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Survey of Temporal Knowledge Representation (Second Exam)

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Survey of Temporal Knowledge Representation (Second Exam) Survey of Temporal Knowledge Representation (Second Exam) Sami Al-Dhaheri The Graduate Center, CUNY Department of Computer Science June 20, 2016 Abstract Knowledge Representation (KR) is a subfield within Artificial Intelligence that aims to represent, store, and retrieve knowledge in symbolic form that is easily manipulated using a computer. The vision of Semantic Web has recently increased because of the interest of using and a applying the Knowledge Representation methodology in both academia and industry. Knowledge Representation formalism are often named as one of the main tools that can support the Semantic Web. Knowledge Representation has many forms, including logic-based and non-logic based formalisms. This survey will only be concerned with logic-based KR. The review will present various methods authors have used in applying logic-based KR. We will present each methodology with respect to its formalism, and we will present the reasoning in Description logic. The survey will also discuss possible temporal extensions of Description Logic, RDF and OWL. 1 Introduction Knowledge Representation and Reasoning is a scientific domain within the area of Artificial Intelligence in Computer Science. It aims to encode human knowledge in a computerized fashion using formal models that allow the computer to take human knowledge and represent it in a meaningful manner [1]. In this context, computers are able to represent implicit knowledge found within the encoded body of human knowledge provided to the system, thus resulting in a behavior that is known as “Intelligent” one. Computer Scientists have devised systems to represent knowledge and make inferences based on the encoded knowledge. Such systems have been referred to as Knowledge Base Systems; their main components are a knowledge base and a reasoning engine. Mathematicians were the first to explore and apply logic-based Knowledge Representation before the computers revolution. However, the field acquired more recognition during the 1970s with the rise of the field of Computer Science in the academy and industry. Two forms of Knowledge Representation emerged: logic based and non-logic based. With the introduction of the World Wide Web in 1989, Knowledge Representation became of crucial importance to achieve its goal of representing implicit knowledge found within the wide array of information created and shared by people [2]. Knowledge Representation has played a significant role in the development of Semantic Web. One of the areas KR has been active in within Semantic Web is Ontologies. Ontologies provide vocabulary for annotations of semantics in application domains. The first and foremost significant aim of Knowledge Representation is creating new forms of knowledge and reasoning by using existing ones. Computers can be taught to use encoded human languages to extract implicit trends, dimensions, semantics and other forms of knowledge. The formal specification of things is also accessible to machines. Practically, a standard web ontology language is needed and have been developed for the above applications. Knowledge Representation becomes useful in many fields including medical decisions’ support, financial decision making, institutional research frameworks and others. The developed Knowledge Based Systems can be used to not only extract knowledge, but also instruct to make decisions based on such knowledge. Therefore, the wide application of Semantic Web renders Knowledge Representation as one of the most worthy topics of research and development. Science not only strives to explain variance in a given variable, it also tries to develop an understanding of a particular phenomenon. One element scientists consider in their analyses is the effect of time. As in social sciences, time matters and witnesses many outcomes. It is also of utmost significance to Computer Scientists, especially in Semantic Web. For instance, the description of a concept in ontology often reference to temporal patterns. e.g., the definition of salary in employee ontology that changes over time. Similarly, time plays a critical role when using Description logics (DL) to represent and reason about conceptual representation of temporal knowledge. Therefore, models used in Knowledge Representation includes time as an important element. We can represent “static knowledge" that means not changing. Besides that, "temporality" is an important issue imposed by the flows of time. The issue comes when the represented knowledge changes over time. Which is called dynamic knowledge. Dynamic knowledge is maintained by updating, deleting, or putting "outdated flag" on the "old" knowledge. In the past two decades, temporal databases have been extensively researched. They cover several dozens of temporal data models that are mainly extension of the relational model. Temporal query languages are proposed. Issues of representing time have also been tackled. In general, a knowledge representation formalism can be extended to cover the temporal part of the knowledge. There are two basic strategies: one is to formally extend the model. For instance, extend the Description Logics to Temporal Description Logics. The other is that the temporal extension is added on top of the formalism. Ontology versioning is an example. The reification approach is somewhat between these two extremities [3]. In this survey we review the recent research and developments in KR. We examine various logic-based knowledge representation approaches with the aim of deciding upon a better approach for temporal knowledge representation. Our focus is on temporal aspect of knowledge representation within semantic web. It’s worth mentioning that much of this survey is concerned with logic-based knowledge representation. In this survey, we will explore important issues in knowledge representation formalisms, including their formal semantics when a syntax is referred, and reasoning and inference services provided. Temporal extensions will be presented next. The survey paper is organized as follows: in section 2, different logic-based knowledge representation formalisms are introduced. Section 3 discusses various nonlogic-base knowledge representation technologies associated with the Semantic Web .Section 4 covers the temporal extension to Description logics, Temporal RDF and Temporal OWL. Section 5 provides a brief comparisons between the formalisms. Section 6 general discussion. Finally, in Section 7, we draw conclusion. 2 Logic-based Knowledge Representation Formalisms One may classify knowledge by its nature: declarative or procedural. Declarative knowledge represents simple facts, it includes the truth of propositions or statements about the world. For instance, humans can eat at any given time of the day. Declarative knowledge can be easily retrieved from a knowledge base using a simple operation, a look up process. The retrieval of declarative knowledge could be the solution for a problem a user has, or it could be part of a larger solution. Conversely, procedural knowledge involves knowing HOW to do something. It details a set of processes as opposed to facts. Procedural knowledge outlines a step by step process for reaching a particular end or solution to a problem facing the user. For instance, operating a vehicle on the road necessitates procedural knowledge allowing a driver to fulfill the end of driving a car on a certain road. Unlike declarative knowledge, simple look up operations are inadequate to retrieve procedural knowledge. To retrieve procedural knowledge one may resort to the use and implementation of algorithms. Moreover, procedural knowledge involves implicit learning. Researchers developed a number of knowledge representation formalisms within the semantic web context. The decision of choosing a particular formalism heavily depends on the need and nature of the problem faced by the user. Semantic networks, production rules and formal logic provide the main knowledge representation formalisms in semantic web. RDF graphs can be utilized to specify models of semantic networks [4]. Logic is used to recognize a precise semantic interpretation for both of the other forms. By providing formal semantics for knowledge representation languages. A Knowledge representation formalism help in representing the relevant knowledge of a specific domain as facts and rules in such a way that make it efficiently retrievable. Despite this useful application of KR, one may be interested in retrieving knowledge that is not explicit in the knowledge base. One may be interested not only in facts or rules, but also in reasoning based on a combination of declarative, as well as procedural knowledge. In retrieving explicit knowledge from a knowledge base, one may need extra programs in order to specify what is wanted from the machine. In the case of implicit knowledge representation, this process becomes more complex. The process of asking the machine to produce implicit knowledge based on explicit knowledge in a KB is referred to as reasoning or inferences. Two main categories of inference problems are: subsumption and instance problems. The former is to check whether one concept is more specific than the other. The latter is to check if a given individual is an instance of a particular class [5]. In this survey, whenever reasoning and/or inference are mentioned, they correspond to similar concepts stated above. A knowledge-based system is based on acquired knowledge of a specific domain represented using some formalism. Basic components of a knowledge-based
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