Uncertainty Reduction for Knowledge Discovery and Information Extraction on the World Wide Web

During the past decade there have been significant advances in the field of Natural Language Processing (NLP) and, in particular, Information Extraction (IE) which have fueled the development and deployment of a wide variety of systems in diverse application areas. However, one application area that has only recently been explored by the NLP community is the area of Information Assurance and Security. In this paper we describe a system for human authentication called QDP, or Query-Directed Passwords, that uses information extraction techniques with the World Wide Web as a database to provide feedback, from the security viewpoint, on the quality of a hint and answer pair input by a user.

This paper discusses the challenges in operational crisis management and describes the role of information and geo-spatial technologies in meeting those challenges. The paper discusses two main sources of data, Web and very high resolution (VHR) earth observation sensors, in terms of relevance to crisis management and techniques for information extraction and analysis. Although research in information text extraction and analysis is more advanced than in information image extraction and analysis, further research is required in both these fields to take advantage of the increasing complexity but richness of open source and VHR satellite data. The paper also discusses the use of Web, GIS and Digital Earth technologies in facilitating collaborative work, decision-making and information sharing in crisis management. Despite exciting and relevant advances in information sources, information extraction and analysis methods, and collaborative crisis technologies, the main challenge remains to convince stakeholders in operational crisis management that the adoption of these technologies will lead to enhanced and effective crisis management.

Hyperspectral imaging (HSI) is a technology able to measure information about the spectral reflectance or transmission of light from the surface. The spectral data, usually within the ultraviolet and infrared regions of the electromagnetic spectrum, provide information about the interaction between light and different materials within the image. This fact enables the identification of different materials based on such spectral information. In recent years, this technology is being actively explored for clinical applications. One of the most relevant challenges in medical HSI is the information extraction, where image processing methods are used to extract useful information for disease detection and diagnosis. In this chapter, we provide an overview of the information extraction techniques for HSI. First, we introduce the background of HSI, and the main motivations of its usage for medical applications. Second, we present information extraction techniques based on both light propagation models within tissue and machine learning approaches. Then, we survey the usage of such information extraction techniques in HSI biomedical research applications. Finally, we discuss the main advantages and disadvantages of the most commonly used image processing approaches and the current challenges in HSI information extraction techniques in clinical applications.

Decision analysis and optimization are examples of inverse management problems which can be effectively handled by means of Artificial Intelligence (AT) and simulation techniques. However, the application of these techniques in specific industrial contexts finds major constraints in the lack of formalized methodologies and systematic procedures to retrieve and handle the data required for their implementation. The paper proposes Knowledge Discovery (KD) techniques as means of data pre-processing for the development of Al-based decision support systems (DSSs). This use of Knowledge Discovery techniques is illustrated in relation to the design of a hybrid DSS - combining AI and simulation techniques - for the management of maintenance services in the public transport sector.

Subjectivity analysis” systems automatically identify and extract information relating to attitudes, opinions, and sentiments from text. As more and more people make their opinions available on the Internet and as people increasingly consult the Internet to ascertain other people's opinions about products, political issues, and so on, the demand for effective subjectivity analysis systems continues to grow. Information extraction systems, which automatically identify and extract factual information relating to events of interest, remain critically important in this day and age of increasingly vast amounts of text available online. In this work, we discover that these research areas are mutually beneficial. Information extraction techniques may be used to learn informative clues of subjectivity. Then, by bootstrapping from a lexicon of subjectivity clues, we can build a subjective-objective sentence classifier that does not require annotated data as input. This classifier may then be used to improve information extraction performance, on data which have not been annotated for subjectivity, by improving precision.

New techniques for information extraction from multispectral data require physical modeling to understand the energy transfer at the atmosphere/surface interface and to develop appropriate inversion procedures, in combination with advanced processing techniques. A multi-step procedure is proposed in this work: the first step implies a binary decision about the second step to be applied in each case. If the pixel is considered as being a `pure' pixel, through a spectral/spatial classification procedure based on multiresolution techniques, then numerical inversion techniques, based on a multiple-scattering reflectance model, are used to extract parameters representing specific surface properties, which can only be defined for homogeneous pixels. If the pixel is considered from the first step as being a mixture of different elements, a linear unmixing approach is used to extract the amount of each component. For such heterogeneous pixels, effective values for the parameters are defined. Multiresolution techniques are applied to improve information extraction algorithms. The relation between spectral and spatial integration effects is discussed. Broad-band spectral integration is analyzed taking into account spatial integration over heterogeneous areas. The problem of the spatial scale is also considered, as the development of these techniques is closely related to the spatial/spectral resolution of the data. Applications of these algorithms to AVIRIS/TMS and Landsat TM/NOAA AVHRR data are an illustration of the capabilities and limits of such approaches.

Internet users have been exposing an increasing amount of Personally Identifiable Information (PII) on social media. Such exposed PII can be exploited by cybercriminals and cause severe losses to the users. Informing users of their PII exposure in social media is crucial to raise their privacy awareness and encourage them to take protective measures. To this end, advanced techniques are needed to extract users’ exposed PII in social media automatically, whereas most existing studies remain manual. While Information Extraction (IE) techniques can be used to extract the PII automatically, Deep Learning (DL)-based IE models alleviate the need for feature engineering and further improve the efficiency. However, DL-based IE models often require large-scale labeled data for training, but PII-labeled social media posts are difficult to obtain due to privacy concerns. Also, these models rely heavily on pre-trained word embeddings, while PII in social media often varies in forms and thus has no fixed representations in pre-trained word embeddings. In this study, we propose the Deep Transfer Learning for PII Extraction (DTL-PIIE) framework to address these two limitations. DTL-PIIE transfers knowledge learned from publicly available PII data to social media in order to address the problem of rare PII-labeled data. Moreover, our framework leverages Graph Convolutional Networks (GCNs) to incorporate syntactic patterns to guide PIIE without relying on pre-trained word embeddings. Evaluation against benchmark IE models indicates that our approach outperforms state-of-the-art DL-based IE models. An ablation analysis further confirms the efficacy of each component in our model. Our proposed framework can facilitate various applications, such as PII misuse prediction and privacy risk assessment, thereby protecting the privacy of internet users.

An increasing amount of electronically available information is stored in Asian language documents, which makes Information Retrieval (IR) and Information Extraction (IE) for these languages important for a large number of users. Analysis and extraction of information in these languages presents several interesting problems not seen in Western European languages; these are interesting in their own right and for the insights they can give into more general IR and IE techniques. We describe these problems and our system for Thai language IE One of the main concerns when working with Thai natural language is that the structure of the language itself is highly ambiguous. The analyser therefore requires more sophisticated techniques and large amounts of domain knowledge to cope with these ambiguities. We describe our approach to a natural language analysis system that performs preprocessing for the Thai language and the extraction module to retrieve specific information according to the predefined concept definitions.

Cybersecurity has become a critical necessity in aerospace, especially given the recent prevalent utilization of cyberspace as well as computing infrastructure, tools, and platforms in the domain. Cybersecurity relies on the timely discovery of potential and active vulnerabilities to mitigate threats, cyberattacks and theft of intellectual property. These vulnerabilities are commonly communicated via textual channels which can be mined and detected by information extraction techniques, such as Automatic Terminology Extraction (ATE), in order to mitigate imminent and future attacks. Distributed Specificity, a modern and highly effective ATE paradigm, was first introduced by [4], [3] and further improved by [21]. These methods, however, regard the context words linearly and indiscriminately, which 1) disregards the non-linear relations among the context words and 2) assigns an equal weight to each word edge in the sequence despite the fact that the connections of the nodes are not of equal strength. As our first contribution, the present article addresses this shortcoming by integrating the self-attention mechanism into the distributed specificity paradigm to enhance candidate terms' representation resulting in a more accurate extraction, which in turn lends itself to the higher coverage and a more timely discovery of cyberattacks. Since no ATE dataset exists in the cybersecurity domain, to the best of our knowledge, the present endeavor offers a dataset, as its second contribution, that will be the benchmark for our experiments and the ones to follow by both ATE and cybersecurity research communities. This complements the SemEval's SecureNLP [35] information extraction repository. We extend the MalwareTextDB (comprised of 147 cybersecurity reports) to include labeled terminologies as ground truth. The labeled dataset is annotated by cybersecurity practitioners using Term Evaluator [25]. Although for most industries higher coverage and a more timely discovery of cyberthreats will translate into protecting vital data, customer privacy, and sizable funds to name but a few advantages, in aerospace the risk and therefore the reward may be much higher directly resulting in saving human lives in both small and large scales.

In recent years both the number and the size of organisational databases have increased rapidly. However, although available processing power has also grown, the increase in stored data has not necessarily led to a corresponding increase in useful information and knowledge. This has led to a growing interest in the development of tools capable of harnessing the increased processing power available to better utilise the potential of stored data. The terms Knowledge Discovery in Databases and Mining have been adopted for a field of research dealing with the automatic discovery of knowledge implicit within databases. Data mining is useful in situations where the volume of data is either too large or too complicated for manual processing or, to a lesser extent, where human experts are unavailable to provide knowledge. The success already attained by a wide range of data mining applications has continued to prompt further investigation into alternative data mining techniques and the extension of data mining to new domains. This paper surveys, from the standpoint of the database systems community, current issues in data mining research by examining the architectural and process models adopted by knowledge discovery systems, the different types of discovered knowledge, the way knowledge discovery systems operate on different data types, various techniques for knowledge discovery and the ways in which discovered knowledge is used.

Waste container weighing data processing to create reliable information of household waste generation

: Six essential processes that must be accomplished if use of a remote- sensing system is to result in useful information are defined as problem specification, ground control data acquisition, remote-sensor information acquisition, data manipulation, information extraction, and information presentation. Several fairly common and not so common sensor types are introduced, and some devices and information extraction and presentation techniques found to be useful in remote-sensing projects are described. An overview of the current state-of-the-art of remote sensing is presented.

Textual corpora, such as financial documents, contain a wealth of knowledge. Recently, knowledge graphs have become a popular approach to capturing structured knowledge of entities and their interrelationships. In this paper, we evaluate open information extraction (IE) and knowledge graph construction techniques for assessing the relevance of textual segments in the Financial Entity Identification and Information Integration Challenge. Our approach is to extract several textual signals, including topics and open IE triples, and combine these in a probabilistic framework to predict the relevance of each potential relationship.

Our goal is to construct a domain-targeted, high precision knowledge base (KB), containing general (subject,predicate,object) statements about the world, in support of a downstream question-answering (QA) application. Despite recent advances in information extraction (IE) techniques, no suitable resource for our task already exists; existing resources are either too noisy, too named-entity centric, or too incomplete, and typically have not been constructed with a clear scope or purpose. To address these, we have created a domain-targeted, high precision knowledge extraction pipeline, leveraging Open IE, crowdsourcing, and a novel canonical schema learning algorithm (called CASI), that produces high precision knowledge targeted to a particular domain - in our case, elementary science. To measure the KB’s coverage of the target domain’s knowledge (its “comprehensiveness” with respect to science) we measure recall with respect to an independent corpus of domain text, and show that our pipeline produces output with over 80% precision and 23% recall with respect to that target, a substantially higher coverage of tuple-expressible science knowledge than other comparable resources. We have made the KB publicly available.

Under the aims at corpus automatic collection on the process of the large-scale Lexicography corpus collection, the paper described corpus collection Technology which is based on Web. And then, the paper introduces the book information corpus tool, including how to make use of the technique of search engine and information extraction in our system.