Finding Structured and Unstructured Features to Improve the Search Result of Complex Question

-Recently, search engine got challenge deal with such a natural language questions. Sometimes, these questions are complex questions. A complex question is a question that consists several clauses, several intentions or need long answer. In this work we proposed that finding structured features and unstructured features of questions and using structured data and unstructured data could improve the search result of complex questions. According to those, we will use two approaches, IR approach and structured retrieval, QA template. Our framework consists of three parts. Question analysis, Resource Discovery and Analysis The Relevant Answer. In Question Analysis we used a few assumptions, and tried to find structured and unstructured features of the questions. Structured feature refers to Structured data and unstructured feature refers to unstructured data. In the resource discovery we integrated structured data (relational database) and unstructured data (webpage) to take the advantaged of two kinds of data to improve and reach the relevant answer. We will find the best top fragments from context of the webpage In the Relevant Answer part, we made a score matching between the result from structured data and unstructured data, then finally used QA template to reformulate the question. In the experiment result, it shows that using structured feature and unstructured feature and using both structured and unstructured data, using approach IR and QA template could improve the search result of complex questions

Comparative analysis of knowledge representation and reasoning requirements across a range of life sciences textbooks.

BackgroundUsing knowledge representation for biomedical projects is now commonplace. In previous work, we represented the knowledge found in a college-level biology textbook in a fashion useful for answering questions. We showed that embedding the knowledge representation and question-answering abilities in an electronic textbook helped to engage student interest and improve learning. A natural question that arises from this success, and this paper's primary focus, is whether a similar approach is applicable across a range of life science textbooks. To answer that question, we considered four different textbooks, ranging from a below-introductory college biology text to an advanced, graduate-level neuroscience textbook. For these textbooks, we investigated the following questions: (1) To what extent is knowledge shared between the different textbooks? (2) To what extent can the same upper ontology be used to represent the knowledge found in different textbooks? (3) To what extent can the questions of interest for a range of textbooks be answered by using the same reasoning mechanisms?ResultsOur existing modeling and reasoning methods apply especially well both to a textbook that is comparable in level to the text studied in our previous work (i.e., an introductory-level text) and to a textbook at a lower level, suggesting potential for a high degree of portability. Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook. We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important.ConclusionsWith some additional work, our representation methodology would be applicable to a range of textbooks. The requirements for knowledge representation are common across textbooks, suggesting that a shared semantic infrastructure for the life sciences is feasible. Because our representation overlaps heavily with those already being used for biomedical ontologies, this work suggests a natural pathway to include such representations as part of the life sciences curriculum at different grade levels

Knowledge Rich Natural Language Queries over Structured Biological Databases

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made

Finding Structured and Unstructured Features to Improve the Search Result of Complex Question

The current researches on question answer usually achieve the answer only from unstructured text resources such as collection of news or pages. According to our observation from Yahoo!Answer, users sometimes ask in complex natural language questions which contain structured and unstructured features. Generally, answering the complex questions needs to consider not only unstructured but also structured resource. In this work, researcher propose a new idea to improve accuracy of the answers of complex questions by recognizing the structured and unstructured features of questions and them in the web. Our framework consists of three parts: Question Analysis, Resource Discovery, and Analysis of The Relevant Answer. In Question Analysis researcher used a few assumptions and tried to find structured and unstructured features of the questions. In the resource discovery researcher integrated structured data (relational database) and unstructured data (web page) to take the advantage of two kinds of data to improve and to get the correct answers. We can find the best top fragments from context of the relevant web pages in the Relevant Answer part and then researcher made a score matching between the result from structured data and unstructured data, then finally researcher used QA template to reformulate the questions. Penelitian yang ada pada saat ini mengenai Question Answer (QA) biasanya mendapatkan jawaban dari sumber teks yang tidak terstruktur seperti kumpulan berita atau halaman. Sesuai dengan observasi peneliti dari pengguna Yahoo!Answer, biasanya mereka bertanya dalam natural language yang sangat kompleks di mana mengandung bentuk yang terstruktur dan tidak terstruktur. Secara umum, menjawab pertanyaan yang kompleks membutuhkan pertimbangan yang tidak hanya sumber tidak terstruktur tetapi juga sumber yang terstruktur. Pada penelitian ini, peneliti mengajukan suatu ide baru untuk meningkatkan keakuratan dari jawaban pertanyaan yang kompleks dengan mengenali bentuk terstruktur dan tidak terstruktur dan mengintegrasikan keduanya di web. Framework yang digunakan terdiri dari tiga bagian: Question Analysis, Resource Discovery, dan Analysis of The Relevant Answer. Pada Question Analysis peneliti menggunakan beberapa asumsi dan mencoba mencari bentuk data yang terstruktur dan tidak terstruktur. Dalam penemuan sumber daya, peneliti mengintegrasikan data terstruktur (relational database) dan data tidak terstruktur (halaman web) untuk mengambil keuntungan dari dua jenis data untuk meningkatkan dan untuk mencapai jawaban yang benar. Peneliti dapat menemukan fragmen atas terbaik dari konteks halaman web pada bagian Relevant Answer dan kemudian peneliti membuat pencocoka skor antara hasil dari data terstruktur dan data tidak terstruktur. Terakhir peneliti menggunakan template QA untuk merumuskan pertanyaan

Developing a question answering system for the slovene language

In todayćs world the majority of information is sought after on the internet. A common method is the use of search engines. However since the result of a query to the search engine is a ranked list of results, this is not the final step. It is up to the user to review the results and determine which of the results provides the information needed. Often this process is time consuming and does not provide the sought after information. Besides the number of returned results the limiting factor is often the lack of ability of the usersto form the correct query. The solution for this can be found in the formof question answering systems, where the user proposes a question in the natural language, similarly as talking to another person. The answer is the exact answer instead of a list of possible results. This paper presents the design of a question answering system in natural slovene language. The system searches for the answers for our target domain (Faculty of Electrical Engineering and Computer Science) with the use of a local database, databases of the facultyćs information system, MS Excel files and through web service calls. We have developed two separate applications: one for users and the other for the administrators of the system. With the help of the latter application the administrators supervise the functioning and use of entire system. The former application is actually the system that answers the questions

DBMSs Should Talk Back Too

Natural language user interfaces to database systems have been studied for several decades now. They have mainly focused on parsing and interpreting natural language queries to generate them in a formal database language. We envision the reverse functionality, where the system would be able to take the internal result of that translation, say in SQL form, translate it back into natural language, and show it to the initiator of the query for verification. Likewise, information extraction has received considerable attention in the past ten years or so, identifying structured information in free text so that it may then be stored appropriately and queried. Validation of the records stored with a backward translation into text would again be very powerful. Verification and validation of query and data input of a database system correspond to just one example of the many important applications that would benefit greatly from having mature techniques for translating such database constructs into free-flowing text. The problem appears to be deceivingly simple, as there are no ambiguities or other complications in interpreting internal database elements, so initially a straightforward translation appears adequate. Reality teaches us quite the opposite, however, as the resulting text should be expressive, i.e., accurate in capturing the underlying queries or data, and effective, i.e., allowing fast and unique interpretation of them. Achieving both of these qualities is very difficult and raises several technical challenges that need to be addressed. In this paper, we first expose the reader to several situations and applications that need translation into natural language, thereby, motivating the problem. We then outline, by example, the research problems that need to be solved, separately for data translations and query translations.Comment: CIDR 200