Building and Maintaining a Semantically Adequate Lexicon Using Cyc

Although to-do lists are a ubiquitous form of personal task management, there has been no work on intelligent assistance to automate, elaborate, or coordinate a user’s to-dos. Our research focuses on three aspects of intelligent assistance for to-dos. We investigated the use of intelligent agents to automate to-dos in an office setting. We collected a large corpus from users and developed a paraphrase-based approach to matching agent capabilities with to-dos. We also investigated to-dos for personal tasks and the kinds of assistance that can be offered to users by elaborating on them on the basis of substep knowledge extracted from the Web. Finally, we explored coordination of user tasks with other users through a to-do management application deployed in a popular social networking site. We discuss the emergence of Social Task Networks, which link users‘ tasks to their social network as well as to relevant resources on the Web. We show the benefits of using common sense knowledge to interpret and elaborate to-dos. Conversely, we also show that to-do lists are a valuable way to create repositories of common sense knowledge about tasks.

Expert system knowledge represents expertise obtained through formal education, training, and/or experience. Formal education provides deep knowledge of a particular domain; experience and training result in heuristic knowledge. A knowledge base defines the range of information and understanding with which the system is capable of dealing; therefore, its information must be structured and filed for ready access. The objective of this symposium is to address the challenges associated with establishment of valid expert system knowledge, specifically, knowledge to be used by expert system shells. As expert system knowledge is obtained, structured, and stored, it is formulated. In this symposium, knowledge formulation is addressed as a three-phase process: knowledge acquisition, the mechanics associated with structuring knowledge, and knowledge porting. Knowledge acquisition is the process of extracting expertise from a domain expert. Expertise may be collected through a series of interviews between the expert and a knowledge engineer or through sessions the expert holds with an automated knowledge acquisition tool. Thus, the ultimate outcome of knowledge acquisition is a collection of raw knowledge data. The following human factors issues become apparent: documenting mental models (where mental models are the expert's conceptualization of a problem), recording cognitive problem-solving strategies, and specifying an appropriate interface between the domain expert and the acquisition methodology. The knowledge structuring process involves the refinement of raw knowledge data, where knowledge is organized and assigned a semantic structure. One issue that must be considered is how to interpret knowledge data such that formal definitions, logical relationships, and facts can be established. Finally, formulation involves knowledge porting, that is, the movement of an expert system shell's knowledge base to various other shells. The outcome of this process is a portable knowledge base, where the challenges lie in maintaining consistent knowledge, understanding the constraints inherent to a shell (the shell's ability to incorporate all relevant knowledge), and designing an acceptable user-expert system interface. The fundamental component of any expert system is its knowledge base. The issues to be presented in this symposium are important because they address three processes that are critical to the development of a knowledge base. In addition to presenting computer science challenges, knowledge base formulation also presents human factors challenges, for example, understanding cognitive problem-solving processes, representing uncertain information, and defining human-expert system interface problems. This symposium will provide a forum for discussion of both types of challenges.

An expert database system is an expert system which is developed on databases using DBMS technology to manage facts and rules. Frame-based expert systems are widely used as the knowledge representation for such expert systems with large knowledge bases. Many systems have the ability to connect to external databases. Facts stored in databases can be loaded into expert system's knowledge base and inference is performed by the inference engine of the expert system. In many cases, such external facts are required several times for each inference. Thus, a lot of communication traffic takes place. This research work present the design and implementation of a frame-based object-relational database system which has a tight coupling between the expert system and the external knowledge base. The external knowledge base also use frame as its knowledge representation. More over, it has its own inference engine so that inference can be perform on the knowledge base side and the results, not only simple facts, are sent back to the expert system for further inference. A medical consultation system is used as an illustrated example an Oracle lOg object relational DBMS is used as the database platform.

In this paper we analyse the explanation facilities of several expert systems using a framework based on factors relevant to the structure and content of a generated explanation. We compare this with the lack of such facilities in expert system shells, and lay out criteria for an explanation facility within an expert system shell for education. We then describe their realisation in the explores (explanation oriented expert system) shell, where domain-independent templates allow questions to be asked about the domain of any knowledge base being consulted. In response, the shell generates domain-based explanations, with further information available through follow-up questions related to the original question, the generated explanation, and the available knowledge. This facility allows a user to explore and learn about a knowledge domain, and has implications for the use of expert system shells in education.

The goal of this research is to develop a fuzzy logic-based system for a first-cut end-of-line diagnosis function. Based on measured abnormal electrical test data, the system provides the engineers a list of prioritized causes (process steps) for further investigation. The intelligent diagnosis system consists of three major modules: fuzzy modeling, knowledge base and inference engine. Experienced engineers diagnosis knowledge is captured in the knowledge base using fuzzy logic knowledge representation models. Each major processing step's fault possibility is calculated in the inference engine. The intelligent diagnosis system has been validated against 23 real fab cases. Results show that version 2.0 of the system identifies the real causes as the top three causes in 20 cases. Our analysis indicates that the inference engine is robust but the knowledge base is insufficient. Improvement strategy has been to periodically update the knowledge base by field engineers based on lessons learned from the case study.

The use of an expert system shell (EXPERTECH Xi Plus) in the construction of an expert system for the diagnosis of infertility has been evaluated. A module was devised for predicting ovulation from the medical history alone. Two versions of this system were constructed, one using the expert system shell, and the other using QuickBASIC. The two systems have been compared with respect to: (1) ease of construction; (2) ease of knowledge base update; (3) help and explanation facilities; (4) diagnostic accuracy; (5) acceptability to patients and clinicians; (6) user-friendliness and ease of use; (7) use of memory space; and (8) run time. The responses of patients and clinicians were evaluated by questionnaires. The predictions made by the computer systems were compared to the conclusions reached by clinicians and to the "gold standard" of day 21 progesterone. The conclusions of this pilot study are: (1) the construction of this expert system was NOT facilitated by the use of this expert system shell; (2) update of the knowledge base was not facilitated either; (3) the expert system shell offered built-in help and explanation facilities, but as the system increased in complexity these became less useful; (4) after initial adjustment of decision thresholds the diagnostic accuracy of the system equalled that of the clinician; (5) the patient response to computer history-taking was very favorable but much less favorable to computer diagnosis; (6) the clinicians took a positive attitude to computer diagnosis; (7) the systems were easy to use; (8) the expert systems shell required much more memory space and had a much slower response time than the system written in BASIC.

In this paper, we present CogNet, a knowledge base (KB) dedicated to integrating three types of knowledge: (1) linguistic knowledge from FrameNet, which schematically describes situations, objects and events. (2) world knowledge from YAGO, Freebase, DBpedia and Wikidata, which provides explicit knowledge about specific instances. (3) commonsense knowledge from ConceptNet, which describes implicit general facts. To model these different types of knowledge consistently, we introduce a three-level unified frame-styled representation architecture. To integrate free-form commonsense knowledge with other structured knowledge, we propose a strategy that combines automated labeling and crowdsourced annotation. At present, CogNet integrates 1,000+ semantic frames from linguistic KBs, 20,000,000+ frame instances from world KBs, as well as 90,000+ commonsense assertions from commonsense KBs. All these data can be easily queried and explored on our online platform, and free to download in RDF format for utilization under a CC-BY-SA 4.0 license. The demo and data are available at http://cognet.top/.

This paper argues that the best chance of utilising AI & Law models of legal argument in practice in the near future may be in desiging legal argumentation management systems. Such systems do not have a knowledge base and inference engine but allow the user to structure a legal case dossier in terms of the argumentation structure of a case, so that better overview over the dossier is obtained and documents can be better retrieved, compared and drafted. To obtain insight in how such systems should be designed, a case study is presented in which a dossier of a solicitor representing a client in a Dutch civil dispute is analysed according to its argumentation structure. The resulting structures are visualised using the Araucaria software tool. Special attention is paid to the creation of the argumentation structure over time in the course of the dispute, and to the argumentative tactics and strategies employed by the solicitor.

The application of an expert system (expert system) which is very useful for the wider community is certainly very much needed by society today. Apart from that, this development also supports various human activities. Expert systems are also able to make recommendations for a series of user actions. The aim of this research is to determine the development of expert systems in management information systems. The research method used in this research is a descriptive research method with theoretical studies, namely a method that discusses several possibilities for solving actual problems by collecting data, compiling or classifying them, analyzing and interpreting them with existing theories and comparing them from literature sources. The research results show the development of expert systems in management information systems, namely knowledge bases, inference engines, and user interfaces. It can be concluded that expert systems help work and technological progress more quickly and the decision-making process is based on existing information and facts. Expert systems have also been used in various fields, from medical, legal, to financial. In developing an expert system, it is necessary to increase the level of knowledge related to the latest technology and information. So you can utilize modern technology to complete work more effectively and efficiently.

An expert system for selecting the methods for retrofitting fatigue cracking in steel bridges is developed. The expert system involves a knowledge base with new knowledge representations and a new inference engine. The knowledge is based on 90 cases of fatigue cracking in existing steel bridges, and a knowledge base is constructed with knowledge being represented as production relations. All the relations in the knowledge base are assumed to have a certainty factor. The new inference engine was developed in the C language so that it has the ability to learn by using the knowledge‐based network model. The inference engine is able to modify relations with a certainty factor. Therefore, the knowledge‐based network model can grow into an optimum knowledge‐based network. The present system is evaluated for the actual fatigue cracking in three steel bridges. It is found that the system is able to infer reasonable methods for retrofitting cracked members.

Expert systems have now proven their value in a variety of enterprises ranging from medicine to geology. Small expert systems are at work in numerous commercial and industrial sites, but large systems are still rare. Besides the difficulties inherent in the collection of useful and correct domain knowledge, large knowledge bases present special problems in data management. These problems often cause a good pilot system to founder when expanded to an operational size.This paper addresses the difficulties of efficiently handling large knowledge bases. The methods discussed result from viewing a knowledge base as a database and analyzing the knowledge as one would analyze a database application, using Chen's Entity-Relationship (E-R) Diagrams [CHEN76] to build a relational database representation. Besides providing a different perspective on the management of knowledge bases, such a method facilitates the use of a commercial DBMS for this task.Following these ideas the simple assertions of the knowledge base are viewed as one entity type and the rules themselves as another, with IF and THEN being separate relationships between these two types. This decomposition is reasonable for capturing the knowledge represented by the rules and leads to a relational description which is considerably different than usual rule representations. More precisely, the E-R diagram of Figure 1 is taken as the basis of the model, where the rectangles represent entities and the ellipses represent relationships between the entities connected to that ellipse. The unboxed words such as rule# are attributes of the associated entities and correspond to the information to be saved in the database.The E-R diagram in Figure 1 can be converted to the relational database tables shown in Figure 2 where rule# is a name or a number that identifies the rule, if_part is a simple assertion Ai (antecedent), then_part is a simple assertion Ci (conclusion), and conf is a number representing the confidence in that deduction.This results in a very different data organization from the usual collection of knowledge found in rule-based systems. Beyond that analysis, very specific implementation techniques are suggested for knowledge bases, using both standard relational data bases and simpler file-handling techniques such as those available in standard programming languages. Caution must be taken, however, before simply trying to turn knowledge base management over to a commercial DBMS, since the behavior of “queries” within a rule base is very different from that of ordinary database queries. Comparisons are made to such recent works as those of Deering, Debanne, and Bouzegoub which suggest a variety of implementations for knowledge bases.Often in the modeling of a domain, one discovers a hierarchical relation among the basic concepts, vocabulary, or objects of study. Such an IsA or AKindOf hierarchy can be used to create a segmentation of the knowledge base resulting in significant efficiency improvements within the system. This possibility is explored in some detail.Throughout this work, emphasis is given to dynamic rule bases which are subject to frequent changes and which, therefore, require a generalized inference engine and flexible data structures. Often due to execution speed requirements some domain knowledge is encoded as procedures within the inference engine. Although this may improve the efficiency of the system, it undermines the generality and flexibility that are also required. This paper investigates a promising method for incorporating such procedural inclusions within the general knowledge base structure. The approach developed allows the absorption of changes without restructuring of the knowledge base or modifications to the inference engine, and maintains system transparency, simplicity, and expandability.

Wordnets, which are repositories of lexical semantic knowledge containing semantically linked synsets and lexically linked words, are indispensable for work on computational linguistics and natural language processing. While building wordnets for Hindi and Marathi, two major Indo-European languages, we observed that the verb hierarchy in the Princeton Wordnet was rather shallow. We set to constructing a verb knowledge base for Hindi, which arranges the Hindi verbs in a hierarchy of is-a (hypernymy) relation. We realized that there are unique Indian language phenomena that bear upon the lexicalization vs. syntactically derived choice. One such example is the occurrence of conjunct and compound verbs (called Complex Predicates) which are found in all Indian languages. This paper presents our experience in the construction of lexical knowledge bases for Indian languages with special attention to Hindi. The question of storing versus deriving complex predicates has been dealt with linguistically and computationally. We have constructed empirical tests to decide if a combination of two words, the second of which is a verb, is a complex predicate or not. Such tests provide a principled way of deciding the status of complex predicates in Indian language wordnets.

Dual coding of knowledge in the human brain.

Topic models are often used to discover latent semantic patterns from document collections. However, existing unsupervised approaches have the following drawbacks: (1) The mined topics may not match user interests; (2) They are prone to extract semantically similar topics and sacrifice diversity; (3) The mined topics often have low interpretability, which does not meet common sense knowledge. To address these limitations, we propose the Distributed Keyword-guided Topic Model (DiskTM) that incorporates Gaussian-distributed keyword prior knowledge into the modeling process to mine user-interested topics. Furthermore, to inject common-sense knowledge and improve the topic’s interpretability, we extend DiskTM and propose the Distributed Keyword-guided Topic Model with Lexical Knowledge (DiskTM-LK). Experimental results on three publicly available text corpora show that our proposed approaches could extract topics that match user interests (keywords). Moreover, DiskTM and DiskTM-LK could also obtain more coherent and diverse topics, outperforming the state-of-the-art baseline approaches.

A common assumption for logic-based argumentation is that an argument is a pair (Φ, α) where Φ is a minimal subset of the knowledgebase such that Φ is consistent and Φ entails the claim α. However, real arguments (i.e. arguments presented by humans) usually do not have enough explicitly presented premises for the entailment of the claim (i.e. they are enthymemes). This is because there is some common knowledge that can be assumed by a proponent of an argument and the recipient of it. This allows the proponent of an argument to encode an argument into a real argument by ignoring the common knowledge, and it allows a recipient of a real argument to decode it into the intended argument by drawing on the common knowledge. If both the proponent and recipient use the same common knowledge, then this process is straightforward. Unfortunately, this is not always the case, and this raises interesting issues for dialogue systems in which the recipient has to cope with the disparities between the different views on what constitutes common knowledge. Here we investigate the use of enthymemes in inquiry dialogues. For this, we propose a generative inquiry dialogue system and show how, in this dialogue system, enthymemes can be managed by the agents involved, and how common knowledge can evolve through dialogue.