Grammars controlled by petri nets with inhibitor arcs.

A Petri net controlled grammar is a grammar equipped with a Petri net whose transitions are labeled with production rules of the grammar, and the associated language consists of all terminal strings which can be derived in the grammar and the sequence of rules in every terminal derivation corresponds to some occurrence sequence of transitions of the Petri net which is enabled at the initial marking and finished at a final marking of the net. In this paper we define grammars controlled by Petri nets with inhibitor arcs and investigate their computational capacities

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

Wi-Fi signal strength vs. magnetic fields for indoor positioning systems

In this research we compare Wi-Fi received signal strength indication and magnetic field based real-time location systems (RTLS) from various perspectives such as system complexity, accuracy and stability. To evaluate the performance of these systems we built several test fields with different types of environments. We will compare both approaches side-by-side and answer such issues as optimal calibration step (measurement interval), location accuracy, effect of minor and major environment changes to fingerprint DB and overall system accuracy

Wi-Fi signal strengths database construction for indoor positioning systems using Wi-Fi RFID

Nowadays, fingerprinting based Wi-Fi positioning systems successfully provide location information to mobile users. Main idea behind fingerprinting is to build signal strength database of target area prior to location estimation. This process is called calibration. Indoor positioning system accuracy highly depends on calibration (sampling) intensity. This procedure requires huge amount of time and effort, and makes large-scale deployments of indoor positioning systems non-trivial. Newly constructed database may no longer be valid if there are any major changes in the target site. In this research we present a new approach of constructing fingerprint database. We propose a hybrid calibration procedure that combines signal sampling process with path-loss prediction algorithm. Instead of manual signal sampling, proposed method requires several Wi-Fi RFID tags to be installed in a target site. Advantage of such tag is that it can be read directly by commercial Wi-Fi access points from long distance. Several RFID tags mounted in target area will monitor the signal strength levels continuously and send scan data to the server. Whenever there are significant changes in signal levels detected, server will initiate database reconstruction procedure. Compared to existing calibration procedure our method requires only few signal samples from RFID tags to be collected and rest of the database is recovered using path-loss prediction algorithm

Nonterminal complexity of tree controlled grammars

This paper studies the nonterminal complexity of tree controlled grammars. It is proved that the number of nonterminals in tree controlled grammars without erasing rules leads to an infinite hierarchy of families of tree controlled languages, while every recursively enumerable language can be generated by a tree controlled grammar with erasing rules and at most nine nonterminals

Semantics representation in a sentence with concept relational model (CRM)

The current way of representing semantics or meaning in a sentence is by using the conceptual graphs. Conceptual graphs define concepts and conceptual relations loosely. This causes ambiguity because a word can be classified as a concept or relation. Ambiguity disrupts the process of recognizing graphs similarity, rendering difficulty to multiple graphs interaction. Relational flow is also altered in conceptual graphs when additional linguistic information is input. Inconsistency of relational flow is caused by the bipartite structure of conceptual graphs that only allows the representation of connection between concept and relations but never between relations per se. To overcome the problem of ambiguity, the concept relational model (CRM) described in this article strictly organizes word classes into three main categories; concept, relation and attribute. To do so, CRM begins by tagging the words in text and proceeds by classifying them according to a predefi ned mapping. In addition, CRM maintains the consistency of the relational flow by allowing connection between multiple relations as well. CRM then uses a set of canonical graphs to be worked on these newly classified components for the representation of semantics. The overall result is better accuracy in text engineering related task like relation extraction

Distributed knowledge management portal for Learning Organizations with collaborative environment

A knowledge management system (KMS) is a concept that can be used for creating knowledge repositories, improving knowledge access, sharing and communicating through collaboration, enhancing the knowledge environment and managing knowledge as an asset for an organization as well as inter-organization especially in Learning Organization (LO). In this paper, we will discuss and propose a distributed KMS portal for LOs with collaborative environment, a model and its components of KMS portal in LOs that will help the organizations to increase its productivity and quality as well as to gain return on investment (ROI). The component of KMS portal consists of its functionality, architecture, taxonomy, psychological, sociocultural and audit

A bayesian approach to intention-based response generation

The statistical approach to natural language generation of overgeneration-andranking suffers from expensive over generation. This article reports the findings of response classification experiment in the new approach of intention-based classification-andranking. Possible responses are deliberately chosen from a dialogue corpus rather than wholly generated, so the approach allows short ungrammatical utterances as long as they satisfy the intended meaning of the input utterance. We hypothesize that a response is relevant when it satisfies the intention of the preceding utterance, therefore this approach highly depends on intentions, rather than syntactic characterization of input utterance. The response classification experiment is tested on a mixed-initiative, transaction dialogue corpus in the theater domain. This article reports a promising start of 73% accuracy in prediction of response classes in a classification experiment with application of Bayesian networks

Corpus-based analysis on cross-domain experiments in classification-and-ranking generation

Problem statement: Overgeneration-and-ranking architecture works well in written language where sentence is the basic unit. However, in spoken language where utterance is the basic unit, the disadvantage becomes critical as spoken language also render intentions, hence short strings may be of equivalent impact. Approach: In classification-and-ranking, response was deliberately chosen from dialogue corpus rather than wholly generated, such that it allows short ungrammatical utterances as long as they satisfy the intended meaning of input utterance. Because the architecture is intention-based, it adopted an open-domain knowledge representation, whereby response utterances were semantically represented using some ontology general enough for future reuse in another domain. Results: This study presented corpus-based analysis on cross-domain experimentation using different type of corpus to validate the consistency of the response classifier that delimits the searching space for ranking. The open-domain quality for classification-an-ranking architecture was tested on two mixed-initiative, transaction dialogue corpus in theater reservation and emergency planning. Results showed consistent distribution accuracies in both classification and ranking experiment, indicating that the approach is viable for cross-domain implementations. Conclusion: The ability of a response generation system to directly learn response utterances from the domain corpus suggested the possibility to build a dialogue system by feeding the learning module with a target corpus and the system learned the response behavior directly from the training corpus