With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.

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Web mining research: a survey

We present a new keyword extraction algorithm that applies to a single document without using a corpus. Frequent terms are extracted first, then a set of cooccurrence between each term and the frequent terms, i.e., occurrences in the same sentences, is generated. Co-occurrence distribution shows importance of a term in the documentas follows. If probability distribution of co-occurrence between term a and the frequent terms is biased to a particular subset of frequent terms, then term a is likely to be a keyword. The degree of biases of distribution is measured by the χ 2 -measure. Our algorithm shows comparable performance to tfidf without using a corpus.

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Keyword extraction from a single document using word co-occurrence statistical information

Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.

Mining Text Data

Generally, decisions are based on information. To be useful, information must be reliable. Basically, the concept of a Z-number relates to the issue of reliability of information. A Z-number, Z, has two components, Z=(A,B). The first component, A, is a restriction (constraint) on the values which a real-valued uncertain variable, X, is allowed to take. The second component, B, is a measure of reliability (certainty) of the first component. Typically, A and B are described in a natural language, for example: (about 45 minutes, very sure). Z-number has many applications, especially in the realms of economics, decision analysis, risk assessment, prediction, anticipation, rule-based characterization of imprecise functions and relations, and biomedicine. Different methods, applications, and systems are discussed. Other Fuzzy concepts are also discussed.

Methods and Systems for Applications for Z-numbers

In this paper, we propose an unsupervised method for discovering inference rules from text, such as "X is author of Y a X wrote Y", "X solved Y a X found a solution to Y", and "X caused Y a Y is triggered by X". Inference rules are extremely important in many fields such as natural language processing, information retrieval, and artificial intelligence in general. Our algorithm is based on an extended version of Harris' Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus.

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DIRT @SBT@discovery of inference rules from text

Knowledge Discovery in Databases (KDD) focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form. Previous work in text mining focused at the word or the tag level. This paper presents an approach to performing text mining at the term level. The mining process starts by preprocessing the document collection and extracting terms from the documents. Each document is then represented by a set of terms and annotations characterizing the document. Terms and additional higher-level entities are then organized in a hierarchical taxonomy. In this paper we will describe the Term Extraction module of the Document Explorer system, and provide experimental evaluation performed on a set of 52,000 documents published by Reuters in the years 1995–1996.

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Text Mining at the Term Level

Knowledge Discovery in Databases (KDD), also known as data mining, focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form Given a collection of text documents, most approaches to text mining perform knowledge-discovery operations on labels associated with each document At one extreme, these labels are keywords that represent the results of non-trivial keyword-labeling processes, and, at the other extreme, these labels are nothing more than a list of the words within the documents of interest This paper presents an intermediate approach, one that we call text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document These terms plus additional higher-level entities are then organized in a hierarchical taxonomy and are used in the knowledge discovery process This paper describes Document Explorer, our tool that implements text mining at the term level It consists of a document retrieval module, which converts retrieved documents from their native formats into documents represented using the SGML mark-up language used by Document Explorer; a two-stage term-extraction approach, in which terms are first proposed in a termgeneration stage, and from which a smaller set are then selected in a term-filtering stage in light of their frequencies of occurrence elsewhere in the collection; our taxonomy-creation tool by which the user can help specify higher-level entities that inform the knowledge-discovery process; and our knowledge-discovery tools for the resulting term-labeled documents Finally, we evaluate our approach on a collection of patent records as well as Reuters newswire stories Our results confirm that Text Mining serves as a powerful technique to manage knowledge encapsulated in large document collections

Knowledge Management: A Text Mining Approach

Knowledge Discovery in Databases (KDD), also known as data mining, focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form. Given a collection of text documents, most approaches to text mining perform knowledge-discovery operations on labels associated with each document. At one extreme, these labels are keywords that represent the results of non-trivial keyword-labeling processes, and, at the other extreme, these labels are nothing more than a list of the words within the documents of interest. This paper presents an intermediate approach, one that we call text mining via information extraction, in which knowledge discovery takes place on a more focused collection of events and phrases that are extracted from and label each document. These events plus additional higher-level entities are then organized in a hierarchical taxonomy and are used in the knowledge discovery process. This approach was implemented in the Textoscope system. Textoscope consists of a document retrieval module which converts retrieved documents from their native formats into SGML documents used by Textoscope; an information extraction engine, which is based on a powerful attribute grammar which is augmented by a rich background knowledge; a taxonomy-creation tool by which the user can help specify higher-level entities that inform the knowledge-discovery process; and a set of knowledge-discovery tools for the resulting event-labeled documents. We evaluate our approach on a collection of newswire stories extracted by Textoscope’s own agent. Our results confirm that Text Mining via information extraction serves as an accurate and powerful technique by which to manage knowledge encapsulated in large document collections.

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Text Mining via Information Extraction

The proliferation of digitally available textual data necessitates automatic tools for analyzing large textual collections. Thus, in analogy to data mining for structured databases, text mining is defined for textual collections. A central tool in text-mining is the analysis of concept relationship, which discovers connections between different concepts, as reflected in the collection. However, discovering these relationships is not sufficient, as they also have to be presented to the user in a meaningful and manageable way. In this paper we introduce a new family of visualization tools, which we coin circle graphs, which provide means for visualizing concept relationships mined from large collections. Circle graphs allow for instant appreciation of multiple relationships gathered from the entire collection. A special type of circle-graphs, called Trend Graphs, allows tracking of the evolution of relationships over time.

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Yonatan Schler

Papers

Text Mining at the Term Level

Knowledge Management: A Text Mining Approach

Text Mining via Information Extraction

Circle Graphs: New Visualization Tools for Text-Mining