On Cognitive Preferences and the Plausibility of Rule-based Models

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus on plausibility and relation to interpretability, comprehensibility, and justifiabilit

UniversityIE: Information Extraction From University Web Pages

The amount of information available on the web is growing constantly. As a result, theproblem of retrieving any desired information is getting more difficult by the day. Toalleviate this problem, several techniques are currently being used, both for locatingpages of interest and for extracting meaningful information from the retrieved pages.Information extraction (IE) is one such technology that is used for summarizingunrestricted natural language text into a structured set of facts. IE is already being appliedwithin several domains such as news transcripts, insurance information, and weatherreports. Various approaches to IE have been taken and a number of significant resultshave been reported.In this thesis, we describe the application of IE techniques to the domain of universityweb pages. This domain is broader than previously evaluated domains and has a varietyof idiosyncratic problems to address. We present an analysis of the domain of universityweb pages and the consequences of having them input to IE systems. We then presentUniversityIE, a system that can search a web site, extract relevant pages, and processthem for information such as admission requirements or general information. TheUniversityIE system, developed as part of this research, contributes three IE methods anda web-crawling heuristic that worked relatively well and predictably over a test set ofuniversity web sites.We designed UniversityIE as a generic framework for plugging in and executing IEmethods over pages acquired from the web. We also integrated in the system a genericweb crawler (built at the University of Kentucky) and ported to Java and integrated anexternal word lexicon (WordNet) and a syntax parser (Link Grammar Parser)

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Democratizing Self-Service Data Preparation through Example Guided Program Synthesis,

The majority of real-world data we can access today have one thing in common: they are not immediately usable in their original state. Trapped in a swamp of data usability issues like non-standard data formats and heterogeneous data sources, most data analysts and machine learning practitioners have to burden themselves with "data janitor" work, writing ad-hoc Python, PERL or SQL scripts, which is tedious and inefficient. It is estimated that data scientists or analysts typically spend 80% of their time in preparing data, a significant amount of human effort that can be redirected to better goals. In this dissertation, we accomplish this task by harnessing knowledge such as examples and other useful hints from the end user. We develop program synthesis techniques guided by heuristics and machine learning, which effectively make data preparation less painful and more efficient to perform by data users, particularly those with little to no programming experience. Data transformation, also called data wrangling or data munging, is an important task in data preparation, seeking to convert data from one format to a different (often more structured) format. Our system Foofah shows that allowing end users to describe their desired transformation, through providing small input-output transformation examples, can significantly reduce the overall user effort. The underlying program synthesizer can often succeed in finding meaningful data transformation programs within a reasonably short amount of time. Our second system, CLX, demonstrates that sometimes the user does not even need to provide complete input-output examples, but only label ones that are desirable if they exist in the original dataset. The system is still capable of suggesting reasonable and explainable transformation operations to fix the non-standard data format issue in a dataset full of heterogeneous data with varied formats. PRISM, our third system, targets a data preparation task of data integration, i.e., combining multiple relations to formulate a desired schema. PRISM allows the user to describe the target schema using not only high-resolution (precise) constraints of complete example data records in the target schema, but also (imprecise) constraints of varied resolutions, such as incomplete data record examples with missing values, value ranges, or multiple possible values in each element (cell), so as to require less familiarity of the database contents from the end user.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163059/1/markjin_1.pd

Advanced Knowledge Technologies at the Midterm: Tools and Methods for the Semantic Web

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.In a celebrated essay on the new electronic media, Marshall McLuhan wrote in 1962:Our private senses are not closed systems but are endlessly translated into each other in that experience which we call consciousness. Our extended senses, tools, technologies, through the ages, have been closed systems incapable of interplay or collective awareness. Now, in the electric age, the very instantaneous nature of co-existence among our technological instruments has created a crisis quite new in human history. Our extended faculties and senses now constitute a single field of experience which demands that they become collectively conscious. Our technologies, like our private senses, now demand an interplay and ratio that makes rational co-existence possible. As long as our technologies were as slow as the wheel or the alphabet or money, the fact that they were separate, closed systems was socially and psychically supportable. This is not true now when sight and sound and movement are simultaneous and global in extent. (McLuhan 1962, p.5, emphasis in original)Over forty years later, the seamless interplay that McLuhan demanded between our technologies is still barely visible. McLuhan’s predictions of the spread, and increased importance, of electronic media have of course been borne out, and the worlds of business, science and knowledge storage and transfer have been revolutionised. Yet the integration of electronic systems as open systems remains in its infancy.Advanced Knowledge Technologies (AKT) aims to address this problem, to create a view of knowledge and its management across its lifecycle, to research and create the services and technologies that such unification will require. Half way through its sixyear span, the results are beginning to come through, and this paper will explore some of the services, technologies and methodologies that have been developed. We hope to give a sense in this paper of the potential for the next three years, to discuss the insights and lessons learnt in the first phase of the project, to articulate the challenges and issues that remain.The WWW provided the original context that made the AKT approach to knowledge management (KM) possible. AKT was initially proposed in 1999, it brought together an interdisciplinary consortium with the technological breadth and complementarity to create the conditions for a unified approach to knowledge across its lifecycle. The combination of this expertise, and the time and space afforded the consortium by the IRC structure, suggested the opportunity for a concerted effort to develop an approach to advanced knowledge technologies, based on the WWW as a basic infrastructure.The technological context of AKT altered for the better in the short period between the development of the proposal and the beginning of the project itself with the development of the semantic web (SW), which foresaw much more intelligent manipulation and querying of knowledge. The opportunities that the SW provided for e.g., more intelligent retrieval, put AKT in the centre of information technology innovation and knowledge management services; the AKT skill set would clearly be central for the exploitation of those opportunities.The SW, as an extension of the WWW, provides an interesting set of constraints to the knowledge management services AKT tries to provide. As a medium for the semantically-informed coordination of information, it has suggested a number of ways in which the objectives of AKT can be achieved, most obviously through the provision of knowledge management services delivered over the web as opposed to the creation and provision of technologies to manage knowledge.AKT is working on the assumption that many web services will be developed and provided for users. The KM problem in the near future will be one of deciding which services are needed and of coordinating them. Many of these services will be largely or entirely legacies of the WWW, and so the capabilities of the services will vary. As well as providing useful KM services in their own right, AKT will be aiming to exploit this opportunity, by reasoning over services, brokering between them, and providing essential meta-services for SW knowledge service management.Ontologies will be a crucial tool for the SW. The AKT consortium brings a lot of expertise on ontologies together, and ontologies were always going to be a key part of the strategy. All kinds of knowledge sharing and transfer activities will be mediated by ontologies, and ontology management will be an important enabling task. Different applications will need to cope with inconsistent ontologies, or with the problems that will follow the automatic creation of ontologies (e.g. merging of pre-existing ontologies to create a third). Ontology mapping, and the elimination of conflicts of reference, will be important tasks. All of these issues are discussed along with our proposed technologies.Similarly, specifications of tasks will be used for the deployment of knowledge services over the SW, but in general it cannot be expected that in the medium term there will be standards for task (or service) specifications. The brokering metaservices that are envisaged will have to deal with this heterogeneity.The emerging picture of the SW is one of great opportunity but it will not be a wellordered, certain or consistent environment. It will comprise many repositories of legacy data, outdated and inconsistent stores, and requirements for common understandings across divergent formalisms. There is clearly a role for standards to play to bring much of this context together; AKT is playing a significant role in these efforts. But standards take time to emerge, they take political power to enforce, and they have been known to stifle innovation (in the short term). AKT is keen to understand the balance between principled inference and statistical processing of web content. Logical inference on the Web is tough. Complex queries using traditional AI inference methods bring most distributed computer systems to their knees. Do we set up semantically well-behaved areas of the Web? Is any part of the Web in which semantic hygiene prevails interesting enough to reason in? These and many other questions need to be addressed if we are to provide effective knowledge technologies for our content on the web

Design considerations for a hierarchical semantic compositional framework for medical natural language understanding

Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods. However, current medical NLP systems fall considerably short when faced with the task of logically interpreting clinical text. In this paper, we describe a framework inspired by mechanisms of human cognition in an attempt to jump the NLP performance curve. The design centers about a hierarchical semantic compositional model (HSCM) which provides an internal substrate for guiding the interpretation process. The paper describes insights from four key cognitive aspects including semantic memory, semantic composition, semantic activation, and hierarchical predictive coding. We discuss the design of a generative semantic model and an associated semantic parser used to transform a free-text sentence into a logical representation of its meaning. The paper discusses supportive and antagonistic arguments for the key features of the architecture as a long-term foundational framework

Theory and Applications for Advanced Text Mining

Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields

Approach for testing the extract-transform-load process in data warehouse systems, An

2018 Spring.Includes bibliographical references.Enterprises use data warehouses to accumulate data from multiple sources for data analysis and research. Since organizational decisions are often made based on the data stored in a data warehouse, all its components must be rigorously tested. In this thesis, we first present a comprehensive survey of data warehouse testing approaches, and then develop and evaluate an automated testing approach for validating the Extract-Transform-Load (ETL) process, which is a common activity in data warehousing. In the survey we present a classification framework that categorizes the testing and evaluation activities applied to the different components of data warehouses. These approaches include both dynamic analysis as well as static evaluation and manual inspections. The classification framework uses information related to what is tested in terms of the data warehouse component that is validated, and how it is tested in terms of various types of testing and evaluation approaches. We discuss the specific challenges and open problems for each component and propose research directions. The ETL process involves extracting data from source databases, transforming it into a form suitable for research and analysis, and loading it into a data warehouse. ETL processes can use complex one-to-one, many-to-one, and many-to-many transformations involving sources and targets that use different schemas, databases, and technologies. Since faulty implementations in any of the ETL steps can result in incorrect information in the target data warehouse, ETL processes must be thoroughly validated. In this thesis, we propose automated balancing tests that check for discrepancies between the data in the source databases and that in the target warehouse. Balancing tests ensure that the data obtained from the source databases is not lost or incorrectly modified by the ETL process. First, we categorize and define a set of properties to be checked in balancing tests. We identify various types of discrepancies that may exist between the source and the target data, and formalize three categories of properties, namely, completeness, consistency, and syntactic validity that must be checked during testing. Next, we automatically identify source-to-target mappings from ETL transformation rules provided in the specifications. We identify one-to-one, many-to-one, and many-to-many mappings for tables, records, and attributes involved in the ETL transformations. We automatically generate test assertions to verify the properties for balancing tests. We use the source-to-target mappings to automatically generate assertions corresponding to each property. The assertions compare the data in the target data warehouse with the corresponding data in the sources to verify the properties. We evaluate our approach on a health data warehouse that uses data sources with different data models running on different platforms. We demonstrate that our approach can find previously undetected real faults in the ETL implementation. We also provide an automatic mutation testing approach to evaluate the fault finding ability of our balancing tests. Using mutation analysis, we demonstrated that our auto-generated assertions can detect faults in the data inside the target data warehouse when faulty ETL scripts execute on mock source data