Naeem Khalid Janjua

Semantic information and knowledge integration through argumentative reasoning to support intelligent decision making

The availability of integrated, high quality information is a pre-requisite for a decision support system (DSS) to aid in the decision-making process. The introduction of semantic web ensures the seamless integration of information derived from diverse sources and transforms the DSS to an adoptable and flexible Semantic Web-DSS (Web-DSS). However, due to the monotonic nature of the layered development of semantic web, it lacks the capability to represent, reason and integrate incomplete and conflicting information. This, in turn, renders an enterprise incapable of knowledge integration; that is, integration of information about a subject that could potentially be incomplete, inconsistent and distributed among different Web-DSS within or across enterprises. In this article, we address the issues of incomplete and inconsistent semantic information and knowledge integration by using argumentation and argumentation schemes. We discuss the Argumentation-enabled Information Integration Web-DSS (Web@IDSS) along with its syntax and semantics for semantic information integration, and devise a methodology for sharing the results of Web@IDSS in Argument Interchange Format (AIF) format. We also discuss Argumentation-enabled Knowledge Integration Web-DSS (Web@KIDSS) for semantic knowledge integration. We provide formal syntax and semantics for the Web@KIDSS, propose a conceptual framework, and describe it in detail. We present the algorithms for knowledge integration and the prototype application for validation of results.

Web@IDSS - Argumentation-enabled Web-based IDSS for reasoning over incomplete and conflicting information

Over the past few decades, there has been a resurgence of interest in using high-level software intelligence for business intelligence (BI). The objective is to produce actionable information that is delivered at the right time, easily comprehendible and exportable to other software to assist business decision-making processes. Although the design and development of decision support systems (DSS) has been carried out for over 40 years, DSS still suffer from many limitations such as poor maintainability, poor flexibility and less reusability. The development of the Internet and WWW has helped information systems to overcome those limitations and Web DSS is now an active area of research in business intelligence, impacting significantly on the way information is exchanged and businesses are conducted. However, to remain competitive, companies rely on business intelligence (BI) to continually monitor and analyze the operating environment (both internal and external), to identify potential risks, and to devise competitive business strategies. However, the current Web DSS applications are not able to reason over information present across organizational boundaries which could be incomplete and conflicting. The use of an argumentation-based mechanism has not been explored to address such shortcomings in Web DSS. Argumentation is a kind of commonsense reasoning used by human beings to reach a justifiable conclusion when available information is incomplete and/or inconsistent among participants. In this paper, we propose and elaborate in detail a conceptual framework and formal argumentation-based semantics for Web enabled Intelligent DSS (Web@IDSS). We evaluate the use of argumentative reasoning in Web DSS with the help of a case study, prototype development and future directions. Applications built according to the proposed framework will provide more practical, understandable results to decision makers.

Digital health care ecosystem: SOA compliant HL7 based health care information interchange

Increasing demand for health care services pose a number of challenges to healthcare industry and one of them is sharing health care information among heterogeneous health care information systems. Digital health care ecosystem aims at providing continues treatment process requiring seamless information sharing across multiple healthcare providers/organizations. Different health care standards and technologies have been proposed to address this challenge. HL7 is one of the standards for information sharing involves object oriented modelling to the development and specification of information being interchanged. It also specifies transportation specification for message exchange named as web service profiles. We propose Service Oriented Architecture (SOA) compliant HL7 based system architecture for health care information interchange. The propose architecture comprises of Web Service Adapter, HL7 V3 message parsing/generation, V2 to V3 translation and database mapping to HL7 modelling elements. We describe in detail the WSDL construct document for the Result Query Filler application role in Laboratory domain.

In-house Crowdsourcing-Based Entity Resolution: Dealing with Common Names

Entity Resolution (ER) is one of the techniques used to disambiguate the various manifestations of same object to improve search results in databases. Recently, Crowd sourcing has been utilized to improve entity resolution to gain positive impact when searching for particular information in a database. In this paper, we consider the domain of Customer Relationship Management (CRM) and utilize the approach of Crowd sourcing to enrich the process of achieving ER. Specifically our focus is to identify the right customer that has been manifested in various ways under a common name in a database using In-house Crowd sourcing-based Entity Resolution approach (ICER). The ICER takes the list of possible duplicates into consideration (which are pre-determined) and identifies the pair of record that has the maximum impact in achieving ER. Then, this pair is crowd sourced to Customer Service Representatives (CSRs) to have their input (labeling). ICER incorporates the principles of Human Intelligence Task (HIT) that aims to keep the questions asked to the CSR to a minimum. Two ICER approaches are proposed in this study based on probabilistic (modified approach of Whang et al) and active learning schemas. The applicability of the proposed ICER approaches and comparison of their results have been highlighted by using an example.

Development of a Logic Layer in the Semantic Web: Research Issues

The ontology layer of the semantic web is now mature enough (i.e. standards like RDF, RDFs, OWL, OWL 2) and the next step is to work on a logic layer for the development of advanced reasoning capabilities for knowledge extraction and efficient decision making. Adding logic to the web means using rules to make inferences. Rules are a means of expressing business processes, policies, contracts etc but most of the studies have focused on the use of monotonic logics in layered development of the semantic web which provides no mechanism for representing or handling incomplete or contradictory information respectively. This paper discusses argumentation, semantic web and defeasible logic programming with their distinct features and identifies the different research issues that need to be addressed in order to realize defeasible argumentative reasoning in the semantic web applications.

Cognition and Statistical-Based Crowd Evaluation Framework for ER-in-House Crowdsourcing System: Inbound Contact Center

Entity identification and resolution has been a hot topic in computer science from last three decades. The ever increasing amount of data and data quality issues such as duplicate records pose great challenge to organizations to efficiently and effectively perform their business operations such as customer relationship management, marketing, contact centers management etc. Recently, crowdsourcing technique has been used to improve the accuracy of entity resolution that make use of human intelligence to label the data and make it ready for further processing by entity resolution (ER) algorithms. However, labelling of data by humans is an error prone process that affects the process of entity resolution and eventually overall performance of crowd. Thus controlling the quality of labeling task is an essential for crowdsourcing systems. However, this task becomes more challenging due to unavailability of ground data. In this paper, we address the above mentioned challenge and design and develop framework for evaluating performance of ER-In-house crowdsourcing system using cognition and statistical-based techniques. Our methodology is divided into two phases namely before-hand evaluation and in-process evaluation. In before-hand evaluation a cognitive approach is used to filter out workers with an inappropriate cognitive style for ER-labeling task. To this end, analytic hierarchy process (AHP) is used to classify the existing four primary cogitative styles discussed in the literature either as suitable or not-suitable for labelling task under consideration. To control the quality of work by crowd-workers, we extend and use the statistical approach proposed by Joglekar et al. during second phase i.e. in-process evaluation. To illustrate effectiveness of our approach; we have considered the domain of Inbound Contact Center and using Customer Service Representatives (CSRs) knowledge for ER-labeling task. In the proposed ER-In-house crowdsourcing system CSRs are considered as crowd-workers. Synthetic dataset is used to demonstrate the applicability of the proposed cognition and statistical-based CSRs evaluation approaches.

Process Map Discovery from Business Policies: A Knowledge Representation Approach with Argumentative Reasoning (KR@PMD)

In Chap. 5, an Argumentation-enabled Web-based IDSS (Web@IDSS) was proposed to help decision makers consider the structured information, which exists within the enterprise and/or in other enterprises, to represent, reason over it, resolve conflicts between this information and the existing enterprise information using the Generalize specificity-based conflict resolution strategy and integrate this to assist in the decision-making process.

Defeasible Reasoning Based Argumentative Web-IDSS for Virtual Teams (VTs)

The Web-based intelligent decision support system(Web-IDSS) is pivotal for a Virtual Team (VT) to successfully execute business-related tasks. The current generation of Web-IDSS built on top of semantic web technologies for VTs lacks the capability to provide decision support when underlying information is incomplete and/or contradictory. In this article, we address this limitation of current Web-IDSS through defeasible logic based argumentation formalism. The proposed Web-IDSS uses a hybrid reasoning approach: forward chaining(data-driven) for the construction of arguments over incomplete information, and backward chaining (goal-driven) for conflict identification and resolution with explanation. The proposed Web-IDSS adheres to web standards and publishes the outcome of argumentative reasoning in Argument Interchange Format (AIF).

Event-driven approach for predictive and proactive management of SLA violations in the Cloud of Things

Abstract In a dynamic environment such as the cloud-of-things, one of the most critical factors for successful service delivery is the QoS under defined constraints. Even though guarantees in the form of service level agreements (SLAs) are provided to users, many services exhibit dynamic Quality of Service (QoS) variations. This QoS variation as well as changes in the behavior and state of the service is caused by some internal events (such as varying loads) and external events (such as location and weather), which results in frequent SLA violations. Most of the existing violation prediction approaches use historic data to predict future QoS values. They do not consider dynamic changes and the events that cause these changes in QoS attributes. In this paper, we propose an event-driven-based proactive approach for predicting SLA violations by combining logic-based reasoning and probabilistic inferencing. The results show that our proposed approach is efficient and proactively identifies SLA violations under uncertain QoS observations.

Interleaving Collaborative Planning and Execution Along with Deliberation in Logistics and Supply Chain

Automated planning is a rich technical filed in Artificial Intelligence (AI) and most of the existing research focused on path finding methods in a compact state-transition system where planning is decoupled from execution. The introduction of the Web has led to increasing emphasis in AI on the development of planning algorithms for real-world applications where planning is distributed and plan generation can happen concurrently with plan execution. An example of one such real-world application is logistics and supply chain. In this paper, we envisage a collaborative planning and execution framework for logistics and supply chain operations. The framework supports human planners for a collaborative plan construction. The planning is interleaved with execution where new information collected during execution is used to refine the plan if required. Additionally, planning is defeasible in nature. During planning either conflicting viewpoints may arise among planners and/or the new information collected during execution may result in conflicts among planned tasks (situations). Deliberation module in the proposed framework provides a platform to human planners where they can start an argumentative dialogue to resolve the conflicts by establishing preferences between conflicting tasks. We use situation calculus to model the framework and propose an algorithm to interleave collaborative planning with execution along with deliberation support.