3,050 research outputs found
Semantic Web in Action: Ontology-driven Information Search, Integration and Analysis
Keynote at the Net Object Days and MATES, Erfurt, Germany, September 23, 2003
Visual Exploration System for Analyzing Trends in Annual Recruitment Using Time-varying Graphs
Annual recruitment data of new graduates are manually analyzed by human
resources specialists (HR) in industries, which signifies the need to evaluate
the recruitment strategy of HR specialists. Every year, different applicants
send in job applications to companies. The relationships between applicants'
attributes (e.g., English skill or academic credential) can be used to analyze
the changes in recruitment trends across multiple years' data. However, most
attributes are unnormalized and thus require thorough preprocessing. Such
unnormalized data hinder the effective comparison of the relationship between
applicants in the early stage of data analysis. Thus, a visual exploration
system is highly needed to gain insight from the overview of the relationship
between applicants across multiple years. In this study, we propose the
Polarizing Attributes for Network Analysis of Correlation on Entities
Association (Panacea) visualization system. The proposed system integrates a
time-varying graph model and dynamic graph visualization for heterogeneous
tabular data. Using this system, human resource specialists can interactively
inspect the relationships between two attributes of prospective employees
across multiple years. Further, we demonstrate the usability of Panacea with
representative examples for finding hidden trends in real-world datasets and
then describe HR specialists' feedback obtained throughout Panacea's
development. The proposed Panacea system enables HR specialists to visually
explore the annual recruitment of new graduates
Human Resources Recommender system based on discrete variables
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceNatural Language Processing and Understanding has become one of the most exciting and challenging
fields in the area of Artificial Intelligence and Machine Learning. With the rapidly changing business
environment and surroundings, the importance of having the data transformed in such a way that
makes it easy to interpret is the greatest competitive advantage a company can have. Having said this,
the purpose of this thesis dissertation is to implement a recommender system for the Human
Resources department in a company that will aid the decision-making process of filling a specific job
position with the right candidate. The recommender system fill be fed with applicants, each being
represented by their skills, and will produce a subset of most adequate candidates given a job position.
This work uses StarSpace, a novelty neural embedding model, whose aim is to represent entities in a
common vectorial space and further perform similarity measures amongst them
BUILDING DSS USING KNOWLEDGE DISCOVERY IN DATABASE APPLIED TO ADMISSION & REGISTRATION FUNCTIONS
This research investigates the practical issues surrounding the development and
implementation of Decision Support Systems (DSS). The research describes the traditional
development approaches analyzing their drawbacks and introduces a new DSS development
methodology. The proposed DSS methodology is based upon four modules; needs' analysis,
data warehouse (DW), knowledge discovery in database (KDD), and a DSS module.
The proposed DSS methodology is applied to and evaluated using the admission and
registration functions in Egyptian Universities. The research investigates the organizational
requirements that are required to underpin these functions in Egyptian Universities. These
requirements have been identified following an in-depth survey of the recruitment process in
the Egyptian Universities. This survey employed a multi-part admission and registration DSS
questionnaire (ARDSSQ) to identify the required data sources together with the likely users
and their information needs. The questionnaire was sent to senior managers within the
Egyptian Universities (both private and government) with responsibility for student
recruitment, in particular admission and registration.
Further, access to a large database has allowed the evaluation of the practical suitability of
using a data warehouse structure and knowledge management tools within the decision
making framework. 1600 students' records have been analyzed to explore the KDD process,
and another 2000 records have been used to build and test the data mining techniques within
the KDD process.
Moreover, the research has analyzed the key characteristics of data warehouses and explored
the advantages and disadvantages of such data structures. This evaluation has been used to
build a data warehouse for the Egyptian Universities that handle their admission and
registration related archival data. The decision makers' potential benefits of the data
warehouse within the student recruitment process will be explored.
The design of the proposed admission and registration DSS (ARDSS) will be developed and
tested using Cool: Gen (5.0) CASE tools by Computer Associates (CA), connected to a MSSQL
Server (6.5), in a Windows NT (4.0) environment. Crystal Reports (4.6) by Seagate will
be used as a report generation tool. CLUST AN Graphics (5.0) by CLUST AN software will
also be used as a clustering package.
Finally, the contribution of this research is found in the following areas:
A new DSS development methodology;
The development and validation of a new research questionnaire (i.e. ARDSSQ);
The development of the admission and registration data warehouse;
The evaluation and use of cluster analysis proximities and techniques in the KDD process
to find knowledge in the students' records;
And the development of the ARDSS software that encompasses the advantages of the
KDD and DW and submitting these advantages to the senior admission and registration
managers in the Egyptian Universities.
The ARDSS software could be adjusted for usage in different countries for the same purpose,
it is also scalable to handle new decision situations and can be integrated with other systems
Structuring visual exploratory analysis of skill demand
The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on
NewsReader: Using knowledge resources in a cross-lingual reading machine to generate more knowledge from massive streams of news
Abstract In this article, we describe a system that reads news articles in four different languages and detects what happened, who is involved, where and when. This event-centric information is represented as episodic situational knowledge on individuals in an interoperable RDF format that allows for reasoning on the implications of the events. Our system covers the complete path from unstructured text to structured knowledge, for which we defined a formal model that links interpreted textual mentions of things to their representation as instances. The model forms the skeleton for interoperable interpretation across different sources and languages. The real content, however, is defined using multilingual and cross-lingual knowledge resources, both semantic and episodic. We explain how these knowledge resources are used for the processing of text and ultimately define the actual content of the episodic situational knowledge that is reported in the news. The knowledge and model in our system can be seen as an example how the Semantic Web helps NLP. However, our systems also generate massive episodic knowledge of the same type as the Semantic Web is built on. We thus envision a cycle of knowledge acquisition and NLP improvement on a massive scale. This article reports on the details of the system but also on the performance of various high-level components. We demonstrate that our system performs at state-of-the-art level for various subtasks in the four languages of the project, but that we also consider the full integration of these tasks in an overall system with the purpose of reading text. We applied our system to millions of news articles, generating billions of triples expressing formal semantic properties. This shows the capacity of the system to perform at an unprecedented scale
Crime mapping and spatial analysis
Crime maps are becoming significant tools in crime and justice. Advances in the areas of information
technology and Geographic Information Systems (GIS) have opened new opportunities
for the use of digital mapping in crime control and prevention programs. Crime maps are also
valuable for the study of the ecology and the locational aspects of crime. Maps enable areas of
unusually high or low concentration of crime to be visually identified. Maps are however only
pictorial representations of the results of more or less complex spatial data analyses.
A hierarchical model dealing with crime analysis is proposed and applied to the regional analysis
of crime in Tehran, the model helps to identify spatial concentration of crimes in specific
area (area based method). In area-based methods, crime data are aggregated into geographical
areas such as blocks, precincts, and for each area, the analyst computes a measure of crime
value. Multicriteria evaluation concept has been used to assess the crime rate in various blocks a
discrete (part) of Tehran city. In this part we used two methods for crime density assessment:
• Crime assessment based on crime per block,
• Crime assessment based on density of crime per population.
After determination of hot spots based on two methods mentioned above spatial function is
used to find suitable location to establish new police station or direct patrol to the hot spots to
reduce of crime
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