81,570 research outputs found
Exploiting synergy between ontologies and recommender systems
Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations.Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain.
This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured
KnowText: Auto-generated Knowledge Graphs for custom domain applications
While industrial Knowledge Graphs enable information extraction from massive data volumes creating the backbone of the Semantic Web, the specialised, custom designed knowledge graphs focused on enterprise specific information are an emerging trend. We present âKnowTextâ, an application that performs automatic generation of custom Knowledge Graphs from unstructured text and enables fast information extraction based on graph visualisation and free text query methods designed for non-specialist users. An OWL ontology automatically extracted from text is linked to the knowledge graph and used as a knowledge base. A basic ontological schema is provided including 16 Classes and Data type Properties. The extracted facts and the OWL ontology can be downloaded and further refined. KnowText is designed for applications in business (CRM, HR, banking). Custom KG can serve for locally managing existing data, often stored as âsensitiveâ information or proprietary accounts, which are not on open web access. KnowText deploys a custom KG from a collection of text documents and enable fast information extraction based on its graph based visualisation and text based query methods
Generating adaptive hypertext content from the semantic web
Accessing and extracting knowledge from online documents is crucial for therealisation of the Semantic Web and the provision of advanced knowledge services.
The Artequakt project is an ongoing investigation tackling these issues to facilitate the creation of tailored biographies from information harvested from the web.
In this paper we will present the methods we currently use to model, consolidate and store knowledge extracted from the web so that it can be re-purposed as adaptive content. We look at how Semantic Web technology could be used within this process and also how such techniques might be used to provide content to be published via the Semantic Web
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
FVQA: Fact-based Visual Question Answering
Visual Question Answering (VQA) has attracted a lot of attention in both
Computer Vision and Natural Language Processing communities, not least because
it offers insight into the relationships between two important sources of
information. Current datasets, and the models built upon them, have focused on
questions which are answerable by direct analysis of the question and image
alone. The set of such questions that require no external information to answer
is interesting, but very limited. It excludes questions which require common
sense, or basic factual knowledge to answer, for example. Here we introduce
FVQA, a VQA dataset which requires, and supports, much deeper reasoning. FVQA
only contains questions which require external information to answer.
We thus extend a conventional visual question answering dataset, which
contains image-question-answerg triplets, through additional
image-question-answer-supporting fact tuples. The supporting fact is
represented as a structural triplet, such as .
We evaluate several baseline models on the FVQA dataset, and describe a novel
model which is capable of reasoning about an image on the basis of supporting
facts.Comment: 16 page
Design and evaluation of acceleration strategies for speeding up the development of dialog applications
In this paper, we describe a complete development platform that features different innovative acceleration strategies, not included in any other current platform, that simplify and speed up the definition of the different elements required to design a spoken dialog service. The proposed accelerations are mainly based on using the information from the backend database schema and contents, as well as cumulative information produced throughout the different steps in the design. Thanks to these accelerations, the interaction between the designer and the platform is improved, and in most cases the design is reduced to simple confirmations of the âproposalsâ that the platform dynamically provides at each step.
In addition, the platform provides several other accelerations such as configurable templates that can be used to define the different tasks in the service or the dialogs to obtain or show information to the user, automatic proposals for the best way to request slot contents from the user (i.e. using mixed-initiative forms or directed forms), an assistant that offers the set of more probable actions required to complete the definition of the different tasks in the application, or another assistant for solving specific modality details such as confirmations of user answers or how to present them the lists of retrieved results after querying the backend database. Additionally, the platform also allows the creation of speech grammars and prompts, database access functions, and the possibility of using mixed initiative and over-answering dialogs. In the paper we also describe in detail each assistant in the platform, emphasizing the different kind of methodologies followed to facilitate the design process at each one.
Finally, we describe the results obtained in both a subjective and an objective evaluation with different designers that confirm the viability, usefulness, and functionality of the proposed accelerations. Thanks to the accelerations, the design time is reduced in more than 56% and the number of keystrokes by 84%
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