Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Empowering users to access databases using simple keywords can relieve the users from the steep learning curve of mastering a structured query language and understanding complex and possibly fast evolving data schemas. In this tutorial, we give an overview of the state-of-the-art techniques for supporting keyword search on structured and semi-structured data, including query result definition, ranking functions, result generation and top-k query processing, snippet generation, result clustering, query cleaning, performance optimization, and search quality evaluation. Various data models will be discussed, including relational data, XML data, graph-structured data, data streams, and workflows. We also discuss applications that are built upon keyword search, such as keyword based database selection, query generation, and analytical processing. Finally we identify the challenges and opportunities of future research to advance the field.

/pdf/keyword-search-on-structured-and-semi-structured-data-216cw01urw.pdf

Keyword search on structured and semi-structured data

It has become highly desirable to provide users with flexible ways to query/search information over databases as simple as keyword search like Google search. This book surveys the recent developments on keyword search over databases, and focuses on finding structural information among objects in a database using a set of keywords. Such structural information to be returned can be either trees or subgraphs representing how the objects, that contain the required keywords, are interconnected in a relational database or in an XML database. The structural keyword search is completely different from finding documents that contain all the user-given keywords. The former focuses on the interconnected object structures, whereas the latter focuses on the object content. The book is organized as follows. In Chapter 1, we highlight the main research issues on the structural keyword search in different contexts. In Chapter 2, we focus on supporting structural keyword search in a relational database management system using the SQL query language. We concentrate on how to generate a set of SQL queries that can find all the structural information among records in a relational database completely, and how to evaluate the generated set of SQL queries efficiently. In Chapter 3, we discuss graph algorithms for structural keyword search by treating an entire relational database as a large data graph. In Chapter 4, we discuss structural keyword search in a large tree-structured XML database. In Chapter 5, we highlight several interesting research issues regarding keyword search on databases. The book can be used as either an extended survey for people who are interested in the structural keyword search or a reference book for a postgraduate course on the related topics. Table of Contents: Introduction / Schema-Based Keyword Search on Relational Databases / Graph-Based Keyword Search / Keyword Search in XML Databases / Other Topics for Keyword Search on Databases

/pdf/keyword-search-in-databases-zgmjig4158.pdf

Keyword Search in Databases

Studies show that about 50% of web search is for information exploration purpose, where a user would like to investigate, compare, evaluate, and synthesize multiple relevant results. Due to the absence of general tools that can effectively analyze and differentiate multiple results, a user has to manually read and comprehend potentially large results in an exploratory search. Such a process is time consuming, labor intensive and error prone. With meta information embedded, keyword search on structured data provides the potential for automating or semi-automating the comparison of multiple results.In this paper we present an approach for differentiating search results on structured data. We define the differentiability of query results and quantify the degree of difference. Then we define the problem of identifying a limited number of valid features in a result that can maximally differentiate this result from the others, which is proved to be NP-hard. We propose two local optimality conditions, namely single-swap and multi-swap. Efficient algorithms are designed to achieve local optimality. To show the applicability of our approach, we implemented a system XRed for XML result differentiation. Our empirical evaluation verifies the effectiveness and efficiency of the proposed approach.

/pdf/structured-search-result-differentiation-27iq4ouiv5.pdf

Structured search result differentiation

Keyword search is integrated in many applications on account of the convenience to convey users' query intention. Recently, answering keyword queries on XML data has drawn the attention of web and database communities, because the success of this research will relieve users from learning complex XML query languages, such as XPath/XQuery, and/or knowing the underlying schema of the queried XML data. As a result, information in XML data can be discovered much easier.To model the result of answering keyword queries on XML data, many LCA (lowest common ancestor) based notions have been proposed. In this paper, we focus on ELCA (Exclusive LCA) semantics, which is first proposed by Guo et al. and afterwards named by Xu and Papakonstantinou. We propose an algorithm named Hash Count to find ELCAs efficiently. Our analysis shows the complexity of Hash Count algorithm is O(kd|S1|), where k is the number of keywords, d is the depth of the queried XML document and |S1| is the frequency of the rarest keyword. This complexity is the best result known so far. We also evaluate the algorithm on a real DBLP dataset, and compare it with the state-of-the-art algorithms. The experimental results demonstrate the advantage of Hash Count algorithm in practice.

/pdf/fast-elca-computation-for-keyword-queries-on-xml-data-3mpsxovv7d.pdf

Fast ELCA computation for keyword queries on XML data

Adapting keyword search to XML data has been attractive recently, generalized as XML keyword search (XKS). One of its key tasks is to return the meaningful fragments as the result. [1] is the latest work following this trend, and it focuses on returning the fragments rooted at SLCA (Smallest LCA -- Lowest Common Ancestor) nodes. To guarantee that the fragments only contain interesting nodes, [1] proposes a contributor-based filtering mechanism in its MaxMatch algorithm. However, the filtering mechanism is not sufficient. It will commit the false positive problem (discarding interesting nodes) and the redundancy problem (keeping uninteresting nodes).In this paper, our interest is to propose a framework of retrieving meaningful fragments rooted at not only the SLCA nodes, but all LCA nodes. We begin by introducing the concept of Relaxed Tightest Fragment (RTF) as the basic result type. Then we propose a new filtering mechanism to overcome those two problems in Max-Match. Its kernel is the concept of valid contributor, which helps to distinguish the interesting children of a node. The new filtering mechanism is then to prune the nodes in a RTF which are not valid contributors to their parents. Based on the valid contributor concept, our ValidRTF algorithm not only overcomes those two problems in MaxMatch, but also satisfies the axiomatic properties deduced in [1] that an XKS technique should satisfy. We compare ValidRTF with MaxMatch on real and synthetic XML data. The result verifies our claims, and shows the effectiveness of our valid-contributor-based filtering mechanism.

/pdf/retrieving-meaningful-relaxed-tightest-fragments-for-xml-47hbw0j3i3.pdf

Retrieving meaningful relaxed tightest fragments for XML keyword search

The Web of data with meaning in the sense that a computer program can learn enough about what the data means to process it. During the last decade, the World Wide Web has evolved into the most important public data store on world. An important challenge for computer science today is to develop accurate information extraction and question answering mechanisms for the Web. Berners-Lee points out the difficulty of that task, and that it might even require more adequate formats of Web data representation. Information must be structured, and structure should reflect semantic information, so that machines can learn enough about what the data means. The standard document formats of the Web today, HTML and XML, rely on tree structures that encompass textual information. In this project we want to incorporate novel approaches for modeling tree structure and emerging techniques for machine learning into adaptive information extraction systems for the Web. In the future, we might also have to account for semantic information. The research team is a joint project team with the lifl (CNRS and Lille 1 University) and the Grappa Group (Lille 3 University). The project will be located in Lille 3 University.

Modeling Tree Structures, Machine Learning, and Information Extraction

This paper proposes a new effective filtering mechanism for pruning the uninteresting nodes implied in the SLCA-based (Smallest LCA --- Lowest Common Ancestor) fragments for XML keyword search. Its fundamental concept is the valid contributor. Given two nodes v and u , and u is v 's parent, the child v is a valid contributor to its parent u , if (1) v 's label is unique among all u 's children; or (2) for the siblings with same label as v , v 's content is not covered by any of them. The new filtering mechanism can be described as following: every node in each retrieved fragment should be valid contributor to its parent.

ValidMatch: Retrieving More Reasonable SLCA-Based Result for XML Keyword Search

Adapting keyword search to XML data has been attractive recently, generalized as XML Keyword Search (XKS). Its fundamental task is to retrieve meaningful and concise result for the given keyword query, and [1] is the latest work which returns the fragments rooted at the SLCA (Smallest LCA - Lowest Common Ancestor) nodes. To guarantee the fragments only containing meaningful nodes, [1] proposed a contributor-based filtering mechanism in its MaxMatch algorithm. However, the filtering mechanism is not sufficient. It will commit the false positive problem (discarding interesting nodes) and the redundancy problem (keeping uninteresting nodes).In this paper, we propose a new filtering mechanism to overcome those two problems. The fundamental concept is valid contributor. A child v is a valid contributor to its parent u, if (1) v's label is unique among all u's children; or (2) for the siblings with same label as v, v's content is not covered by any of them. Our new filtering mechanism is: all the nodes in each retrieved fragment should be valid contributors to their parents. By doing so, it not only satisfies the axiomatic properties proposed by [1], but also ensures the filtered fragment more meaningful and concise. We implement our proposal in ValidMatch, and compare ValidMatch with MaxMatch on real and synthetic XML data. The result verifies our claims, and shows the effectiveness of our valid-contributor-based filtering mechanism.

Retrieving valid matches for XML keyword search

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Retrieving meaningful relaxed tightest fragments for XML keyword search

Modeling Tree Structures, Machine Learning, and Information Extraction

ValidMatch: Retrieving More Reasonable SLCA-Based Result for XML Keyword Search

Retrieving valid matches for XML keyword search