• International journal of advanced smart convergence
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• v.8 no.2
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• pp.94-101
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• 2019

Building a stable knowledge base is an important issue in the application of knowledge engineering. In this paper, we present an algorithm for detecting and locating discrepancies in the line of the reasoning process especially when discrepancies occur on belief values. This includes backtracking the rule firing from a goal node of the rule network. Retracting a belief function allows the current belief state to move back to another belief state without the rule firing. It also gives an estimate, called contribution measure, of how much the rule has an impact on the current belief state. Examining the measure leads the expert to locate the possible cause of problem in the rule. For non-monotonic reasoning, the belief retraction method moves the belief state back to the previous state. A tracing algorithm is presented to identify and locate the cause of problem. This also gives repair suggestions for rule refinement.

• Journal of KIISE
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• v.45 no.2
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• pp.113-125
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• 2018

Recently, with the advent of the Big Data era, we have gained the capability of acquiring vast amounts of knowledge from various fields. The collected knowledge is expressed by well-formed formula and in particular, OWL, a standard language of ontology, is a typical form of well-formed formula. The symbolic reasoning is actively being studied using large amounts of ontology data for extracting intrinsic information. However, most studies of this reasoning support the restricted rule expression based on Description Logic and they have limited applicability to the real world. Moreover, knowledge management for inaccurate information is required, since knowledge inferred from the wrong information will also generate more incorrect information based on the dependencies between the inference rules. Therefore, this paper suggests that the SWAT, knowledge management system should be combined with the SWRL (Semantic Web Rule Language) reasoning based on ATMS (Assumption-based Truth Maintenance System). Moreover, this system was constructed by combining with SWRL reasoning and ATMS for managing large ontology data based on the distributed In-memory framework. Based on this, the ATMS monitoring system allows users to easily detect and correct wrong knowledge. We used the LUBM (Lehigh University Benchmark) dataset for evaluating the suggested method which is managing the knowledge through the retraction of the wrong SWRL inference data on large data.

• Archives of Craniofacial Surgery
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• v.11 no.1
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• pp.28-32
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• 2010

Purpose: In accordance with the increasing number of accidents caused by various reasons and recently developed fine diagnostic skills, the incidence of orbital blow-out fracture cases is increasing. As it causes complications, such as diplopia and enophthalmos, surgical reduction is commonly required. This article reports a retrospective series of 5 blow-out fracture cases that had unusual nerve injuries after reduction operations. We represents the clinical experiences about treatment process and follow-up. Methods: From January 2000 to August 2009, we treated total 705 blow-out fracture patients. Among them, there were 5 patients (0.71%) who suffered from postoperative neurologic complications. In all patients, the surgery was performed with open reduction with insertion of $Medpor^{(R)}$. Clinical symptoms and signs were a little different from each other. Results: In case 1, the diagnosis was oculomotor nerve palsy. The diagnosis of the case 2 was superior orbital fissure syndrome, case 3 was abducens nerve palsy, and case 4 was idiopathic supraorbital nerve injury. The last case 5 was diagnosed as optic neuropathy. Most of the causes were extended fracture, especially accompanied with medial and inferomedial orbital blow-out fracture. Extensive dissection and eyeball swelling, and over-retraction by assistants were also one of the causes. Immediately, we performed reexploration procedure to remove hematomas, decompress and check the incarceration. After that, we checked VEP (visual evoked potential), visual field test, electromyogram. With ophthalmologic test and followup CT, we can rule out the orbital apex syndrome. We gave $Salon^{(R)}$ (methylprednisolone, Hanlim pharmaceuticals) 500 mg twice a day for 3 days and let them bed rest. After that, we were tapering the high dose steroid with $Methylon^{(R)}$ (methylprednisolon 4 mg, Kunwha pharmaceuticals) 20 mg three times a day. Usually, it takes 1.2 months to recover from the nerve injury. Conclusion: According to the extent of nerve injury after the surgery of orbital blow-out fracture, the clinical symptoms were different. The most important point is to decide quickly whether the optic nerve injury occurred or not. Therefore, it is necess is to diagnose the nerve injury immediately, perform reexploration for decompression and use corticosteroid adequately. In other words, the early diagnosis and treatment is most important.