Extraction of ontology and semantic web information from online business reports

CAINES, Content Analysis and INformation Extraction System, employs an information extraction (IE) methodology to extract unstructured text from the Web. It can create an ontology and a Semantic Web. This research is different from traditional IE systems in that CAINES examines the syntactic and semantic relationships within unstructured text of online business reports. Using CAINES provides more relevant results than manual searching or standard keyword searching. Over most extraction systems, CAINES extensively uses information extraction from natural language, Key Words in Context (KWIC), and semantic analysis. A total of 21 online business reports, averaging about 100 pages long, were used in this study. Based on financial expert opinions, extraction rules were created to extract information, an ontology, and a Semantic Web of data from financial reports. Using CAINES, one can extract information about global and domestic market conditions, market condition impacts, and information about the business outlook. A Semantic Web was created from Merrill Lynch reports, 107,533 rows of data, and displays information regarding mergers, acquisitions, and business segment news between 2007 and 2009. User testing of CAINES resulted in recall of 85.91%, precision of 87.16%, and an F-measure of 86.46%. Speed with CAINES was also greater than manually extracting information. Users agree that CAINES quickly and easily extracts unstructured information from financial reports on the EDGAR database

Learning-based Rule-Extraction from Support Vector Machines

In recent years, support vector machines (SVMs) have shown good performance in a number of application areas, including text classification. However, the success of SVMs comes at a cost - an inability to explain the process by which a learning result was reached and why a decision is being made. Rule-extraction from SVMs is important for the acceptance of this machine learning technology, especially for applications such as medical diagnosis. It is crucial for the users to understand how the system makes a decision. In this paper, a novel approach for rule-extraction from support vector machines is presented. This approach handles rule-extraction as a learning task, which proceeds in two steps. The first is to use the labeled patterns from a data set to train an SVM. The second step is to use the generated model to predict the label (class) for an extended data set or different, unlabeled data set. The resulting patterns are then used to train a decision tree learning system and to extract the corresponding rule sets. The output rule sets are verified against available knowledge for the domain problem (e.g. a medical expert), and other classification techniques, to assure correctness and validity of rules

An annotated corpus with nanomedicine and pharmacokinetic parameters

A vast amount of data on nanomedicines is being generated and published, and natural language processing (NLP) approaches can automate the extraction of unstructured text-based data. Annotated corpora are a key resource for NLP and information extraction methods which employ machine learning. Although corpora are available for pharmaceuticals, resources for nanomedicines and nanotechnology are still limited. To foster nanotechnology text mining (NanoNLP) efforts, we have constructed a corpus of annotated drug product inserts taken from the US Food and Drug Administration’s Drugs@FDA online database. In this work, we present the development of the Engineered Nanomedicine Database corpus to support the evaluation of nanomedicine entity extraction. The data were manually annotated for 21 entity mentions consisting of nanomedicine physicochemical characterization, exposure, and biologic response information of 41 Food and Drug Administration-approved nanomedicines. We evaluate the reliability of the manual annotations and demonstrate the use of the corpus by evaluating two state-of-the-art named entity extraction systems, OpenNLP and Stanford NER. The annotated corpus is available open source and, based on these results, guidelines and suggestions for future development of additional nanomedicine corpora are provided

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types