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In the era of big data, how to obtain useful knowledge from online news and utilize it as an important basis to make investment decision has become the hotspot of industrial and academic research. At present, there have been research and practice on explicit knowledge acquisition from news, but tacit knowledge acquisition is still under exploration. Based on the general mechanism of domain knowledge, knowledge reasoning, and knowledge discovery, this paper constructs a framework for discovering tacit knowledge from news and applying the knowledge to stock forecasting. The concrete work is as follows: First, according to the characteristics of financial field and the conceptual cube, the conceptual structure of industry–company–product is constructed, and the framework of domain ontology is put forward. Second, with the construction of financial field ontology, the financial news knowledge management framework is proposed. Besides, with the application of attributes in ontology and domain rules extracted from news text, the knowledge reasoning mechanism of financial news is constructed to achieve financial news knowledge discovery. Finally, news knowledge that reflects important information about stock changes is integrated into the traditional stock price forecasting model and the newly proposed model performs well in the empirical analysis of polyester industry.

In the medical field, ontologies are mainly used to standardise the coding of knowledge, either during the drafting phase of documents or during subsequent processing intended to give them a format which makes them usable for automatic processing. In this sense, they have a normative role analogous to the classical medical terminologies (in particular thesauri): to set up a common vocabulary and to make use of shared representations and concepts, in order to allow the interoperability of documents and medicals information systems. Medical and biomedical data play a very important role in the medical field. They solve many of the problems encountered during the diagnosis of diseases and in the prescription medical treatments. The objective of this paper is to develop ontology for the field of medical analyses in an attempt to facilitate the tasks of the various actors in the medical field, namely: doctors, laboratory assistants and patients. It also endeavours to improve the care of patients and facilitate the various acts of health professionals, the purpose of which is to aid in decision-making in the treatment of a disease. To this end, we followed the so-called METHONTOLOGY based on the phases ranging from the needs specification to the evaluation and documentation of this ontology of medical analysis.

With the exponential and rapid growth of online resources in recent years, there has been a huge increase in the use of search engines; these are also one of the most common ways to navigate the Web content without taking into account, in general, the request meaning by which was successfully added the user’s webpage provides us with a lot of results. This problem has led to the integration of semantics in the search for information on the Web (Semantic Web). The use of semantic tools, such as ontology, WordNet dictionary, semantic similarity measure, etc., has contributed to the semantic search development and more particularly, semantic Metaⁿ-search. The success of semantic search is closely linked to the availability of domain ontologies. The objective of this paper is to propose a double model of repetitive semantic search, called Double Metaⁿ-Semantic Search Model (2${\infty }^n$-SSM). On the one hand, it is assisted and based on the concepts extracted from the user’s search domain ontology, which will permit the user to choose a concept from this list of concepts and launch their search; on the other hand, it is free, in that the user enters their own concept and launches their search. This is based on WordNet tool, user’s same search domain ontology and the semantic similarity calculation techniques between concepts in the same ontology. The result of this model is a set of URL links. The term Metaⁿ indicates that the search is done in depth (${\infty }^n$-SS) via choosing each time a URL result by the user. Its experimentation in the asthma disease field gave very promising results in quantity and quality of information via the URL link results (semantic support).

This paper proposes an advanced searching method, aimed at improving Web Information Systems by adopting semantic technology solutions. In particular, it first illustrates the main solutions for semantic search and then proposes the semantic search method $SemSi{m}^{+}$ that represents an evolution of the original SemSim method. The latter is based on the annotation of the resources in a given search space by means of Ontology Feature Vectors ($OFV$), built starting from a reference ontology. Analogously, a user request is expressed as a set of keywords (concepts) selected from the reference ontology, that represent the desired characteristics of the searched resources. Then, the searching method consists in extracting the resources having the $OFV$ that exhibit the highest conceptual similarity to the user request. The new method, $SemSi{m}^{+}$, improves the above mechanism by enriching the $OFV$ with scores. In the user request, a score (High, Medium, Low) is associated with a concept and indicates the preference (i.e., the priority) that the user assigns to the different concepts in searching for resources. In the resource annotation, the score indicates the level of quality of the concept used to characterize the resource. The $SemSi{m}^{+}$ method has been experimented and the results show that it outperforms the SemSim method and, therefore, also the most representative similarity methods proposed in the literature, as already shown in previous works of the authors.

Currently, rapidly locating relevant data sources and assembling the most appropriate data on the Internet is a tremendously difficult task for researchers. To address these issues, this paper proposes the development of a high availability ontology-based water environmental data retrieval and visualization system (OntoWE). The OntoWE prototypes have two significant features. Firstly, it establishes a water environment domain ontology to offer facilities to alleviate semantic heterogeneity and associate semantic information with the data retrieval process. Secondly, it embraces the capability of Service Oriented Architectures (SOA) and Silverlight to leverage the latest protocols of several open web service standards with the Managed Extensibility Framework (MEF). This system provides a centralized and easy-to-use interactive system which enables users to do a one-stop search, access and visualize different types of water data in a single environment. The feasibility and effectiveness of OntoWE system was demonstrated through several investigations about water quality data discovery and retrieval at basin scale in China.

This paper proposes an advanced searching method, aimed at improving Web Information Systems by adopting semantic technology solutions. In particular, it first illustrates the main solutions for semantic search and then proposes the semantic search method SemSim⁺ that represents an evolution of the original SemSim method. The latter is based on the annotation of the resources in a given search space by means of Ontology Feature Vectors (OFV), built starting from a reference ontology. Analogously, a user request is expressed as a set of keywords (concepts) selected from the reference ontology, that represent the desired characteristics of the searched resources. Then, the searching method consists in extracting the resources having the OFV that exhibit the highest conceptual similarity to the user request. The new method, SemSim⁺, improves the above mechanism by enriching the OFV with scores. In the user request, a score (High, Medium, Low) is associated with a concept and indicates the preference (i.e., the priority) that the user assigns to the different concepts in searching for resources. In the resource annotation, the score indicates the level of quality of the concept used to characterize the resource. The SemSim⁺ method has been experimented and the results show that it outperforms the SemSim method and, therefore, also the most representative similarity methods proposed in the literature, as already shown in previous works of the authors.

To overcome short queries and word mismatching problems, several query expansion techniques were proposed to help users formulate a better query to obtain the appropriate information satisfying their needs. This paper introduces a query expansion system called Arabic Legal query Expansion System (ALES) for information retrieval in Arabic legal databases. The developed approach exploits information coming from Arabic domain ontology.