The growth of technology and sciences has greatly influenced the area of management and decision-making procedures, and has dramatically changed the decision-making processes in different levels, both quantitatively and qualitatively. Knowledge management plays a vital role in supporting enterprise learning, since it facilitates the effective collective intellect of the enterprise. Different methods for user-friendly knowledge access have been developed previously. The most sophisticated ones provide a simple text box for a query which takes Natural Language (NL) queries as input. Question Answering (QA) system is playing an important role in current search engine optimization. Natural language processing technique is mostly implemented in QA system for asking user‟s question and several steps are also followed for conversion of questions to query form for getting an exact answer. Query languages have complex syntax, requiring a good understanding of the representation schema, including knowledge of details like namespaces, class and property names. In this research we proposed an model to implement Conceptual Question Answering and Automatic Information Inferences for the enterprise's operational knowledge management in ontology-based learning organization.
Retrieval and extraction processes - for enterprise management and decision-making have gained an excessive importance as the mass of data and information stored in various resources increases. Knowledge is considered a key factor for enterprise prosperity at present and future. Knowledge management is an integrated, systematic process that applies a suitable combination of information technologies and human cooperation in order to identify, manage and share the information capitals. In addition, it both includes the explicit and implicit knowledge of the staff and it applies various and extensive methods to retrieve, store and share knowledge in a certain enterprise.
The application of “Semantic Web” technologies to learning processes is receiving
an increasing attention from the perspective of facilitating the selection, delivery and
tailoring of learning experiences. But most of the current approaches are centered on
the final interaction of the learner with the “learning objects” provided for him/her,
neglecting the organizational perspective. From the viewpoint of an organization, the
application of Semantic Web technologies should be motivated by the improvement
of learning-oriented mechanisms, including both cultural and structural aspects, and
considering the ideal of achieving a state of continuous improvement in learning
behavior. Such an approach to achieving a “semantic learning organization” gives a
complementary perspective to existing “educational Semantic Web” propositions [
Most knowledge bases provide facilities for querying through the use of some
formal language such as SPARQL or SeRQL. However, these have a fairly complex
syntax, requiring a good understanding of the data schema and being prone to errors
due to the need to type long and complicated URIs. These languages are homologous
to the use of SQL for interrogating traditional relational databases and should not be
seen as an end user tool[
The obvious solution to these problems is to create some additional abstraction level that provides a user friendly way of generating formal queries. It may be possible to infer from this information for the machine so that we can carry out the decision-making and planning procedures in enterprise processes through automatic inference. 2
A basic method to transform an organization into a learning organization is to apply
knowledge management within the organization. By facilitating the process of
creating and sharing knowledge, and through providing positive working environments
and effective rewarding systems, knowledge management accelerates enterprise
learning and helps the enterprise adjust itself to today's rapid changes and hence survive in
pace with these changes[
Probably due to the extraordinary popularity of search engines such as Google, people have come to prefer search interfaces which offer a single text input field where they describe their information need and the system does the required work to find relevant results. While employing this kind of interface is straightforward for full text search systems, using it for conceptual search requires an extra step that converts the user's query into semantic restrictions like those expressed in formal search languages. Following are discussed some examples of such query interfaces.
CLOnE[
The Controlled Language for Ontology Editing (CLOnE) allows users to design, create, and manage information spaces without knowledge of complicated standards (such as XML1, RDF2 and OWL3) or ontology engineering tools. It was implemented as a simplified natural language processor that allows the specification of logical data for semantic knowledge technology purposes in normal language. CLOnE is designed either to accept input as valid or to reject it and warn the user of his errors; because the parsing process is deterministic, the usual IE performance measures (precision and recall) are not relevant.
QACID [
ONLI (Ontology Natural Language Interaction) [
QAAL [
QuestIO [
PANTO [
OntoTriples are finally interpreted as SPARQL form. The performance of PANTO is analyzed by using FMeasure type. At the maximum 88.05% Precision is achieved for Geography domain with tested queries. So this system helps bridge the gap between the real world users with the semantic web based on logic model.
AquaLog [
QASYO [
The whole QA process is composed of two consecutive phases: question analysis and answer retrieval. This model requires both an evaluation of its query answering ability. Another extension is to provide information about the nature and complexity of the possible changes required for the ontology and the linguistic component.
Knowledge management system includes methods for obtaining or gathering information, organizing, distributing and sharing information among the staff in an organization. In this research, the potential role of the Semantic Web Technology as a driver for advanced learning organizations and Question Answering system is focused on providing access to the information stored in a KB by means of natural language queries. 3
The current research is aimed to show that using standard NLP tools, ontology and informal to formal semantic query model proposed in the current research can establish a relationship among various sectors including duties, activities, resources and information structure of a certain enterprise so that managerial requirements can be desirably met through semantic modeling. As a result, we may have a better chance of using this information for the managers and the users through conceptual queries on the information system of the enterprise. In attention to the actual state of semantic web technology and NLP, the recommended path for organizations that are committed to the view of a learning organization is that of first addressing infrastructural elements. Such infrastructures can be considered as the study and provision of the ontologies for each aspect of the semantic learning organization. Therefore, how can we improve knowledge management in enterprises through an appropriate selection based on ontology?. Also, how can we respond to the managerial requirements of the enterprises from simple decisions to strategic ones and how can we perform automatic extraction of the information?. Consequently, the following objectives are followed in parallel with works carried out previously: 1. Conceptual framework for the notion of a semantic learning organization with using semantic search model instead of using normal keyword search model is provided. 2. Designing and presenting a method to translate user´s semantic queries into welldefined queries using the results of NLP Interchange Format (NIF) to answer the semantic questions. 3. The necessity to be robust and ability to deal with all kinds of input including ungrammatical text, sentence fragments, short queries, etc.
The existing organizational architecture is faced with a semantic shortage between
humans and systems for having a precise and general understanding of them, which in
turn causes communication problems between humans and systems or vice versa.
These problems prohibit the materialization of the organizations in an assembled and
concordant form with other organizations [
There is technically too complicated to represent and comprehend the domain for a domain expert who has little knowledge in the well-defined queries. More importantly, from a practical point of view, there is no publicly known robust engine to manage a large KB with practical performance. On the other hand, we should increase the machines' capability in understanding the organizational structure (Intelligentmaking). To this end, having analyzed the existing concepts in the scope of knowledge management of the learning organizations, we reckon the significance of the information capitals of an enterprise through an ontology-based method. Answering to semantic questions will help increase the capability of learning organizations.
The growing interest in Semantic Web applications and need to translate natural language question into a machine-readable format create many uses for such applications. It is implemented as a natural language processor that allows the specification of logical data for semantic knowledge technology purposes in normal language, but with high accuracy and reliability. The components are based on NLP Interchange Format(NIF) with using statistical machine translation method. 5
We designed an initial model to implement Conceptual Question Answering and
Automatic Information Inferences for the enterprise's operational knowledge
management in learning organization. To achieve this goal, we evaluate the SPARQL and
SeRQL languages for semantic search. In [
The main undertaking of the current contribution is to present ongoing work in facilitating learning organizations and their use of ontology-based tools by striving to translate natural language queries into well-defined queries and retrieving exact answers, which in turn can be executed in the framework presented here . A model was introduced to automatically convert semantic query to formal query in a bid to provide answers for conceptual question and to infer information from organizational knowledge base.
Answers are retrieved from ontology using semantic search approach interoperability for NIF components, web services and question-to-query algorithm is evaluated in our system for analyzing performance evaluation. Finally performance of question answering system of getting exact result can be improved by using semantic search methodology to retrieve optimum answers from organizational ontology model.
I would like to thank our software department (LSI) from KEMLG research group in Polytechnic University of Catalonia (UPC). Especially, I would like to thank Dr. Miquel Sànchez-Marrè for his helpful comments and guidance. I acknowledge the financial support of the Generalitat de Catalunya through the AGAUR agency for Consolidated Research Groups. This support (2009SGR 1365) was granted to the Knowledge Engineering & Machine Learning group (KEMLG).