Textual Membership Queries

Human labeling of data can be very time-consuming and expensive, yet, in many cases it is critical for the success of the learning process. In order to minimize human labeling efforts, we propose a novel active learning solution that does not rely on existing sources of unlabeled data. It uses a small amount of labeled data as the core set for the synthesis of useful membership queries (MQs) - unlabeled instances generated by an algorithm for human labeling. Our solution uses modification operators, functions that modify instances to some extent. We apply the operators on a small set of instances (core set), creating a set of new membership queries. Using this framework, we look at the instance space as a search space and apply search algorithms in order to generate new examples highly relevant to the learner. We implement this framework in the textual domain and test it on several text classification tasks and show improved classifier performance as more MQs are labeled and incorporated into the training set. To the best of our knowledge, this is the first work on membership queries in the textual domain.Comment: Accepted to IJCAI 2020. Code is available at github.com/jonzarecki/textual-mqs . Additional material is available at tinyurl.com/sup-textualmqs . SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserve

A Learning Algorithm for Deterministic Finite Automata using JFLAP

AbstractThe JFLAP package is a free, interactive visualization, and teaching tool for formal languages. JFLAP is based on the principle that a picture of a concept can be easier to understand than a textual representation. With the help of this package, we implement Dana Angluin's algorithm which is able to learn Deterministic Finite Automata. The use of JFLAP allows users to visualize each step in the process of learning. The protocol used by the algorithm is called exact learning from membership and equivalence queries. This protocol was also introduced by Dana Angluin, who showed that her learning algorithm discovers the unique minimum automaton coherent with the queries in an efficient running time

CROEQS: Contemporaneous Role Ontology-based Expanded Query Search: implementation and evaluation

Searching annotated items in multimedia databases becomes increasingly important. The traditional approach is to build a search engine based on textual metadata. However, in manually annotated multimedia databases, the conceptual level of what is searched for might differ from the high-levelness of the annotations of the items. To address this problem, we present CROEQS, a semantically enhanced search engine. It allows the user to query the annotated persons not only on their name, but also on their roles at the time the multimedia item was broadcast. We also present the ontology used to expand such queries: it allows us to semantically represent the domain knowledge on people fulfilling a role during a temporal interval in general, and politicians holding a political office specifically. The evaluation results show that query expansion using data retrieved from an ontology considerably filters the result set, although there is a performance penalty

Peer - Mediated Distributed Knowledge Management

Distributed Knowledge Management is an approach to knowledge management based on the principle that the multiplicity (and heterogeneity) of perspectives within complex organizations is not be viewed as an obstacle to knowledge exploitation, but rather as an opportunity that can foster innovation and creativity. Despite a wide agreement on this principle, most current KM systems are based on the idea that all perspectival aspects of knowledge should be eliminated in favor of an objective and general representation of knowledge. In this paper we propose a peer-to-peer architecture (called KEx), which embodies the principle above in a quite straightforward way: (i) each peer (called a K-peer) provides all the services needed to create and organize "local" knowledge from an individual's or a group's perspective, and (ii) social structures and protocols of meaning negotiation are introduced to achieve semantic coordination among autonomous peers (e.g., when searching documents from other K-peers). A first version of the system, called KEx, is imple-mented as a knowledge exchange level on top of JXTA

Topic modeling for entity linking using keyphrase

This paper proposes an Entity Linking system that applies a topic modeling ranking. We apply a novel approach in order to provide new relevant elements to the model. These elements are keyphrases related to the queries and gathered from a huge Wikipedia-based knowledge resourcePeer ReviewedPostprint (author’s final draft

Automatic organisation of retrieved images into a hierarchy

Image retrieval is of growing interest to both search engines and academic researchers with increased focus on both content-based and caption-based approaches. Image search, however, is different from document retrieval: users often search a broader set of retrieved images than they would examine returned web pages in a search engine. In this paper, we focus on a concept hierarchy generation approach developed by Sanderson and Croft in 1999, which was used to organise retrieved images in a hierarchy automatically generated from image captions. Thirty participants were recruited for the study. Each of them conducted two different kinds of searching tasks within the system. Results indicated that the user retrieval performance in both interfaces of system is similar. However, the majority of users preferred to use the concept hierarchy to complete their searching tasks and they were satisfied with using the hierarchical menu to organize retrieved results, because the menu appeared to provide a useful summary to help users look through the image results