Use of Wikipedia Categories in Entity Ranking

Wikipedia is a useful source of knowledge that has many applications in language processing and knowledge representation. The Wikipedia category graph can be compared with the class hierarchy in an ontology; it has some characteristics in common as well as some differences. In this paper, we present our approach for answering entity ranking queries from the Wikipedia. In particular, we explore how to make use of Wikipedia categories to improve entity ranking effectiveness. Our experiments show that using categories of example entities works significantly better than using loosely defined target categories

Enhancing Content-And-Structure Information Retrieval using a Native XML Database

Three approaches to content-and-structure XML retrieval are analysed in this paper: first by using Zettair, a full-text information retrieval system; second by using eXist, a native XML database, and third by using a hybrid XML retrieval system that uses eXist to produce the final answers from likely relevant articles retrieved by Zettair. INEX 2003 content-and-structure topics can be classified in two categories: the first retrieving full articles as final answers, and the second retrieving more specific elements within articles as final answers. We show that for both topic categories our initial hybrid system improves the retrieval effectiveness of a native XML database. For ranking the final answer elements, we propose and evaluate a novel retrieval model that utilises the structural relationships between the answer elements of a native XML database and retrieves Coherent Retrieval Elements. The final results of our experiments show that when the XML retrieval task focusses on highly relevant elements our hybrid XML retrieval system with the Coherent Retrieval Elements module is 1.8 times more effective than Zettair and 3 times more effective than eXist, and yields an effective content-and-structure XML retrieval

Users and Assessors in the Context of INEX: Are Relevance Dimensions Relevant?

The main aspects of XML retrieval are identified by analysing and comparing the following two behaviours: the behaviour of the assessor when judging the relevance of returned document components; and the behaviour of users when interacting with components of XML documents. We argue that the two INEX relevance dimensions, Exhaustivity and Specificity, are not orthogonal dimensions; indeed, an empirical analysis of each dimension reveals that the grades of the two dimensions are correlated to each other. By analysing the level of agreement between the assessor and the users, we aim at identifying the best units of retrieval. The results of our analysis show that the highest level of agreement is on highly relevant and on non-relevant document components, suggesting that only the end points of the INEX 10-point relevance scale are perceived in the same way by both the assessor and the users. We propose a new definition of relevance for XML retrieval and argue that its corresponding relevance scale would be a better choice for INEX

Hybrid XML Retrieval: Combining Information Retrieval and a Native XML Database

This paper investigates the impact of three approaches to XML retrieval: using Zettair, a full-text information retrieval system; using eXist, a native XML database; and using a hybrid system that takes full article answers from Zettair and uses eXist to extract elements from those articles. For the content-only topics, we undertake a preliminary analysis of the INEX 2003 relevance assessments in order to identify the types of highly relevant document components. Further analysis identifies two complementary sub-cases of relevance assessments ("General" and "Specific") and two categories of topics ("Broad" and "Narrow"). We develop a novel retrieval module that for a content-only topic utilises the information from the resulting answer list of a native XML database and dynamically determines the preferable units of retrieval, which we call "Coherent Retrieval Elements". The results of our experiments show that -- when each of the three systems is evaluated against different retrieval scenarios (such as different cases of relevance assessments, different topic categories and different choices of evaluation metrics) -- the XML retrieval systems exhibit varying behaviour and the best performance can be reached for different values of the retrieval parameters. In the case of INEX 2003 relevance assessments for the content-only topics, our newly developed hybrid XML retrieval system is substantially more effective than either Zettair or eXist, and yields a robust and a very effective XML retrieval.Comment: Postprint version. The editor version can be accessed through the DO

Evaluation of effective XML information retrieval

XML is being adopted as a common storage format in scientific data repositories, digital libraries, and on the World Wide Web. Accordingly, there is a need for content-oriented XML retrieval systems that can efficiently and effectively store, search and retrieve information from XML document collections. Unlike traditional information retrieval systems where whole documents are usually indexed and retrieved as information units, XML retrieval systems typically index and retrieve document components of varying granularity. To evaluate the effectiveness of such systems, test collections where relevance assessments are provided according to an XML-specific definition of relevance are necessary. Such test collections have been built during four rounds of the INitiative for the Evaluation of XML Retrieval (INEX). There are many different approaches to XML retrieval; most approaches either extend full-text information retrieval systems to handle XML retrieval, or use database technologies that incorporate existing XML standards to handle both XML presentation and retrieval. We present a hybrid approach to XML retrieval that combines text information retrieval features with XML-specific features found in a native XML database. Results from our experiments on the INEX 2003 and 2004 test collections demonstrate the usefulness of applying our hybrid approach to different XML retrieval tasks. A realistic definition of relevance is necessary for meaningful comparison of alternative XML retrieval approaches. The three relevance definitions used by INEX since 2002 comprise two relevance dimensions, each based on topical relevance. We perform an extensive analysis of the two INEX 2004 and 2005 relevance definitions, and show that assessors and users find them difficult to understand. We propose a new definition of relevance for XML retrieval, and demonstrate that a relevance scale based on this definition is useful for XML retrieval experiments. Finding the appropriate approach to evaluate XML retrieval effectiveness is the subject of ongoing debate within the XML information retrieval research community. We present an overview of the evaluation methodologies implemented in the current INEX metrics, which reveals that the metrics follow different assumptions and measure different XML retrieval behaviours. We propose a new evaluation metric for XML retrieval and conduct an extensive analysis of the retrieval performance of simulated runs to show what is measured. We compare the evaluation behaviour obtained with the new metric to the behaviours obtained with two of the official INEX 2005 metrics, and demonstrate that the new metric can be used to reliably evaluate XML retrieval effectiveness. To analyse the effectiveness of XML retrieval in different application scenarios, we use evaluation measures in our new metric to investigate the behaviour of XML retrieval approaches under the following two scenarios: the ad-hoc retrieval scenario, exploring the activities carried out as part of the INEX 2005 Ad-hoc track; and the multimedia retrieval scenario, exploring the activities carried out as part of the INEX 2005 Multimedia track. For both application scenarios we show that, although different values for retrieval parameters are needed to achieve the optimal performance, the desired textual or multimedia information can be effectively located using a combination of XML retrieval approaches

Entity Ranking in Wikipedia

The traditional entity extraction problem lies in the ability of extracting named entities from plain text using natural language processing techniques and intensive training from large document collections. Examples of named entities include organisations, people, locations, or dates. There are many research activities involving named entities; we are interested in entity ranking in the field of information retrieval. In this paper, we describe our approach to identifying and ranking entities from the INEX Wikipedia document collection. Wikipedia offers a number of interesting features for entity identification and ranking that we first introduce. We then describe the principles and the architecture of our entity ranking system, and introduce our methodology for evaluation. Our preliminary results show that the use of categories and the link structure of Wikipedia, together with entity examples, can significantly improve retrieval effectiveness.Comment: to appea

Specificity

Specificity is a relevance dimension that describes the extent to which a document part focuses on the topic of request. In the context of semi-structured text (XML) retrieval, a document part corresponds to an XML element. Specificity is defined as the length ratio, typically in number of characters, of contained relevant to irrelevant text in the document part. Different Specificity values can be associated to a document part. These values are drawn from the Specificity relevance scale, which has evolved from a discrete multi-graded relevance scale to a continuous relevance scale

Evaluation Metrics

An evaluation metric is used to evaluate the effectiveness of information retrieval systems and to justify theoretical and/or pragmatical developments of these systems. It consists of a set of measures that follow a common underlying evaluation methodology. There are many metrics that can be used to evaluate the effectiveness of semi-structured text (XML) retrieval systems. These metrics are based on different evaluation assumptions, incorporate different hypotheses of the expected user behaviour, and implement their own evaluation methodologies to handle the level of overlap among the XML information units

Relevance

Relevance is the extent to which some information is pertinent, connected, or applicable to the matter at hand. It represents a key concept in the fields of documentation, information science, and information retrieval. In information retrieval, the notion of relevance is used in three main contexts. Firstly, an algorithmic relevance score is assigned to a search result (usually a whole document) representing an estimated likelihood of relevance of the search result to a topic of request. This relevance score often determines the order in which search results are presented to the user. Secondly, when the performance of information retrieval systems is tested experimentally, the retrieved documents are assessed for their actual relevance to the topic of request by human assessors (topic experts). A binary relevance scale is typically used to assess the relevance of the search result, where the relevance is restricted to be either zero (when the result is not relevant to the user request) or one (when the result is relevant). Thirdly, in experiments involving users (or in operational settings) a broader notion of relevance is often used, with the aim of expressing the degree to which the retrieved documents are perceived as useful in solving the user's search task. In semi-structured text (XML) retrieval, the search result is typically an XML element, and the relevance score assigned by an XML retrieval system again represents an estimated likelihood of relevance of the search result to the topic of request. However, when the results are subsequently assessed for relevance, the binary relevance scale is not sufficient, primarily due to the hierarchical relationships that exist among the elements in an XML document. Accordingly, in XML retrieval one or more relevance dimensions (each with a multi-graded relevance scale) have been used to assess the relevance of the search result

Evaluating Focused Retrieval Tasks

International audienceFocused retrieval, identified by question answering, passage retrieval, and XML element retrieval, is becoming increasingly important within the broad task of information retrieval. In this paper, we present a taxonomy of text retrieval tasks based on the structure of the answers required by a task. Of particular importance are the in context tasks of focused retrieval, where not only relevant documents should be retrieved but also relevant information within each document should be correctly identified. Answers containing relevant information could be, for example, best entry points, or non-overlapping passages or elements. Our main research question is: How should the effectiveness of focused retrieval be evaluated? We propose an evaluation framework where different aspects of the in context focused retrieval tasks can be consistently evaluated and compared, and use fidelity tests on simulated runs to show what is measured. Results from our fidelity experiments demonstrate the usefulness of the proposed evaluation framework, and show its ability to measure different aspects and model different evaluation assumptions of focused retrieval