• Journal of Advanced Navigation Technology
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• v.14 no.1
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• pp.108-118
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• 2010

In this paper, we propose Context-based Music Recommendation (COMUS) ontology for modeling user's musical preferences and context and for supporting reasoning about the user's desired emotion and preferences. The COMUS provides an upper Music Ontology that captures concepts about the general properties of music such as title, artists and genre and also provides extensibility for adding domain-specific ontologies, such as Mood and Situation, in a hierarchical manner. The COMUS is music dedicated ontology in OWL constructed by incorporating domain specific classes for music recommendation into the Music Ontology. Using this context ontology, we believe that the use of logical reasoning by checking the consistency of context information, and reasoning over the high-level, implicit context from the low-level, explicit information. As a novelty, our ontology can express detailed and complicated relations among the music, moods and situations, enabling users to find appropriate music for the application. We present some of the experiments we performed as a case-study for music recommendation.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.1
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• pp.91-103
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• 2010

This paper introduces efficient rule-based semantic search techniques to ontology-based knowledge commerce services. Primarily, the search techniques presented in this paper define rules of reasoning that are required for users to search using the concept of ontology, multiple characteristics, relations among concepts and data type. In addition, based on the defined rules, the rule-based reasoning techniques search ontology for knowledge commerce services. This paper explains the conversion rules of query which convert user's query language into semantic search words, and transitivity rules which enable users to search related tags, knowledge products and users. Rule-based sematic search techniques are also presented; these techniques comprise knowledge search modules that search ontology using validity examination of queries, query conversion modules for standardization and expansion of search words and rule-based reasoning. The techniques described in this paper can be applied to sematic knowledge search systems using tags, since transitivity reasoning, which uses tags, knowledge products, and relations among people, is possible. In addition, as related users can be searched using related tags, the techniques can also be employed to establish collaboration models or semantic communities.

• Journal of KIISE:Software and Applications
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• v.36 no.7
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• pp.509-522
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• 2009

The W3C Ontology Working Group has recently developed OWL as an ontology language for the Semantic Web. OWL, however, fails to perform the process of reasoning about temporal knowledge because it lacks full-pleadged semantics for temporal language. Entities in the real world are changing as time passes, while new facts are being introduced as new events occur. KBs without temporal information are incomplete and incorrect. In this paper, we propose an extended temporal ontology language called TL-OWL which provides an abstract syntax and semantics for representing and reasoning about temporal information in the Semantic Web.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.282-296
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• 2010

OWL is an ontology language for the Semantic Web, and suited to modelling the knowledge of a specific domain in the real-world. Ontology also can infer new implicit knowledge from the explicit knowledge. However, the modeled knowledge cannot be complete as the whole of the common-sense of the human cannot be represented totally. Ontology do not concern handling nonmonotonic reasoning to detect incomplete modeling such as the integrity constraints and exceptions. A default rule can handle the exception about a specific class in ontology. Integrity constraint can be clear that restrictions on class define which and how many relationships the instances of that class must hold. In this paper, we propose a practical reasoning system for open and closed-world reasoning that supports a novel hybrid integration of ontology based on open world assumption (OWA) and non-monotonic rule based on closed-world assumption (CWA). The system utilizes a method to solve the problem which occurs when dealing with the incomplete knowledge under the OWA. The method uses the answer set programming (ASP) to find a solution. ASP is a logic-program, which can be seen as the computational embodiment of non-monotonic reasoning, and enables a query based on CWA to knowledge base (KB) of description logic. Our system not only finds practical cases from examples by the Protege, which require non-monotonic reasoning, but also estimates novel reasoning results for the cases based on KB which realizes a transparent integration of rules and ontologies supported by some well-known projects.

• Journal of Korean Medical classics
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• v.22 no.3
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• pp.97-105
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• 2009

We in this paper have constructed herb and formula ontologies. Herb instances and formula instances can be distinguished by nature, used part, effect, disease pattern, symptom, and formula and constituent herb, dosage, effect, disease pattern, symptom, and medical book, respectively. The knowledge for herbs and formulas in ontology is formalized with the distinguishable elements and their relations. Based on the herb and formula ontologies, we propose the three reasoning rules as follows: In herb ontology, the relation between herb and disease can be reasoned if there are the relation between herb and effect, and effect and disease. In formula ontology, there are two reasoning rules. First, if each constituent herb, dosage, effect, disease pattern, and symptom of two formulas is same, it can be reasoned that two formulas are same though the medical books of the formulas are different. Second, if each constituent herb and dosage is same in two formula, it can be reasoned that each formula has all of effects, disease patterns, and symptoms of formulas. In future study, we study other ontologies such as disease ontology with respect to Korean Medicine and define the reasoning rules about the ontologies.

• Journal of KIISE
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• v.43 no.1
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• pp.61-70
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• 2016

In recent years, there has been a need for techniques for large-scale ontology inference in order to infer new knowledge from existing knowledge at a high speed, and for a diversity of semantic services. With the recent advances in distributed computing, developments of ontology inference engines have mostly been studied based on Hadoop or Spark frameworks on large clusters. Parallel programming techniques using GPGPU, which utilizes many cores when compared with CPU, is also used for ontology inference. In this paper, by combining the advantages of both techniques, we propose a new method for reasoning large RDFS ontology data using a Spark in-memory framework and inferencing distributed data at a high speed using GPGPU. Using GPGPU, ontology reasoning over high-capacity data can be performed as a low cost with higher efficiency over conventional inference methods. In addition, we show that GPGPU can reduce the data workload on each node through the Spark cluster. In order to evaluate our approach, we used LUBM ranging from 10 to 120. Our experimental results showed that our proposed reasoning engine performs 7 times faster than a conventional approach which uses a Spark in-memory inference engine.

• Journal of KIISE
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• v.43 no.1
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• pp.87-95
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• 2016

Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.1
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• pp.13-20
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• 2011

LBS services is a user-centric location based information service, where its importance has been discussed as an essential engine in an Ubiquitous Age. We aimed to develop an ontology reasoning system that enables users to derive recommended results suitable through selection standard reasoning according to various users' preferences. In order to achieve this goal, we designed the Geo-ontology system which enabled the construction of personal characteristics of users, knowledge on personal preference and knowledge on spatial and geographical preference. We also integrated a function of reasoning relevant information through the construction of Cost Value ontology using multi-criteria decision making by giving weight according to users' preference.

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.928-935
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• 2009

Ontology schema reasoning is used to maintain consistency of concepts and build concept hierarchy automatically. For the purpose, the search of concepts must be inevitably performed. Ontology schema reasoning performs the test of subsumption relationships of all the concepts delivered in the test set. The result of subsumption tests is determined based on the creation of complete graphs, which seriously weighs with the performance of reasoning. In general, the process of creating complete graph has been known as expressive procedure. This process is essential in improving the leading performance. In this paper, we propose a method enhancing the classification performance by identifying unnecessary subsumption test supported by optimized searching method on subsumption relationship test among concepts. It is achieved by propagating subsumption tests results into other concept.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.133-150
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• 2007

In this paper we investigate a trend analysis service using Semantic Web technology in a news domain. The trend analysis service can provide more intelligent answers rather than the answer given In current news search engines since it can analyze the passage of time and the relation among news. In order to provide the trend analysis service, the capability of temporal reasoning is required, but the Semantic Web language such as OWL does not support the reasoning capability. Therefore, we propose a language TL-OWL(Temporal Web Ontology Language) extending OWL with the temporal reasoning.