Natural language query translation for semantic search

Querying semantic knowledge base often requires the understanding of the ontology schema and proficiency with the query language. Several approaches have existed but mainly dealing with the disambiguation problem which are solved by executing clarification dialogues. This paper addresses the automatic translation of natural language queries into its SPARQL equivalent statement without involving clarification dialogues. We demonstrate that this is achieveable by annotating all ontology concepts in the query. Next the connections between the classes are identified so that the shared properties can be loaded before they are matched with the terms in the query. Then, the identified ontology triples are arranged to construct a valid SPARQL query according to their relation in the ontology schema. We compare the performance of MyAutoSPARQL against FREyA, an NLI that utilizes clarification dialogue. We evaluate our approach on selection typed queries and compare the performance against FREyA. The results show that despite the absent of clarification dialogues, MyAutoSPARQL performance is better than FREyA

The Development of an Ontology-Based Model for Manpower Planning

Manpower planning is complex and demanding task, because in establishing the factual insights of an enterprise, one is required to have the in-depth knowledge of forecasting manpower planning and practices, as well as the knowledge of macroeconomics of the particular business involved. Inconsistency information and lack of knowledge during decision-making process could generate to inaccurate decision. In addition, massive amount of information in unstructured forms need to be managed into a systematic manner. The aim of this research is to develop a generic ontology-based architecture for supporting manpower planning and proves the effectiveness of integrating information extraction from diverse source in supporting information for manpower forecasting. Ontology is built to capture and structure domain expert knowledge based on criteria and preferences for selecting manpower forecasting adjustment. Currently, the framework is under implementation as a research prototype

Application of knowledge-based system in automated data warehouse design

Data warehouse has become more and more popular for an enterprise as a data repository system.Yet tools to appropriately design its conceptual model are rarely available, even though this model is known as a key for the successful of the overall design. In this paper we propose an approach and a tool to guide the decision makers in designing data warehouse conceptual model based on the Entity Relationship (ER) model of the existing operational database systems. Using this approach, the ER model is automatically transformed into the multidimensional model

Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems

Recent years have seen a significant growth in social tagging systems, which allow users to use their own generated tags to organize, categorize, describe and search digital content on social media. The growing popularity of tagging systems is leading to an increasing need for automatic generation of recommended items for users. Much previous research focuses on incorporating recommender techniques in social tagging systems to support the suggestion of suitable tags for annotating related items. Collaborative filtering is one such technique. The most critical task in collaborative filtering is finding related users with similar preferences, i.e., “liked-minded” users. Despite the popularity of collaborative filtering, it still suffers from certain limitations in relation to “cold-start” users, for example, where often there are insufficient preferences to make recommendations. Moreover, there is the data-sparsity problem, where there is limited user feedback data to identify similarities in users’ interests because there is no intersection between users’ transactional data a situation which also results in degraded recommendation quality. For this reason, in this paper we present a new collaborative filtering approach based on users’ semantic tags, which calculates the similarity between users by discovering the semantic spaces in their posted tags. We believe that this approach better reflects the semantic similarity between users according to their tagging perspectives and consequently improves recommendations through the identification of semantically related items for each user. Our experiment on a real-life dataset shows that the proposed approach outperforms the traditional user-based collaborative filtering approach in terms of improving the quality of recommendations

i-JEN: Visual interactive Malaysia crime news retrieval system

Supporting crime news investigation involves a mechanism to help monitor the current and past status of criminal events. We believe this could be well facilitated by focusing on the user interfaces and the event crime model aspects. In this paper we discuss on a development of Visual Interactive Malaysia Crime News Retrieval System (i-JEN) and describe the approach, user studies and planned, the system architecture and future plan. Our main objectives are to construct crime-based event; investigate the use of crime-based event in improving the classification and clustering; develop an interactive crime news retrieval system; visualize crime news in an effective and interactive way; integrate them into a usable and robust system and evaluate the usability and system performance. The system will serve as a news monitoring system which aims to automatically organize, retrieve and present the crime news in such a way as to support an effective monitoring, searching, and browsing for the target users groups of general public, news analysts and policemen or crime investigators. The study will contribute to the better understanding of the crime data consumption in the Malaysian context as well as the developed system with the visualisation features to address crime data and the eventual goal of combating the crimes