• Journal of Intelligence and Information Systems
• /
• v.27 no.4
• /
• pp.49-71
• /
• 2021

Users who intend to utilize knowledge to actively solve given problems proceed their jobs with cross- and sequential exploration of associated knowledge related each other in terms of certain criteria, such as content relevance. A knowledge map is the diagram or taxonomy overviewing status of currently managed knowledge in a knowledge-base, and supports users' knowledge exploration based on certain relationships between knowledge. A knowledge map, therefore, must be expressed in a networked form by linking related knowledge based on certain types of relationships, and should be implemented by deploying proper technologies or tools specialized in defining and inferring them. To meet this end, this study suggests a methodology for developing the knowledge graph-based knowledge map using the Graph DB known to exhibit proper functionality in expressing and inferring relationships between entities and their relationships stored in a knowledge-base. Procedures of the proposed methodology are modeling graph data, creating nodes, properties, relationships, and composing knowledge networks by combining identified links between knowledge. Among various Graph DBs, the Neo4j is used in this study for its high credibility and applicability through wide and various application cases. To examine the validity of the proposed methodology, a knowledge graph-based knowledge map is implemented deploying the Graph DB, and a performance comparison test is performed, by applying previous research's data to check whether this study's knowledge map can yield the same level of performance as the previous one did. Previous research's case is concerned with building a process-based knowledge map using the ontology technology, which identifies links between related knowledge based on the sequences of tasks producing or being activated by knowledge. In other words, since a task not only is activated by knowledge as an input but also produces knowledge as an output, input and output knowledge are linked as a flow by the task. Also since a business process is composed of affiliated tasks to fulfill the purpose of the process, the knowledge networks within a business process can be concluded by the sequences of the tasks composing the process. Therefore, using the Neo4j, considered process, task, and knowledge as well as the relationships among them are defined as nodes and relationships so that knowledge links can be identified based on the sequences of tasks. The resultant knowledge network by aggregating identified knowledge links is the knowledge map equipping functionality as a knowledge graph, and therefore its performance needs to be tested whether it meets the level of previous research's validation results. The performance test examines two aspects, the correctness of knowledge links and the possibility of inferring new types of knowledge: the former is examined using 7 questions, and the latter is checked by extracting two new-typed knowledge. As a result, the knowledge map constructed through the proposed methodology has showed the same level of performance as the previous one, and processed knowledge definition as well as knowledge relationship inference in a more efficient manner. Furthermore, comparing to the previous research's ontology-based approach, this study's Graph DB-based approach has also showed more beneficial functionality in intensively managing only the knowledge of interest, dynamically defining knowledge and relationships by reflecting various meanings from situations to purposes, agilely inferring knowledge and relationships through Cypher-based query, and easily creating a new relationship by aggregating existing ones, etc. This study's artifacts can be applied to implement the user-friendly function of knowledge exploration reflecting user's cognitive process toward associated knowledge, and can further underpin the development of an intelligent knowledge-base expanding autonomously through the discovery of new knowledge and their relationships by inference. This study, moreover than these, has an instant effect on implementing the networked knowledge map essential to satisfying contemporary users eagerly excavating the way to find proper knowledge to use.

• Journal of Korea Multimedia Society
• /
• v.15 no.2
• /
• pp.215-225
• /
• 2012

In multimedia database, a multidimensional space-based indexing has been used to increase search efficiency. However, this method is inefficient in terms of ubiquity because it uses Euclidean distance as a scale of distance calculation. On the contrary, a metric space-based indexing method, in which metric axiom is prerequisite is widely available because a metric scale other than Euclidean distance could be used. This paper is attempted to propose a way of improving VP-tree, one of the metric space indexing methods. The VP-tree calculates the distance with an object which is ultimately linked to the a leaf node depending on the node fit for the search range from a root node and examines if it is appropriate with the search range. Because search speed decreases as the number of distance calculations at the leaf node increases, however, this paper has proposed a method which uses the latest interface on query object as the base point of trigonometric inequality for improvement after focusing on the trigonometric inequality-based range compression method in a leaf node. This improvement method would be able to narrow the search range and reduce the number of distance calculations. According to a system performance test using 10,000 video data, the new method reduced search time for similar videos by 5-12%, compared to a conventional method.

• Journal of Korean Society for Geospatial Information Science
• /
• v.24 no.4
• /
• pp.59-66
• /
• 2016

For the performance experiments of databases systems with moving object databases, we need moving object trajectory data sets. For example, benchmark data sets of moving object trajectories are required for experiments on query processing of moving object databases. For those reasons, several tools have been developed for generating moving objects in Euclidean spaces or road network spaces. Indoor space differs from outdoor spaces in many aspects and moving object generator for indoor space should reflect these differences. Even some tools were developed to produce virtual moving object trajectories in indoor space, the movements generated by them are not realistic. In this paper, we present a moving object generation tool for indoor space. First, this tool generates trajectories for pedestrians in an indoor space. And it provides a parametric generation of trajectories considering not only speed, number of pedestrians, minimum distance between pedestrians but also type of spaces, time constraints, and type of pedestrians. We try to reflect the patterns of pedestrians in indoor space as realistic as possible. For the reason of interoperability, several geospatial standards are used in the development of the tool.

• Journal of KIISE
• /
• v.41 no.11
• /
• pp.943-957
• /
• 2014

Removing noise, called denoising, is an essential factor for the more intuitive and more accurate results in boundary image matching. This paper deals with a partial denoising problem that tries to allow a limited amount of partial noise embedded in boundary images. To solve this problem, we first define partial denoising time-series which can be generated from an original image time-series by removing a variety of partial noises and propose an efficient mechanism that quickly obtains those partial denoising time-series in the time-series domain rather than the image domain. We next present the partial denoising distance, which is the minimum distance from a query time-series to all possible partial denoising time-series generated from a data time-series, and we use this partial denoising distance as a similarity measure in boundary image matching. Using the partial denoising distance, however, incurs a severe computational overhead since there are a large number of partial denoising time-series to be considered. To solve this problem, we derive a tight lower bound for the partial denoising distance and formally prove its correctness. We also propose range and k-NN search algorithms exploiting the partial denoising distance in boundary image matching. Through extensive experiments, we finally show that our lower bound-based approach improves search performance by up to an order of magnitude in partial denoising-based boundary image matching.

• Journal of KIISE
• /
• v.41 no.10
• /
• pp.762-773
• /
• 2014

As the web of data is increasingly producing large RDFS datasets, it becomes essential in building scalable reasoning engines over large triples. There have been many researches used expensive distributed framework, such as Hadoop, to reason over large RDFS triples. However, in many cases we are required to handle millions of triples. In such cases, it is not necessary to deploy expensive distributed systems because logic program based reasoners in a single machine can produce similar reasoning performances with that of distributed reasoner using Hadoop. In this paper, we propose a scalable RDFS reasoner using logical programming methods in a single machine and compare our empirical results with that of distributed systems. We show that our logic programming based reasoner using a single machine performs as similar as expensive distributed reasoner does up to 200 million RDFS triples. In addition, we designed a meta data structure by decomposing the ontology triples into separate sectors. Instead of loading all the triples into a single model, we selected an appropriate subset of the triples for each ontology reasoning rule. Unification makes it easy to handle conjunctive queries for RDFS schema reasoning, therefore, we have designed and implemented RDFS axioms using logic programming unifications and efficient conjunctive query handling mechanisms. The throughputs of our approach reached to 166K Triples/sec over LUBM1500 with 200 million triples. It is comparable to that of WebPIE, distributed reasoner using Hadoop and Map Reduce, which performs 185K Triples/sec. We show that it is unnecessary to use the distributed system up to 200 million triples and the performance of logic programming based reasoner in a single machine becomes comparable with that of expensive distributed reasoner which employs Hadoop framework.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.2
• /
• pp.103-116
• /
• 2017

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner, which can derive new qualitative spatial knowledge representing both topological and directional relationships between two arbitrary spatial objects in efficient way using Aparch Spark SQL. Apache Spark SQL is well known as a distributed parallel programming environment which provides both efficient join operations and query processing functions over a variety of data in Hadoop cluster computer systems. In our spatial reasoner, the overall reasoning process is divided into 6 jobs such as knowledge encoding, inverse reasoning, equal reasoning, transitive reasoning, relation refining, knowledge decoding, and then the execution order over the reasoning jobs is determined in consideration of both logical causal relationships and computational efficiency. The knowledge encoding job reduces the size of knowledge base to reason over by transforming the input knowledge of XML/RDF form into one of more precise form. Repeat of the transitive reasoning job and the relation refining job usually consumes most of computational time and storage for the overall reasoning process. In order to improve the jobs, our reasoner finds out the minimal disjunctive relations for qualitative spatial reasoning, and then, based upon them, it not only reduces the composition table to be used for the transitive reasoning job, but also optimizes the relation refining job. Through experiments using a large-scale benchmarking spatial knowledge base, the proposed reasoner showed high performance and scalability.

• Journal of the Korean Society for information Management
• /
• v.22 no.2 s.56
• /
• pp.125-145
• /
• 2005

This paper presents a semantic vector space retrieval model incorporating a word sense disambiguation algorithm in an attempt to improve retrieval effectiveness. Nine Korean homonyms are selected for the sense disambiguation and retrieval experiments. The total of approximately 120,000 news articles comprise the raw test collection and 18 queries including homonyms as query words are used for the retrieval experiments. A Naive Bayes classifier and EM algorithm representing supervised and unsupervised learning algorithms respectively are used for the disambiguation process. The Naive Bayes classifier achieved $92\%$ disambiguation accuracy. while the clustering performance of the EM algorithm is $67\%$ on the average. The retrieval effectiveness of the semantic vector space model incorporating the Naive Bayes classifier showed $39.6\%$ precision achieving about $7.4\%$ improvement. However, the retrieval effectiveness of the EM algorithm-based semantic retrieval is $3\%$ lower than the baseline retrieval without disambiguation. It is worth noting that the performances of disambiguation and retrieval depend on the distribution patterns of homonyms to be disambiguated as well as the characteristics of queries.

• The KIPS Transactions:PartD
• /
• v.11D no.5
• /
• pp.1049-1062
• /
• 2004

With the popularization of a World Wide Web (WWW), the quantity of web information has been increased. Therefore, an efficient searching system is needed to offer the exact result of diverse Information to user. Due to this reason, it is important to extract and analysis of user requirements in the distributed information environment. The conventional searching method used the only keyword for the web searching. However, the searching method proposed in this paper adds the context information of keyword for the effective searching. In addition, this searching method extracts keywords by the new keyword extraction method proposed in this paper and it executes the web searching based on a keyword mining profile generated by the extracted keywords. Unlike the conventional searching method which searched for information by a representative word, this searching method proposed in this paper is much more efficient and exact. This is because this searching method proposed in this paper is searched by the example based query included content information as well as a representative word. Moreover, this searching method makes a domain keyword list in order to perform search quietly. The domain keyword is a representative word of a special domain. The performance of the proposed algorithm is analyzed by a series of experiments to identify its various characteristic.

• The KIPS Transactions:PartD
• /
• v.18D no.1
• /
• pp.9-22
• /
• 2011

In this paper we present an efficient solution to rotation?invariant boundary image matching. Computing the rotation-invariant distance between image time-series is a time-consuming process since it requires a lot of Euclidean distance computations for all possible rotations. In this paper we propose a novel solution that significantly reduces the number of distance computations using the envelope-based lower bound. To this end, we first present how to construct a single envelope from a query sequence and how to obtain a lower bound of the rotation-invariant distance using the envelope. We then show that the single envelope-based lower bound can reduce a number of distance computations. This approach, however, may cause bad performance since it may incur a larger lower bound by considering all possible rotated sequences in a single envelope. To solve this problem, we present a concept of rotation interval, and using the rotation interval we generalize the envelope-based lower bound by exploiting multiple envelopes rather than a single envelope. We also propose equi-width and envelope minimization divisions as the method of determining rotation intervals in the multiple envelope approach. Experimental results show that our envelope-based solutions outperform existing solutions by one or two orders of magnitude.

• The Journal of Society for e-Business Studies
• /
• v.11 no.4
• /
• pp.33-48
• /
• 2006

Since the emergence of XML as the standard for data exchange on the Web, XML warehousing technology is required to efficiently support Web business applications such as e-Commerce. When the RDBMS is employed as the storage for XML warehouse, XML materialized views of the XML warehouse could be provided by leveraging the materialized views of the RDBMS Because XML documents are mapped into relational tuples, an XML query defining an XML materialized view needs to be transformed into SQL. If relational materialized views were defined with the transformed SQL statements, the XML materialized view could be obtained just by XML-tagging the tuples of the corresponding relational materialized views. The foremost advantage of such a scheme is that the RDBMS does take care of XML materialized view consistency except XML tagging whenever their source XML documents are updated. In this paper, we proposed such a scheme of providing XML materialized views, and implemented it using a commercial RDBMS equipped with materialized view facility in Java on Windows 2000 Professional environment. XML documents in TPC-W, Web e-Commerce Benchmark, were used in performance experiments. The experimental results showed that our proposed scheme for XML materialized views was very effective.