Combining statistical and semantic approaches to the translation of ontologies and taxonomies

Ontologies and taxonomies are widely used to organize concepts providing the basis for activities such as indexing, and as background knowledge for NLP tasks. As such, translation of these resources would prove useful to adapt these systems to new languages. However, we show that the nature of these resources is significantly different from the "free-text" paradigm used to train most statistical machine translation systems. In particular, we see significant differences in the linguistic nature of these resources and such resources have rich additional semantics. We demonstrate that as a result of these linguistic differences, standard SMT methods, in particular evaluation metrics, can produce poor performance. We then look to the task of leveraging these semantics for translation, which we approach in three ways: by adapting the translation system to the domain of the resource; by examining if semantics can help to predict the syntactic structure used in translation; and by evaluating if we can use existing translated taxonomies to disambiguate translations. We present some early results from these experiments, which shed light on the degree of success we may have with each approac

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

Personalized Financial News Recommendation Algorithm Based on Ontology

AbstractTo deal with the challenge of information overload, in this paper, we propose a financial news recommendation algorithm which help users find the articles that are interesting to read. To settle the ambiguity problem, a new presented OF-IDF method is employed to represent the unstructured text data in the form of key concepts, synonyms and synsets which are all stored in the domain ontology. For users, the recommendation algorithm build the profiles based on their behaviors to detect the genuine interests and predict current interests automatically and in real time by applying the thinking of relevance feedback. Finally, the experiment conducted on a financial news dataset demonstrates that the proposed algorithm significantly outperforms the performance of a traditional recommender

Recommender System Based on Semantic Similarity

In electronic commerce, in order to help users to find their favourite products, we essentially need a system to classify the products based on the user's interests and needs to recommend them to the users. For the same reason the recommendation systems are designed to help finding information in large websites. They are basically developed to offer products to the customers in an automated fashion to help them to do conveniently their shopping. The developing of such systems is important since there are often a large number of factors involved in purchasing a product that would make it difficult for the customer to make the best decision. Finding relationship among users and relationships among products are important issue in these systems. One of relations is similarity. Measure similarity among users and products is used in the pure methods for calculating similarity degree. In this paper, semantic similarity is used to find a set of k nearest neighbours to the target user, or target item. Thus, because of incorporating semantic similarity in the proposed recommendation system, from the experimental results, the high accuracy was obtained on private building company dataset in comparison with state-of-the-art recommender systems.DOI:http://dx.doi.org/10.11591/ijece.v3i6.393

A Novel Cooperation and Competition Strategy Among Multi-Agent Crawlers

Multi-Agent theory which is used for communication and collaboration among focused crawlers has been proved that it can improve the precision of returned result significantly. In this paper, we proposed a new organizational structure of multi-agent for focused crawlers, in which the agents were divided into three categories, namely F-Agent (Facilitator-Agent), As-Agent (Assistance-Agent) and C-Agent (Crawler-Agent). They worked on their own responsibilities and cooperated mutually to complete a common task of web crawling. In our proposed architecture of focused crawlers based on multi-agent system, we emphasized discussing the collaborative process among multiple agents. To control the cooperation among agents, we proposed a negotiation protocol based on the contract net protocol and achieved the collaboration model of focused crawlers based on multi-agent by JADE. At last, the comparative experiment results showed that our focused crawlers had higher precision and efficiency than other crawlers using the algorithms with breadth-first, best-first, etc

concept paper

In this concept paper, we outline our working plan for the next phase of the Corporate Semantic Web project. The plan covers the period from March 2009 to March 2010. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human- centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge. Each of these pillars will yield innovative methods and tools during the project runtime until 2013. We propose a concept draft and a working plan covering the next twelve months for an integrative architecture of a Corporate Semantic Web provided by these three core pillars

Enterprise application reuse: Semantic discovery of business grid services

Web services have emerged as a prominent paradigm for the development of distributed software systems as they provide the potential for software to be modularized in a way that functionality can be described, discovered and deployed in a platform independent manner over a network (e.g., intranets, extranets and the Internet). This paper examines an extension of this paradigm to encompass ‘Grid Services’, which enables software capabilities to be recast with an operational focus and support a heterogeneous mix of business software and data, termed a Business Grid - "the grid of semantic services". The current industrial representation of services is predominantly syntactic however, lacking the fundamental semantic underpinnings required to fulfill the goals of any semantically-oriented Grid. Consequently, the use of semantic technology in support of business software heterogeneity is investigated as a likely tool to support a diverse and distributed software inventory and user. Service discovery architecture is therefore developed that is (a) distributed in form, (2) supports distributed service knowledge and (3) automatically extends service knowledge (as greater descriptive precision is inferred from the operating application system). This discovery engine is used to execute several real-word scenarios in order to develop and test a framework for engineering such grid service knowledge. The examples presented comprise software components taken from a group of Investment Banking systems. Resulting from the research is a framework for engineering servic