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

Data Mining Practical Machine Learning Tools and Techniques

Mini Mental State

In the recent years, different Web knowledge graphs, both free and commercial, have been created. While Google coined the term "Knowledge Graph" in 2012, there are also a few openly available knowledge graphs, with DBpedia, YAGO, and Freebase being among the most prominent ones. Those graphs are often constructed from semi-structured knowledge, such as Wikipedia, or harvested from the web with a combination of statistical and linguistic methods. The result are large-scale knowledge graphs that try to make a good trade-off between completeness and correctness. In order to further increase the utility of such knowledge graphs, various refinement methods have been proposed, which try to infer and add missing knowledge to the graph, or identify erroneous pieces of information. In this article, we provide a survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used.

Knowledge graph refinement: A survey of approaches and evaluation methods

Urban planning and building smart cities based on the Internet of Things using Big Data analytics

Enterprise Collaboration Systems are designed in such a way to maximise the efficiency of communication and collaboration within the enterprise. With users becoming mobile, the Internet of Things can play a crucial role in this process, but is far from being seamlessly integrated in modern online communications. In this paper, we showcase the use of a solution that goes beyond today’s ad-hoc integration and processing of heterogeneous data sources for static and streaming data, providing more flexible and efficient processing techniques that can bridge the gap between IoT and online Enterprise Communication Systems. We document the technologies used for sensor deployment, sensor data acquisition based on the OpenIoT framework, and stream federation. Our main contributions are the following, i) we present a conceptual architecture of IoT-enabled Communication Systems, that builds upon existing frameworks for semantic data acquisition, and tools to enable continuous processing, discovery and federation of dynamic data sources based on Linked Data; ii) we present a semantic information model for representing and linking IoT data, social data and personal data by re-using and extending the existing standard semantic models; iii) we evaluate the performance of virtualisation of IoT sources based on OpenIoT in our testbed and show the impact of transmission, annotation and data storage, as well as initial results on scalability of RDF stream query processing in such a framework, providing guidelines and directions for optimisation.

A Semantic Processing Framework for IoT-Enabled Communication Systems

We are currently investigating methods to triplify the content of Wikipedia's tables. We propose that existing knowledge-bases can be leveraged to semi-automatically extract high-quality facts (in the form of RDF triples) from tables embedded in Wikipedia articles (henceforth called "Wikitables"). We present a survey of Wikitables and their content in a recent dump of Wikipedia. We then discuss some ongoing work on using DBpedia to mine novel RDF triples from these tables: we present methods that automatically extract 24.4 million raw triples from the Wikitables at an estimated precision of 52.2%. We believe this precision can be (greatly) improved through machine learning methods and sketch ideas for features that should help classify (in)correct triples.

/pdf/triplifying-wikipedia-s-tables-5211y0criy.pdf

Triplifying Wikipedia's tables

http://www.edwardcurry.org/publications/FGCS2017.pdf

Automated discovery and integration of semantic urban data streams: The ACEIS middleware

We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP). It allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities and for learning of such weights from data (parameter estimation). Weighted formulas are given a semantics in terms of soft and hard constraints which determine a probability distribution over answer sets. In contrast to related approaches, we approach inference by optionally utilizing so-called streamlining XOR constraints, in order to reduce the number of computed answer sets. Our approach is prototypically implemented. Examples illustrate the introduced concepts and point at issues and topics for future research.

Probabilistic Inductive Logic Programming Based on Answer Set Programming

In recent years there has been a growing interest in solutions for the delivery of clinical care for the elderly because of the large increase in aging population. Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but, to be useful, this activity must not be too invasive for patients and a burden for caregivers. We prototyped a system called Secure and INDependent lIving (SINDI), focused on (a) collecting a limited amount of data about the person and the environment through Wireless Sensor Networks (WSN), and (b) inferring from these data enough information to support caregivers in understanding patients' well-being and in predicting possible evolutions of their health. Our hierarchical logic-based model of health combines data from different sources, sensor data, tests results, common-sense knowledge and patient's clinical profile at the lower level, and correlation rules between health conditions across upper levels. The logical formalization and the reasoning process are based on Answer Set Programming. The expressive power of this logic programming paradigm makes it possible to reason about health evolution even when the available information is incomplete and potentially incoherent, while declarativity simplifies rules specification by caregivers and allows automatic encoding of knowledge. This paper describes how these issues have been targeted in the application scenario of the SINDI system.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S147106841000058X

Alessandra Mileo

Papers

A Semantic Processing Framework for IoT-Enabled Communication Systems

Triplifying Wikipedia's tables

Automated discovery and integration of semantic urban data streams: The ACEIS middleware

Probabilistic Inductive Logic Programming Based on Answer Set Programming

Reasoning support for risk prediction and prevention in independent living