User Models for Information Systems: Prospects and Problems

Expert systems attempt to model multiple aspects of human-computer interaction, including the reasoning of the human expert, the knowledge base, and characteristics and goals of the user. This paper focuses on models of the human user that are held by the system and utilized in interaction, with particular attention to information retrieval applications. User models may be classified along several dimensions, including static vs. dynamic, stated vs. inferred, and short-term vs. longterm models. The choice of the type of model will depend on a number of factors, including frequency of use, the relationship between the user and the system, the scope of the system, and the diversity of the user population. User models are most effective for well-defined tasks, domains, and user characteristics and goals. These user-system aspects tend not to be well defined in most information retrieval applications.published or submitted for publicatio

Creating New Pathways to Justice Using Simple Artificial Intelligence and Online Dispute Resolution

Access to justice in can be improved significantly through implementation of simple artificial intelligence (AI) based expert systems deployed within a broader online dispute resolution (ODR) framework. Simple expert systems can bridge the ‘implementation gap’ that continues to impede the adoption of AI in the justice domain. This gap can be narrowed further through the design of multi-disciplinary expert systems that address user needs through simple, non-legalistic user interfaces. This article provides a non-technical conceptual description of an expert system designed to enhance access to justice for non-experts. The system’s knowledge base would be populated with expert knowledge from the justice and dispute resolution domains. A conditional logic rule-based system forms the basis of the inference engine located between the knowledge base and a questionnaire-based user interface. The expert system’s functions include problem diagnosis, delivery of customized information, self-help support, triage and streaming into subsequent ODR processes. Its usability is optimized through the engagement of human computer interaction (HCI) and effective computing techniques that engage the social and emotional sides of technology. The conceptual descriptions offered in this article draw support from empirical observations of an innovative project aimed at creating an expert system for an ODR-enabled civil justice tribunal

Creating New Pathways to Justice Using Simple Artificial Intelligence and Online Dispute Resolution

Access to justice in can be improved significantly through implementation of simple artificial intelligence (AI) based expert systems deployed within a broader online dispute resolution (ODR) framework. Simple expert systems can bridge the ‘implementation gap’ that continues to impede the adoption of AI in the justice domain. This gap can be narrowed further through the design of multi-disciplinary expert systems that address user needs through simple, non-legalistic user interfaces. This article provides a non-technical conceptual description of an expert system designed to enhance access to justice for non-experts. The system’s knowledge base would be populated with expert knowledge from the justice and dispute resolution domains. A conditional logic rule-based system forms the basis of the inference engine located between the knowledge base and a questionnaire-based user interface. The expert system’s functions include problem diagnosis, delivery of customized information, self-help support, triage and streaming into subsequent ODR processes. Its usability is optimized through the engagement of human computer interaction (HCI) and effective computing techniques that engage the social and emotional sides of technology. The conceptual descriptions offered in this article draw support from empirical observations of an innovative project aimed at creating an expert system for an ODR-enabled civil justice tribunal

KB4VA: A Knowledge Base of Visualization Designs for Visual Analytics

Visual analytics (VA) systems have been widely used to facilitate decision-making and analytical reasoning in various application domains. VA involves visual designs, interaction designs, and data mining, which is a systematic and complex paradigm. In this work, we focus on the design of effective visualizations for complex data and analytical tasks, which is a critical step in designing a VA system. This step is challenging because it requires extensive knowledge about domain problems and visualization to design effective encodings. Existing visualization designs published in top venues are valuable resources to inspire designs for problems with similar data structures and tasks. However, those designs are hard to understand, parse, and retrieve due to the lack of specifications. To address this problem, we build KB4VA, a knowledge base of visualization designs in VA systems with comprehensive labels about their analytical tasks and visual encodings. Our labeling scheme is inspired by a workshop study with 12 VA researchers to learn user requirements in understanding and retrieving professional visualization designs in VA systems. The theme extends Vega-Lite specifications for describing advanced and composited visualization designs in a declarative manner, thus facilitating human understanding and automatic indexing. To demonstrate the usefulness of our knowledge base, we present a user study about design inspirations for VA tasks. In summary, our work opens new perspectives for enhancing the accessibility and reusability of professional visualization designs

BRIDGE LAWS IN HYPERTEXT: A LOGIC MODELING APPROACH

Increasingly, computerized systems tend to delegate certain portions of their functionality to other systems. This is routinely done by systems that use Data Base Management Systems (DBMS) to manage their data. The DBMS is in charge of all data related operations. A similar phenomena is emerging in the area of graphical user-interfaces. As more of these delegation phenomena occur, the establishment of flexible communication channels for the different applications becomes increasingly important. We propose to achieve this communication by establishing a set of relationships between the applications. These relationships will be specified by bridge laws, i.e. laws that establish bridges between different domains. We concentrate on a particular example: coupling arbitrary applications to a hypertext user interface. In terms of the discussion above, one of the systems in consideration is fixed. We study the elements that are needed in order to establish effective bridge laws. We do this by defining a general framework and providing two examples. The first example deals with a Data Base Management System, and the second one with a model management system. The examples show that in order to achieve effective interaction between a system and a hypertext interface, some meta-knowledge is required. We extrapolate from our experiments to conclude the type of general properties of bridge laws that are necessary to achieve this high level type of process communication.Information Systems Working Papers Serie

GAIML: A New Language for Verbal and Graphical Interaction in Chatbots

Natural and intuitive interaction between users and complex systems is a crucial research topic in human-computer interaction. A major direction is the definition and implementation of systems with natural language understanding capabilities. The interaction in natural language is often performed by means of systems called chatbots. A chatbot is a conversational agent with a proper knowledge base able to interact with users. Chatbots appearance can be very sophisticated with 3D avatars and speech processing modules. However the interaction between the system and the user is only performed through textual areas for inputs and replies. An interaction able to add to natural language also graphical widgets could be more effective. On the other side, a graphical interaction involving also the natural language can increase the comfort of the user instead of using only graphical widgets. In many applications multi-modal communication must be preferred when the user and the system have a tight and complex interaction. Typical examples are cultural heritages applications (intelligent museum guides, picture browsing) or systems providing the user with integrated information taken from different and heterogenous sources as in the case of the iGoogle™ interface. We propose to mix the two modalities (verbal and graphical) to build systems with a reconfigurable interface, which is able to change with respect to the particular application context. The result of this proposal is the Graphical Artificial Intelligence Markup Language (GAIML) an extension of AIML allowing merging both interaction modalities. In this context a suitable chatbot system called Graphbot is presented to support this language. With this language is possible to define personalized interface patterns that are the most suitable ones in relation to the data types exchanged between the user and the system according to the context of the dialogue

CoNet: Collaborative Cross Networks for Cross-Domain Recommendation

The cross-domain recommendation technique is an effective way of alleviating the data sparse issue in recommender systems by leveraging the knowledge from relevant domains. Transfer learning is a class of algorithms underlying these techniques. In this paper, we propose a novel transfer learning approach for cross-domain recommendation by using neural networks as the base model. In contrast to the matrix factorization based cross-domain techniques, our method is deep transfer learning, which can learn complex user-item interaction relationships. We assume that hidden layers in two base networks are connected by cross mappings, leading to the collaborative cross networks (CoNet). CoNet enables dual knowledge transfer across domains by introducing cross connections from one base network to another and vice versa. CoNet is achieved in multi-layer feedforward networks by adding dual connections and joint loss functions, which can be trained efficiently by back-propagation. The proposed model is thoroughly evaluated on two large real-world datasets. It outperforms baselines by relative improvements of 7.84\% in NDCG. We demonstrate the necessity of adaptively selecting representations to transfer. Our model can reduce tens of thousands training examples comparing with non-transfer methods and still has the competitive performance with them.Comment: Deep transfer learning for recommender system

Mining Useful Information from Big Data Models Through Semantic-based Process Modelling and Analysis

Over the past few decades, most of the existing methods for analysing large growing knowledge bases, particularly Big Data, focus on building algorithms and/or technologies to help the knowledge-bases automatically or semi-automatically extend. Indeed, a vast number of such systems that construct the said large knowledge-bases continuously grow, and most often, they do not contain all of the facts about each process instance or elements that can be found within the process base. As a consequence, the resultant process models tend to be vague or missing value datasets. In view of such challenge, the work in this paper demonstrates that a well-designed information retrieval system or the process mining (PM) methods should present the results or discovered patterns in a formal and structured format qua being interpreted as domain knowledge. To this end, the work introduces a process mining approach that supports further enhancement of existing information systems or knowledge-base through the conceptual means of data analysis. In turn, the paper proposes a semantic-based process mining and analysis method, or better still, information retrieval and extraction system - that is capable of detecting patterns or unobserved behaviours within any given knowledge base by making use of the underlying semantics or properties (metadata) that describes the available data. Thus, the proposed approach is grounded on the semantic modelling and process mining techniques. The work illustrates this method using the case study of Learning Process. The goal is to discover user interaction patterns within a learning execution environment and respond by making decisions based on the semantical analysis of the captured users data. Practically, the method applies semantic annotation and ontological representation of the learning process domain data and the resultant models in order to discover patterns automatically by means of semantic reasoning. Theoretically, the process mining and modelling method show that a way of addressing the common challenge with computational intelligent systems or methods is through an effectively well-designed and fit for purpose system that meets the requirements and needs of the intended users. In other words, this paper applies effective reasoning methods to make inferences over a process knowledge-base (e.g. learning process) that leads to an automated discovery of learning patterns or behaviour

Ontology-Based Question Answering System in Restricted Domain

The complexity of natural language presents difficult challenges that traditional Questions and Answers (Q&A) system such as Frequently Asked Questions, relied on the collective predefined questions and answers, unable to address. Traditional Q&A system is unable to retrieve exact answer in response to different kind of natural language questions asked by the user. Therefore, this paper aims to present an architecture of Ontology-based Question Answering (OQA) system, applied to library domain. The main task of OQA system is to parse question expressed in natural language with respect to restricted domain ontology and retrieve the matched answer. Restricted ontology model is designed as a knowledge base to assist the process based on the effective information derived from the questions. In addition, ontology matching algorithm is developed to deal with the questionanswer matching process. A case study is taken from the library of Sultanah Nur Zahirah of Universiti Malaysia Terengganu. A prototype of Sultanah Nur Zahirah Digital Learning ONtologybased FAQ System (SONFAQS) is developed. The experimental result shows that the architecture is feasible and significantly improves man-machine interaction by shortening the searching time

Robots to the Rescue: A Review of Studies on Differential Medical Diagnosis Employing Ontology-Based Chat Bot Technology

Access to medical care is a global issue. Technology-aided approaches have been applied in addressing this. Interventions have however not focused on medical diagnosis as a fully automated procedure and available applications employ mainly text-based inputs rather than conversation in natural language. We explored the utility of ontology-based chatbot technology for the design of intelligent agents for medical diagnosis through a systematic review of the most recent related literature. English articles published in 2011-2016 returned 233 hits which yielded 11 relevant articles after a 3-stage screening. Findings showed that the creation of expert systems had been the focus of many the studies which utilize the physician-system-patient framework with system training based mostly on expert knowledge for designing web- or mobile phone-based applications that serve assistive purposes. Findings further indicated gaps in the design and evaluation of more effective systems deployable as standalone applications, for example, on an embodied robotic system. The need for technology supporting the physical examination part of diagnosis, connection to data sources on patients’ vitals and medical history are also indicated in addition to the need for more qualitative work on natural language-based interaction. The system should be one that is continuously learning. Future works should also be directed towards the building of more robust knowledge base as well as evaluation of theory-based diagnostic methodological option