Answering user queries from hotel ontology for decision making

Semantic web comes out with the vision of making human readable information to be machine processable. Ontology, the core of semantic web, with concept instantiations serves as a domain knowledge base while semantic web query language provides retrieval of that information. In this paper, we presented a system that populates hotel related information in the ontology and a natural language querying platform to retrieve the information from a common interface for decision making. A simple user experiment shows that the system is time effective and helpful in making decisions with minimum queries as compared to browsing even with selected sites

Multimedia Information Retrieval

With recent advances in screen and mass storage technology, together with the on-going advances in computer power, many users of personal computers and low end workstations are now regularly manipulating non-textual information. This information may be in the form of drawings, graphs, animations, sound, or video (for example). With the increased usage of these media on computer systems there has not, however, been much work in the provision of access methods to non-textual computer based information. An increasingly common method for accessing large document bases of textual information is free text retrieval. In such systems users typically enter natural language queries. These are then matched against the textual documents in the system. It is often possible for the user to re-formulate a query by providing relevance feedback, this usually takes the form of the user informing the system that certain documents are indeed relevant to the current search. This information, together with the original query, is then used by the retrieval engine to provide an improved list of matched documents. Although free text retrieval provides reasonably effective access to large document bases it does not provide easy access to non-textual information. Various query based access methods to nontextual document bases are presented, but these are all restricted to specific domains and cannot be used in mixed media systems. Hypermedia, on the other hand, is an access method for document bases which is based on the user browsing through the document base rather than issuing queries. A set of interconnected paths are constructed through the base which the user may follow. Although providing poorer access to large document bases the browsing approach does provide very natural access to non-textual information. The recent explosion in hypermedia systems and discussion has been partly due to the requirement for access to mixed media document bases. Some work is reported which presents an integration of free text retrieval based queries with hypermedia. This provides a solution to the scaling problem of browsing based systems, these systems provide access to textual nodes by query or by browsing. Non-textual nodes are, however, still only accessible by browsing - either from the starting point of the document base or from a textual document which matched the query. A model of retrieval for non-textual documents is developed, this model is based on document's context within the hypermedia document base, as opposed to the document's content. If a non-textual document is connected to several textual documents, by paths in the hypermedia, then it is likely that the non-textual document will match the query whenever a high enough proportion of the textual documents match. This model of retrieval uses clustering techniques to calculate a descriptor for non-textual nodes so that they may be retrieved directly in response to a query. To establish that this model of retrieval for non-textual documents is worthwhile an experiment was run which used the text only CACM collection. Each record within the collection was initially treated as if it were non-textual and had a cluster based description calculated based on citations, this cluster based descriptor was then compared with the actual descriptor (calculated from the record's content) to establish how accurate the cluster descriptor was. As a base case the experiment was repeated using randomly created links, as opposed to citations. The results showed that for citation based links the cluster based descriptions had a mean correlating of 0.230 with the content based description (on a range from 0 to 1, where 1 represents a perfect match) and performed approximately six times better than when random links were used (mean random correlation was 0.037). This shows that citation based cluster descriptions of documents are significantly closer to the actual descriptions than random based links, and although the correlation is quite low, the cluster approach provides a useful technique for describing documents. The model of retrieval presented for non-textual documents relies upon a hypermedia structure existing in the document base, since the model cannot work if the documents are not linked together. A user interface to a document base which gives access to a retrieval engine and to hypermedia links can be based around three main categories: browsing only access, use the retrieval engine to support link creation; query only access, use links to provide access to non-text; query and browsing access Although the last user interface may initially appear most suitable for a document base which can support queries and browsing it is also potentially the most complex interface, and may require a more complex model of retrieval for users to successfully search the document base. A set of user tests were carried out to establish user behaviour and to consider interface issues concerning easy access to documents which are held on such document bases. These tests showed that, overall, no access method was clearly better or poorer than any other method. (Abstract shortened by ProQuest.)

KARL: A Knowledge-Assisted Retrieval Language

Data classification and storage are tasks typically performed by application specialists. In contrast, information users are primarily non-computer specialists who use information in their decision-making and other activities. Interaction efficiency between such users and the computer is often reduced by machine requirements and resulting user reluctance to use the system. This thesis examines the problems associated with information retrieval for non-computer specialist users, and proposes a method for communicating in restricted English that uses knowledge of the entities involved, relationships between entities, and basic English language syntax and semantics to translate the user requests into formal queries. The proposed method includes an intelligent dictionary, syntax and semantic verifiers, and a formal query generator. In addition, the proposed system has a learning capability that can improve portability and performance. With the increasing demand for efficient human-machine communication, the significance of this thesis becomes apparent. As human resources become more valuable, software systems that will assist in improving the human-machine interface will be needed and research addressing new solutions will be of utmost importance. This thesis presents an initial design and implementation as a foundation for further research and development into the emerging field of natural language database query systems

User-Friendly MES Interfaces:Recommendations for an AI-Based Chatbot Assistance in Industry 4.0 Shop Floors

The purpose of this paper is to study an Industry 4.0 scenario of ‘technical assistance’ and use manufacturing execution systems (MES) to address the need for easy information extraction on the shop floor. We identify specific requirements for a user-friendly MES interface to develop (and test) an approach for technical assistance and introduce a chatbot with a prediction system as an interface layer for MES. The chatbot is aimed at production coordination by assisting the shop floor workforce and learn from their inputs, thus acting as an intelligent assistant. We programmed a prototype chatbot as a proof of concept, where the new interface layer provided live updates related to production in natural language and added predictive power to MES. The results indicate that the chatbot interface for MES is beneficial to the shop floor workforce and provides easy information extraction, compared to the traditional search techniques. The paper contributes to the manufacturing information systems field and demonstrates a human-AI collaboration system in a factory. In particular, this paper recommends the manner in which MES based technical assistance systems can be developed for the purpose of easy information retrieval

Mixing Modalities of 3D Sketching and Speech for Interactive Model Retrieval in Virtual Reality

Sketch and speech are intuitive interaction methods that convey complementary information and have been independently used for 3D model retrieval in virtual environments. While sketch has been shown to be an effective retrieval method, not all collections are easily navigable using this modality alone. We design a new challenging database for sketch comprised of 3D chairs where each of the components (arms, legs, seat, back) are independently colored. To overcome this, we implement a multimodal interface for querying 3D model databases within a virtual environment. We base the sketch on the state-of-the-art for 3D Sketch Retrieval, and use a Wizard-of-Oz style experiment to process the voice input. In this way, we avoid the complexities of natural language processing which frequently requires fine-tuning to be robust. We conduct two user studies and show that hybrid search strategies emerge from the combination of interactions, fostering the advantages provided by both modalities

Personalized Memory Transfer for Conversational Recommendation Systems

Dialogue systems are becoming an increasingly common part of many users\u27 daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to the user. To tackle the ambiguities in natural language conversations, we propose Personalized Memory Transfer (PMT) which learns a personalized model in an online manner by leveraging a key-value memory structure to distill user feedback directly from conversations. This memory structure enables the integration of prior knowledge to transfer existing item representations/preferences and natural language representations. We also implement a retrieval based response generation module, where the system in addition to recommending items to the user, also responds to the user, either to elicit more information regarding the user intent or just for a casual chit-chat. The experiments were conducted on two public datasets and the results demonstrate the effectiveness of the proposed approach

Argument Search with Voice Assistants

The need for finding persuasive arguments can arise in a variety of domains such as politics, finance, marketing or personal entertainment. In these domains, there is a demand to make decisions by oneself or to convince somebody about a specific topic. To obtain a conclusion, one has to search thoroughly different sources in literature and on the web to compare various arguments. Voice interfaces, in form of smartphone applications or smart speakers, present the user with natural conversations in a comfortable way to make search requests in contrast to a traditional search interface with keyboard and display. Benefits and obstacles of such a new interface are analyzed by conducting two studies. The first one consists of a survey for analyzing the target group with questions about situations, motivations, and possible demanding features. The latter one is a wizard-of-oz experiment to investigate possible queries on how a user formulates requests to such a novel system. The results indicate that a search interface with conversational abilities can build a helpful assistant, but to satisfy the demands of a broader audience some additional information retrieval and visualization features need to be implemented