Search results

Experimental evidence suggests that enhancing the subject content of OPAC records can improve retrieval performance. This is based on the use of natural language index terms derived from the table of contents and back‐of‐the‐book index of documents. The research reported here investigates the alternative approach of translating these natural language terms into controlled vocabulary. Subject queries were collected by interview at the catalogue, and indexing of the queries demonstrated the impressive ability of PRECIS, and to a lesser extent LCSH, to represent users' information needs. DDC performed poorly in this respect. The assumption was made that an index language adequately specific to represent users' queries should be adequate to represent document contents. Searches were carried out on three test databases, and both natural language and PRECIS enhancement of MARC records increased the number of relevant documents found, with PRECIS showing the better performance. However, with weak stemming the advantage of PRECIS was lost. Consideration must also be given to the potential advantages of controlled vocabulary, over and above basic retrieval performance measures.

There is a huge amount of information and data stored in publicly available online databases that consist of large text files accessed by Boolean search techniques. It is widely held that less use is made of these databases than could or should be the case, and that one reason for this is that potential users find it difficult to identify which databases to search, to use the various command languages of the hosts and to construct the Boolean search statements required. This reasoning has stimulated a considerable amount of exploration and development work on the construction of search interfaces, to aid the inexperienced user to gain effective access to these databases. The aim of our paper is to review aspects of the design of such interfaces: to indicate the requirements that must be met if maximum aid is to be offered to the inexperienced searcher; to spell out the knowledge that must be incorporated in an interface if such aid is to be given; to describe some of the solutions that have been implemented in experimental and operational interfaces; and to discuss some of the problems encountered. The paper closes with an extensive bibliography of references relevant to online search aids, going well beyond the items explicitly mentioned in the text. An index to software appears after the bibliography at the end of the paper.

The field of natural language processing (NLP) demonstrates rapid changes in the design of information retrieval systems and human‐computer interaction. While natural language is being looked on as the most effective tool for information retrieval in a contemporary information environment, the systems using it are only beginning to emerge. This study attempts to evaluate the current state of NLP IR systems from the user’s point of view: what techniques are used by these systems to guide their users through the search process? The analysis focused on the structure and components of the systems’ help mechanisms. Results of the study demonstrated that systems which claimed to be using natural language searching in fact used a wide range of information retrieval techniques from real natural language processing to Boolean searching. As a result, the user assistance mechanisms of these systems also varied. While pseudo‐NLP systems would suit a more traditional method of instruction, real NLP systems primarily utilised the methods of explanation and user‐system “dialogue”.

The purpose of this paper is to better understand three main aspects of semantic web search engines of Google Knowledge Graph and Bing Satori. The authors investigated: coverage of entity types, the extent of their support for list search services and the capabilities of their natural language query interfaces.

The authors manually submitted selected queries to these two semantic web search engines and evaluated the returned results. To test the coverage of entity types, the authors selected the entity types from Freebase database. To test the capabilities of natural language query interfaces, the authors used a manually developed query data set about US geography.

The results indicate that both semantic search engines cover only the very common entity types. In addition, the list search service is provided for a small percentage of entity types. Moreover, both search engines support queries with very limited complexity and with limited set of recognised terms.

Both companies are continually working to improve their semantic web search engines. Therefore, the findings show their capabilities at the time of conducting this research.

The results show that in the near future the authors can expect both semantic search engines to expand their entity databases and improve their natural language interfaces.

As far as the authors know, this is the first study evaluating any aspect of newly developing semantic web search engines. It shows the current capabilities and limitations of these semantic web search engines. It provides directions to researchers by pointing out the main problems for semantic web search engines.

Purpose – The main purpose of this research is to determine whether the performance of natural language (NL) search engines in retrieving exact answers to the NL queries differs from that of keyword searching search engines. Design/methodology/approach – A total of 40 natural language queries were posed to Google and three NL search engines: Ask.com, Hakia and Bing. The first results pages were compared in terms of retrieving exact answer documents and whether they were at the top of the retrieved results, and the precision of exact answer and relevant documents. Findings – Ask.com retrieved exact answer document descriptions at the top of the results list in 60 percent of searches, which was better than the other search engines, but the mean value of the number of exact answer top list documents for three NL search engines (20.67) was a little less than Google's (21). There was no significant difference between the precision for Google and three NL search engines in retrieving exact answer documents for NL queries. Practical implications – The results imply that all NL and keyword searching search engines studied in this research mostly employ similar techniques using keywords of the NL queries, which is far from semantic searching and understanding what the user wants in searching with NL queries. Originality/value – The results shed light into the claims of NL search engines regarding semantic searching of NL queries.

This review reports on the current state and the potential of tools and systems designed to aid online searching, referred to here as online searching aids. Intermediary mechanisms are examined in terms of the two stage model, i.e. end‐user, intermediary, ‘raw database’, and different forms of user — system interaction are discussed. The evolution of the terminology of online searching aids is presented with special emphasis on the expert/non‐expert division. Terms defined include gateways, front‐end systems, intermediary systems and post‐processing. The alternative configurations that such systems can have and the approaches to the design of the user interface are discussed. The review then analyses the functions of online searching aids, i.e. logon procedures, access to hosts, help features, search formulation, query reformulation, database selection, uploading, downloading and post‐processing. Costs are then briefly examined. The review concludes by looking at future trends following recent developments in computer science and elsewhere. Distributed expert based information systems (debis), the standard generalised mark‐up language (SGML), the client‐server model, object‐orientation and parallel processing are expected to influence, if they have not done so already, the design and implementation of future online searching aids.

Current possibilities of accessing business data by regular users usually involve complicated user interfaces or require technical expertise. This results in situations when business owners are separated from their data. The aim of this research is to apply an innovative approach leveraging conversational interfaces to tackle this problem.

The authors examine the current possibilities of accessing business data by business, users with an emphasis on conversational interfaces employing a chatbot as an alternative to traditional approaches. The authors propose a new concept relying on a guided conversation, and through experiments with a real chatbot and database, the authors demonstrate the benefits of the proposed approach.

The authors found out that the key to the success of our approach is a decomposition of complex database queries and their incremental construction in conversations. This also enables natural discovery of the domain model through constantly provided feedback. Based on the experiments with a real chatbot, the authors demonstrate that defining conversation flows and maintaining the conversation context is a crucial aspect contributing to the overall accuracy, together with keeping the conversation within the defined limits in its certain parts.

The authors present a novel approach using natural language interfaces for accessing data by business users. In contrast to existing approaches, the authors emphasize incremental construction of queries, predefined conversation flows and constraining the conversations, when necessary.

With the development of systems and applications, the number of users interacting with databases has increased considerably. The relational database model is still considered as the most used model for data storage and manipulation. However, it does not offer any semantic support for the stored data which can facilitate data access for the users. Indeed, a large number of users are intimidated when retrieving data because they are non-technical or have little technical knowledge. To overcome this problem, researchers are continuously developing new techniques for Natural Language Interfaces to Databases (NLIDB). Nowadays, the usage of existing NLIDBs is not widespread due to their deficiencies in understanding natural language (NL) queries. In this sense, the purpose of this paper is to propose a novel method for an intelligent understanding of NL queries using semantically enriched database sources.

First a reverse engineering process is applied to extract relational database hidden semantics. In the second step, the extracted semantics are enriched further using a domain ontology. After this, all semantics are stored in the same relational database. The phase of processing NL queries uses the stored semantics to generate a semantic tree.

The evaluation part of the work shows the advantages of using a semantically enriched database source to understand NL queries. Additionally, enriching a relational database has given more flexibility to understand contextual and synonymous words that may be used in a NL query.

Existing NLIDBs are not yet a standard option for interfacing a relational database due to their lack for understanding NL queries. Indeed, the techniques used in the literature have their limits. This paper handles those limits by identifying the NL elements by their semantic nature in order to generate a semantic tree. This last is a key solution towards an intelligent understanding of NL queries to relational databases.

The paper describes techniques developed by Tome Associates to process natural language queries into search statements suitable for transmission to online text database systems. The problems discussed include word identification, the handling of unknown words, the contents and structure of system dictionaries, the use of semantic categories and classification, disambiguation of multi‐meaning words, stemming and truncation, noun compounds and indications of relationship between search terms.

STATUS with IQ is a free text retrieval system which invites the user to enter a query either by specifying search terms which reflect concepts relative to the user's information need or by describing that need in natural language. IQ responds by producing an objectively ranked list of those documents which will be most helpful to the inquirer, there being no requirement that any particular search term be present unless this has been made a precondition. In addition, it provides the user with detailed statistical information showing the extent to which each retrieved document contains the various search terms. These details provide valuable additional guidance regarding the information content of the retrieved documents.