency of information. Time—and thus the freshness of web content and link structure—emanates as a factor that should be taken into account in link analysis when computing the importance of a page. So far only minor effort has been spent on the integration of temporal aspects into link-analysis techniques. In this paper we introduce T-Rank Light and T-Rank, two link-analysis approaches that take into account the temporal aspects freshness (i.e., timestamps of most recent updates) and activity (i.e., update rates) of pages and links. Experimental results show that T-Rank Light and T-Rank can produce better rankings of web pages.

A comparison of automatic and manual zoning

In this paper, we study the effects of automatic zoning on retrieval and ranking variability. We will show that OCR-generated text from automatic zoning, followed by postprocessing, produces retrieval results equivalent to OCR-generated text from manual zoning. We further show that there is a strong linear association between the ranked query results obtained from these two methods of zoning.

WebQuery: searching and visualizing the Web through connectivity

Finding information located somewhere on the World-Wide Web is an error-prone and frustrating task. The WebQuery system offers a powerful new method for searching the Web based on connectivity and content. We do this by examining links among the nodes returned in a keyword-based query. We then rank the nodes, giving the highest rank to the most highly connected nodes. By doing so, we are finding “hot spots” on the Web that contain information germane to a user's query. WebQuery not only ranks and filters the results of a Web query, it also extends the result set beyond what the search engine retrieves, by finding “interesting” sites that are highly connected to those sites returned by the original query. Even with WebQuery filtering and ranking query results, the result sets can be enormous. So, we need to visualize the returned information. We explore several techniques for visualizing this information—including cone trees, 2D graphs, 3D graphs, lists, and bullseyes-and discuss the criteria for using each of the techniques.