Creating an Intelligent System for Bankruptcy Detection: Semantic data Analysis Integrating Graph Database and Financial Ontology

In this paper, we propose a novel intelligent methodology to construct a Bankruptcy Prediction Computation Model, which is aimed to execute a company’s financial status analysis accurately. Based on the semantic data analysis and management, our methodology considers the Semantic Database System as the core of the system. It comprises three layers: an Ontology of Bankruptcy Prediction, Semantic Search Engine, and a Semantic Analysis Graph Database

Ontological Services Using Crowdsourcing

This paper develops a service for ontology evolution based on crowdsourcing. The approach is demonstrated using OntoAssist, a specially designed semantic search service that is capable of capturing and disambiguating user’s search intent as well as automatically enabling ontology evolution. Successful and consistent ontology evolution often requires large amount of input data to specify new terms or changes in relationships. These inputs typically come mainly from domain experts or ontology professionals, which makes it hard to keep up with the change of open, dynamic World Wide Web environment. By integrating OntoAssist with an existing search engine, we show that users’ search intent can be disambiguated and aggregated to help to evolve underlying ontology. The disambiguation feature helps the users to find desirable search results. OntoAssist has been implemented and tested by Turkers from Amazon Mechanical Turk in a live demonstration site. Promising results and analysis are reported

User centric web search using ontology

Semantic information retrieval systems query the World Wide Web based on context information, and are intended to provide more pertinent search results. However, most of the existing systems overlook one important aspect ‘the user’. They are more focused on eliminating the obscure results that a conventional or non-semantic search engine would throw up and hence, they are pretty much static. On the other hand, our effort would channel its focus more towards providing a more user-centric service using ontology and involving learning and prediction. By studying the usage statistics of the user, context information can be built and used effectively to produce better search results. Such an approach also entails that the knowledge that is accrued, be organized such that the relationships between the data elements can be elicited easily and unambiguously. The ontology would be described using OWL. Latent semantic indexing algorithm is used for context analysis and retrieva

Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion

Computational Modelling for Bankruptcy Prediction: Semantic data Analysis Integrating Graph Database and Financial Ontology

In this paper, we propose a novel intelligent methodology to construct a Bankruptcy Prediction Computation Model, which is aimed to execute a company's financial status analysis accurately. Based on the semantic data analysis and management, our methodology considers Semantic Database System as the core of the system. It comprises three layers: an Ontology of Bankruptcy Prediction, Semantic Search Engine, and a Semantic Analysis Graph Database system. The Ontological layer defines the basic concepts of the financial risk management as well as the objects that serve as sources of knowledge for predicting a company's bankruptcy. The Graph Database layer utilises a powerful semantic data technology, which serves as a semantic data repository for our model. The article provides a detailed description of the construction of the Ontology and its informal conceptual representation. We also present a working prototype of the Graph Database system, constructed using the Neo4j application, and show the connection between well-known financial ratios. We argue that this methodology which utilises state of the art semantic data management mechanisms enables data processing and relevant computations in a more efficient way than approaches using the traditional relational database. These give us solid grounds to build a system that is capable of tackling the data of any complexity level

Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings