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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

The paper investigates techniques for extracting data from HTML sites through the use of automatically generated wrappers. To automate the wrapper generation and the data extraction process, the paper develops a novel technique to compare HTML pages and generate a wrapper based on their similarities and dierences. Experimental results on real-life data-intensive Web sites confirm the feasibility of the approach.

Towards automatic data extraction from large web sites

/pdf/roadrunner-towards-automatic-data-extraction-from-large-web-340pxk72wc.pdf

RoadRunner: Towards Automatic Data Extraction from Large Web Sites

In the last few years, several works in the literature have addressed the problem of data extraction from Web pages. The importance of this problem derives from the fact that, once extracted, the data can be handled in a way similar to instances of a traditional database. The approaches proposed in the literature to address the problem of Web data extraction use techniques borrowed from areas such as natural language processing, languages and grammars, machine learning, information retrieval, databases, and ontologies. As a consequence, they present very distinct features and capabilities which make a direct comparison difficult to be done. In this paper, we propose a taxonomy for characterizing Web data extraction fools, briefly survey major Web data extraction tools described in the literature, and provide a qualitative analysis of them. Hopefully, this work will stimulate other studies aimed at a more comprehensive analysis of data extraction approaches and tools for Web data.

http://www.knowlesys.com/papers/IE_02_A_Brief_Survey_of_Web_Data_Extraction_Tools.pdf

A brief survey of web data extraction tools

In this paper we present DEByE(Data Extraction By Example), an approach to extracting data from Web sources, based on a small set of examples specified by the user. The novelty is in the fact that the user specifies examples according to a structure of his liking and that this structure is described at example specification time. For the specification of the examples, the user interacts with a tool we developed which adopts nested tables as its visual paradigm. Nested tables are simple, intuitive, and allow shielding the user from technical details (such as HTML tags, formatting operators, and learning automata) related to the extraction problem. The examples provided by the user are then used to generate patterns which allow extracting data from new documents. For the extraction, DEByE adopts a new bottom-up procedure we proposed which is very effective with various Web sources, as demonstrated by our experiments.

DEByE - Date extraction by example

As the number of research papers available on the Web has increased enormously over the years, paper recommender systems have been proposed to help researchers on automatically finding works of interest. The main problem with the current approaches is that they assume that recommending algorithms are provided with a rich set of evidence (e.g., document collections, citations, profiles) which is normally not widely available. In this paper we propose a novel source independent framework for research paper recommendation. The framework requires as input only a single research paper and generates several potential queries by using terms in that paper, which are then submitted to existing Web information sources that hold research papers. Once a set of candidate papers for recommendation is generated, the framework applies content-based recommending algorithms to rank the candidates in order to recommend the ones most related to the input paper. This is done by using only publicly available metadata (i.e., title and abstract). We evaluate our proposed framework by performing an extensive experimentation in which we analyzed several strategies for query generation and several ranking strategies for paper recommendation. Our results show that good recommendations can be obtained with simple and low cost strategies.

A source independent framework for research paper recommendation

The widespread use of templates on the Web is considered harmful for two main reasons. Not only do they compromise the relevance judgment of many web IR and web mining methods such as clustering and classification, but they also negatively impact the performance and resource usage of tools that process web pages. In this paper we present a new method that efficiently and accurately removes templates found in collections of web pages. Our method works in two steps. First, the costly process of template detection is performed over a small set of sample pages. Then, the derived template is removed from the remaining pages in the collection. This leads to substantial performance gains when compared to previous approaches that combine template detection and removal. We show, through an experimental evaluation, that our approach is effective for identifying terms occurring in templates - obtaining F-measure values around 0.9, and that it also boosts the accuracy of web page clustering and classification methods.

https://vgc.poly.edu/~juliana/pub/CIKM630-vieira.pdf

A fast and robust method for web page template detection and removal

As the Web grows, more and more data has become available under dynamic forms of publication, such as legacy databases accessed by an HTML form (the so called hidden Web). In situations such as this, integration of this data relies more and more on the fast generation of agents that can automatically fetch pages for further processing. As a result, there is an increasing need for tools that can help users generate such agents. In this paper, we describe a method for automatically generating agents to collect hidden Web pages. This method uses a pre-existing data repository for identifying the contents of these pages and takes the advantage of some patterns that can be found among Web sites to identify the navigation paths to follow. To demonstrate the accuracy of our method, we discuss the results of a number of experiments carried out with sites from different domains.

Altigran Soares da Silva

Papers

A brief survey of web data extraction tools

DEByE - Date extraction by example

A source independent framework for research paper recommendation

A fast and robust method for web page template detection and removal

Automatic generation of agents for collecting hidden web pages for data extraction