In this paper, we describe the construction of TeKnowbase, a knowledge-base of technical concepts in computer science. Our main information sources are technical websites such as Webopedia and Techtarget as well as Wikipedia and online textbooks. We divide the knowledge-base construction problem into two parts -- the acquisition of entities and the extraction of relationships among these entities. Our knowledge-base consists of approximately 100,000 triples. We conducted an evaluation on a sample of triples and report an accuracy of a little over 90 . We additionally conducted classification experiments on StackOverflow data with features from TeKnowbase and achieved improved classification accuracy.
* Entity extraction One of the important aspects of building domain-specific knowledge-bases is that a dictionary of terms that are relevant to the domain should be acquired. It is possible that such dictionaries are already available (for example, lists of people), but for others, we need techniques to build this dictionary. @cite_11 gives an overview of supervised and unsupervised methods to recognize entities from text. We follow a more straightforward approach -- we specifically target technology websites and write wrappers to extract a list of entities related to computer science.
@cite_11
1541691357 2013109830 2807800730 1968067123
The integration of facts derived from information extraction systems into existing knowledge bases requires a system to disambiguate entity mentions in the text. This is challenging due to issues such as non-uniform variations in entity names, mention ambiguity, and entities absent from a knowledge base. We present a state of the art system for entity disambiguation that not only addresses these challenges but also scales to knowledge bases with several million entries using very little resources. Further, our approach achieves performance of up to 95 on entities mentioned from newswire and 80 on a public test set that was designed to include challenging queries. The recognition of names and their associated categories within unstructured text traditionally relies on semantic lexicons and gazetteers. The amount of effort required to assemble large lexicons confines the recognition to either a limited domain (e.g., medical imaging), or a small set of pre-defined, broader categories of interest (e.g., persons, countries, organizations, products). This constitutes a serious limitation in an information seeking context. In this case, the categories of potential interest to users are more diverse (universities, agencies, retailers, celebrities), often refined (e.g., SLR digital cameras, programming languages, multinational oil companies), and usually overlapping (e.g., the same entity may be concurrently a brand name, a technology company, and an industry leader). We present a lightly supervised method for acquiring named entities in arbitrary categories. The method applies lightweight lexico-syntactic extraction patterns to the unstructured text of Web documents. The method is a departure from traditional approaches to named entity recognition in that: 1) it does not require any start-up seed names or training; 2) it does not encode any domain knowledge in its extraction patterns; 3) it is only lightly supervised, and data-driven; 4) it does not impose any a-priori restriction on the categories of extracted names. We illustrate applications of the method in Web search, and describe experiments on 500 million Web documents and news articles. Abstract Textual information is becoming available in abundance on the web, arising the requirement of techniques and tools to extract the meaningful information. One of such an important information extraction task is Named Entity Recognition and Classification. It is the problem of finding the members of various predetermined classes, such as person, organization, location, date time, quantities, numbers etc. The concept of named entity extraction was first proposed in Sixth Message Understanding Conference in 1996. Since then, a number of techniques have been developed by many researchers for extracting diversity of entities from different languages and genres of text. Still, there is a growing interest among research community to develop more new approaches to extract diverse named entities which are helpful in various natural language applications. Here we present a survey of developments and progresses made in Named Entity Recognition and Classification research. The usual approach to named-entity detection is to learn extraction rules that rely on linguistic, syntactic, or document format patterns that are consistent across a set of documents. However, when there is no consistency among documents, it may be more effective to learn document-specific extraction rules.This paper presents a knowledge-based approach to learning rules for named-entity extraction. Document-specific extraction rules are created using a generate-and-test paradigm and a database of known named-entities. Experimental results show that this approach is effective on Web documents that are difficult for the usual methods.