Representing Documents via Latent Keyphrase Inference.

Parametric logic is a framework that generalises classical first-order logic. A generalised notion of logical consequence—a form of preferential entailment based on a closed world assumption—is defined as a function of some parameters. A concept of possible knowledge base—the counterpart to the consistent theories of first-order logic—is introduced. The notion of compactness is weakened. The degree of weakening is quantified by a nonnull ordinal—the larger the ordinal, the more significant the weakening. For every possible knowledge base T, a hierarchy of sentences that are generalised logical consequences of T is built. The first layer of the hierarchies corresponds to sentences that can be obtained by a deductive inference, characterised by the compactness property. The second layer of the hierarchies corresponds to sentences that can be obtained by an inductive inference, characterised by the property of weak compactness quantified by 1. Weaker forms of compactness—quantified by nonnull ordinals—determine higher layers in the hierarchies, corresponding to more complex inferences. The naturalness of the hierarchies built over the possible knowledge bases is attested by fundamental connections with notions from Learning theory and from topology. The naturalness of the hierarchies built over the possible knowledge bases is attested by fundamental connections with notions from Learning theory—classification in the limit, with or without a bounded number of mind changes—and from topology—in reference to the Borel and the difference hierarchies. In this paper, we introduce the key model-theoretic aspects of Parametric logic, justify the concept of the knowledge base, define the hierarchies of generalised logical consequences and illustrate their relevance to Nonmonotonic reasoning. More specifically, we show that the degree of nonmonotonicity that is required to infer a sentence can be characterised by the least nonnull ordinal that quantifies the weakening of compactness used to locate the inferred sentence in the hierarchies.

Revising knowledge bases (KBs) in description logics (DLs) in a syntax-independent manner is an important, nontrivial problem for the ontology management and DL communities. Several attempts have been made to adapt classical model-based belief revision and update techniques to DLs, but they are restricted in several ways. In particular, they do not provide operators or algorithms for general DL KB revision. The key difficulty is that, unlike propositional logic, a DL KB may have infinitely many models with complex (and possibly infinite) structures, making it difficult to define and compute revisions in terms of models. In this paper, we study general KBs in a specific DL in the DL-Lite family. We introduce the concept of features for such KBs, develop an alternative semantic characterization of KBs using features (instead of models), define two specific revision operators for KBs, and present the first algorithm for computing best approximations for syntax-independent revisions of KBs.

Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation.In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences.The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches.

Regional innovation strategies rank at the top of public policy agendas today. There is a widespread consensus in both academic and policy circles that standardized “best practice” innovation policy models suffer from severe limitations and major shortcomings. The recent literature is replete with claims that regional innovation policies should be placebased and context-sensitive, taking into consideration the specificities of regions and their distinctive preconditions and capacities for innovation. Various conceptual approaches and theories support such a view. This paper discusses two concepts that have a particularly strong potential for informing customized regional innovation policies: the regional innovation system (RIS) approach and the knowledge base concept. The RIS literature highlights the importance of the organizational and institutional setting of a region and suggests that system deficiencies or failures should constitute the starting point for designing regional innovation policies. The differentiated knowledge base approach stresses that regional industries can differ strongly in their underlying knowledge bases and, as a consequence, in their policy needs. We elaborate on the policy implications that originate from these concepts and argue that tailor-made regional innovation policies should consider both region-specific institutional set-ups and knowledge bases. Focusing on peripheral regions, we outline how such an integrated framework can inform customized regional innovation policies.

Text mining is becoming vital as Web 2.0 offers collaborative content creation and sharing. Now Researchers have growing interest in text mining methods for discovering knowledge. Text mining researchers come from variety of areas like: Natural Language Processing, Computational Linguistic, Machine Learning, and Statistics. A typical text mining application involves preprocessing of text, stemming and lemmatization, tagging and annotation, deriving knowledge patterns, evaluating and interpreting the results. There are numerous approaches for performing text mining tasks, like: clustering, categorization, sentimental analysis, and summarization. There is a growing need to standardize the evaluation of these tasks. One major component of establishing standardization is to provide standard datasets for these tasks. Although there are various standard datasets available for traditional text mining tasks, but there are very few and expensive datasets for blog-mining task. Blogs, a new genre in web 2.0 is a digital diary of web user, which has chronological entries and contains a lot of useful knowledge, thus offers a lot of challenges and opportunities for text mining. In this paper, we report a new indigenous dataset for Pakistani Political Blogosphere. The paper describes the process of data collection, organization, and standardization. We have used this dataset for carrying out various text mining tasks for blogosphere, like: blog-search, political sentiments analysis and tracking, identification of influential blogger, and clustering of the blog-posts. We wish to offer this dataset free for others who aspire to pursue further in this domain.

In this chapter, we show how formal knowledge representation and reasoning techniques can be used for the retrieval and interpretation of multimedia data. This section explains what we mean by an “interpretation” using examples of audio and video interpretation. Intuitively, interpretations are descriptions of media data at a high abstraction level, exposing interrelations and coherencies. In Section 3.2.3, we introduce description logics (DLs) as the formal basis for ontology languages of the OWL (web ontology language) family and for the interpretation framework described in subsequent sections. As a concrete example, we consider the interpretation of images describing a sports event in Section 3.3. It is shown that interpretations can be obtained by abductive reasoning, and a general interpretation framework is presented. Stepwise construction of an interpretation can be viewed as navigation in the compositional and taxonomical hierarchies spanned by a conceptual knowledge base. What do we mean by “interpretation” of media objects? Consider the image shown in Fig. 3.1. One can think of the image as a set of primitive objects such as persons, garbage containers, a garbage truck, a bicycle, traffic signs, trees, etc. An interpretation of the image is a description which “makes sense” of these primitive objects. In our example, the interpretation could include the assertions “two workers empty garbage containers into a garbage truck” and “a mailman distributes mail” expressed in some knowledge representation language. When including the figure caption into the interpretation process, we have a multimodal interpretation task which in this case involves visual and textual media objects. The result could be a refinement of the assertions above in terms of the location “in Hamburg”. Note that the interpretation describes activities extending in time although it is only based on a snapshot. Interpretations may generally include

The authors describe a generic framework for model-based behaviour interpretation and its application to monitoring aircraft service activities. Behaviour models are represented in a standardised conceptual knowledge base using OWL-DL for concept definitions and the extension SWRL for constraints. The conceptual knowledge base is automatically converted into an operational scene interpretation system implemented in Java and JESS that accepts tracked objects as input and delivers high-level activity descriptions as output. The interpretation process employs Beam Search for exploring the interpretation space, guided by a probabilistic rating system. The probabilistic model cannot be efficiently represented in the ontology, but it has been designed to closely correspond to the compositional hierarchy of behaviour concepts. Experiments are described that demonstrate the system performance with real airport data.

In this study, the researchers used a classical Delphi method to re-examine the conceptual framework, definition, and knowledge base of the field. Seventeen engaged scholars, each representing the expert agricultural education community, reached consensus on defining the field of study, 10 knowledge domains, and 67 knowledge objects. The Delphi panel agreed, “Agricultural education–2010, as a field of study, integrates social and behavioral sciences with the natural and applied science of agriculture, renewable natural resources, and environment. The knowledge base for agricultural education–2010 includes planning and needs assessment; curriculum development; learning theory; instructional design; delivery strategies; evaluation; research methods and tools; scholarship and writing; history, philosophy, and ethics; and contextual applications, culture, and diversity—all effecting continual improvement. Agricultural education empowers people to think more critically, to perform more skillfully, to communicate more clearly, to plan and affect change more efficiently, to solve problems more creatively, and to act based on principles—all of which involves vital choices and consequences in a global society.” The Delphi panel concurred on 10 knowledge domains composed of 67 core knowledge objects consisting of fundamental and powerful concepts, knowledge, paradigms, skills, and/or theories that are essential for building professional practice in agricultural education.

ABSTRACTWhile classical bag‐of‐word (BoG) approaches represent text content in the word level, recent studies show that knowledge‐based concept indexation is a promising approach to further enhance the text search and mining performance. In this study, we propose a new knowledge indexation/extraction method, Explicit Semantic Path Mining (ESPM), for knowledge‐base text mining. It has roots in a concept‐based vector constructing method, Explicit Semantic Analysis (ESA), which has shown success in text mining tasks. For this new method, given an input piece of text, ESPM can efficiently identify the independent and optimized semantic path(s) on a concept map, which is, in this study, the Wikipedia category tree. Unlike earlier studies focusing on BoG based vector space, ESPM is a semantic path mining algorithm, which generates the top down semantic categories of a given text by leveraging the rich link information between Wikipedia categories and articles. Preliminary experiment based on ODP data shows ESPM delivers high quality independent semantic paths from both precision and ranking viewpoints.

Innovations in science and technology boost-up the generation of an immense volume of digital data in the form of text, audio, video, images and paved the way for a new era called Big Data. A majority of this data is generated as a semi-structured and unstructured format poses a challenge for Natural Language Processing (NLP) and text analytics. This chapter seeks to layout the text mining perspective of big data analytics with an emphasis on applications in biomedicine and healthcare. The chapter aims to provide a better understanding of areas such as Big Data, Text Mining, Data Mining, Natural Language Processing and Machine Learning and its inter linkage. The chapter illustrates phases and task of text mining in big data scope. Finally, the chapter ends with a description of the two application areas biomedicine and healthcare where text mining using big data analytics will be used.

Provides readers with the methods, algorithms, and means to perform text mining tasks This book is devoted to the fundamentals of text mining using Perl, an open-source programming tool that is freely available via the Internet (www.perl.org). It covers mining ideas from several perspectives--statistics, data mining, linguistics, and information retrieval--and provides readers with the means to successfully complete text mining tasks on their own. The book begins with an introduction to regular expressions, a text pattern methodology, and quantitative text summaries, all of which are fundamental tools of analyzing text. Then, it builds upon this foundation to explore: Probability and texts, including the bag-of-words model Information retrieval techniques such as the TF-IDF similarity measure Concordance lines and corpus linguistics Multivariate techniques such as correlation, principal components analysis, and clustering Perl modules, German, and permutation tests Each chapter is devoted to a single key topic, and the author carefully and thoughtfully introduces mathematical concepts as they arise, allowing readers to learn as they go without having to refer to additional books. The inclusion of numerous exercises and worked-out examples further complements the book's student-friendly format. Practical Text Mining with Perl is ideal as a textbook for undergraduate and graduate courses in text mining and as a reference for a variety of professionals who are interested in extracting information from text documents.

Stemming is a technique used to reduce inflected and derived words to their basic forms (stem or root). It is a very important step of pre-processing in text mining, and generally used in many areas of research such as: Natural language Processing NLP, Text Categorization TC, Text Summarizing TS, Information Retrieval IR, and other tasks in text mining. Stemming is frequently useful in text categorization to reduce the size of terms vocabulary, and in information retrieval to improve the search effectiveness and then gives us relevant results. In this paper, we propose a new multilingual stemmer based on the extraction of word root and in which we use the technique of n-grams. We validated our stemmer on three languages which are: Arabic, French and English.

The significance of natural language texts as the prime information structure for the management and dissemination of knowledge in organisations is still increasing. Making relevant documents available depending on varying tasks in different contexts is of primary importance for any efficient task completion. Implementing this demand requires the content based processing of texts, which enables to reconstruct or, if necessary, to explore the relationship of task, context and document. Text mining is a technology that is suitable for solving problems of this kind. In the following, semiotic aspects of text mining are investigated. Based on the primary object of text mining - natural language lexis - the specific complexity of this class of signs is outlined and requirements for the implementation of text mining procedures are derived. This is done with reference to text linkage introduced as a special task in text mining. Text linkage refers to the exploration of implicit, content based relations of texts (and their annotation as typed links in corpora possibly organised as hypertexts). In this context, the term systemic functional hypertext is introduced, which distinguishes genre and register layers for the management of links in a poly-level hypertext system.

In this paper, we propose a novel approach for knowledge discovery from textual data. The generated knowledge base can be used as one of the main components in the cognitive process of question answering systems. The proposed model automatically extract relations between named enti- ties in Persian. Our proposed model is a bootstrapping approach based on n-gram model to nd the representative textual patterns of relations as n-grams in order to extract new knowledge about given named entities. The main motivation for this work is the characteristic of the sentence structure in Persian which, in contrary to English sentences, is in subject- object-verb format. The proposed approach is a purely statistical one and no background knowledge of the target language is required. This makes our method applicable to any open domain relation extraction task. How- ever, as for our test-bed, we focus on the domain of biographical data of international poets and scientists to build a knowledge base about them. Qualitative evaluations based on human assessment is an evidence for the ecacy of our method.

This paper proposes a strategy of semantic processing implemented in an Indonesian text understanding evaluation system. It uses component that already developed in Institut Teknologi Bandung consists of POS Tagger and Syntactic Parser. This research used syntax-driven semantic analysis technique by attaching semantic rules into associated syntactic rule. Semantic rule is used as base of λ-reduction operation, used to produce sentence's knowledge representation in first order logic's form. There is also semantic lexical attachment process, used to give a meaning of each words or phrases in sentence. Semantic processor is implemented in Indonesian text-understanding evaluation system. It has ability to evaluate a reader's understanding of a text by comparing the reader's understanding with computer's understanding. This system consists of text understanding module, question generation module, and understanding evaluation module. Text-understanding module is developed using semantic processor with addition of reference resolution to relate each sentences in text.