627 research outputs found
Automatic extraction of knowledge from web documents
A large amount of digital information available is written as text documents in the form of web pages, reports, papers, emails, etc. Extracting the knowledge of interest from such documents from multiple sources in a timely fashion is therefore crucial. This paper provides an update on the Artequakt system which uses natural language tools to automatically extract knowledge about artists from multiple documents based on a predefined ontology. The ontology represents the type and form of knowledge to extract. This knowledge is then used to generate tailored biographies. The information extraction process of Artequakt is detailed and evaluated in this paper
Artequakt: Generating tailored biographies from automatically annotated fragments from the web
The Artequakt project seeks to automatically generate narrativebiographies of artists from knowledge that has been extracted from the Web and maintained in a knowledge base. An overview of the system architecture is presented here and the three key components of that architecture are explained in detail, namely knowledge extraction, information management and biography construction. Conclusions are drawn from the initial experiences of the project and future progress is detailed
The Epistemology of scheduling problems
Scheduling is a knowledge-intensive task spanning over many activities in day-to-day life. It deals with the temporally-bound assignment of jobs to resources. Although scheduling has been extensively researched in the AI community for the past 30 years, efforts have primarily focused on specific applications, algorithms, or 'scheduling shells' and no comprehensive analysis exists on the nature of scheduling problems, which provides a formal account of what scheduling is, independently of the way scheduling problems can be approached. Research on KBS development by reuse makes use of ontologies, to provide knowledge-level specifications of reusable KBS components. In this paper we describe a task ontology, which formally characterises the nature of scheduling problems, independently of particular application domains and in-dependently of how the problems can be solved. Our results provide a comprehensive, domain-independent and formally specified refer-ence model for scheduling applications. This can be used as the ba-sis for further analyses of the class of scheduling problems and also as a concrete reusable resource to support knowledge acquisition and system development in scheduling applications
Inductive learning spatial attention
This paper investigates the automatic induction of spatial attention
from the visual observation of objects manipulated
on a table top. In this work, space is represented in terms of
a novel observer-object relative reference system, named Local
Cardinal System, defined upon the local neighbourhood
of objects on the table. We present results of applying the
proposed methodology on five distinct scenarios involving
the construction of spatial patterns of coloured blocks
Semantic web technology to support learning about the semantic web
This paper describes ASPL, an Advanced Semantic Platform for Learning, designed using the Magpie framework with an aim to support students learning about the Semantic Web research area. We describe the evolution of ASPL and illustrate how we used the results from a formal evaluation of the initial system to re-design the user functionalities. The second version of ASPL semantically interprets the results provided by a non-semantic web mining tool and uses them to support various forms of semantics-assisted exploration, based on pedagogical strategies such as performing later reasoning steps and problem space filtering
Analysing similarity assessment in feature-vector case representations
Case-Based Reasoning (CBR) is a good technique to solve new problems based in previous experience. Main assumption in CBR relies in the hypothesis that similar problems should have similar solutions. CBR systems retrieve the most similar cases or experiences among those stored in the Case Base. Then, previous solutions given to these most similar past-solved cases can be adapted to fit new solutions for new cases or problems in a particular domain, instead of derive them from scratch. Thus, similarity measures are key elements in obtaining reliable similar cases, which will be used to derive solutions for new cases. This paper describes a comparative analysis of several commonly used similarity measures, including a measure previously developed by the authors, and a study on its performance in the CBR retrieval step for feature-vector case representations. The testing has been done using six-teen data sets from the UCI Machine Learning Database Repository, plus two complex environmental databases.Postprint (published version
Multi-level agent-based modeling - A literature survey
During last decade, multi-level agent-based modeling has received significant
and dramatically increasing interest. In this article we present a
comprehensive and structured review of literature on the subject. We present
the main theoretical contributions and application domains of this concept,
with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic
statistics updated. v7 Change of the name of the paper to reflect what it
became, many refs and text added, bibliographic statistics update
Initiating organizational memories using ontology-based network analysis as a bootstrapping tool
An important problem for many kinds of knowledge systems is their initial set-up. It is difficult to choose the right information to include in such systems, and the right information is also a prerequisite for maximizing the uptake and relevance. To tackle this problem, most developers adopt heavyweight solutions and rely on a faithful continuous interaction with users to create and improve content. In this paper, we explore the use of an automatic, lightweight ontology-based solution to the bootstrapping problem, in which domain-describing ontologies are analysed to uncover significant yet implicit relationships between instances. We illustrate the approach by using such an analysis to provide content automatically for the initial set-up of an organizational memory
Optimal Placement of Valves in a Water Distribution Network with CLP(FD)
This paper presents a new application of logic programming to a real-life
problem in hydraulic engineering. The work is developed as a collaboration of
computer scientists and hydraulic engineers, and applies Constraint Logic
Programming to solve a hard combinatorial problem. This application deals with
one aspect of the design of a water distribution network, i.e., the valve
isolation system design.
We take the formulation of the problem by Giustolisi and Savic (2008) and
show how, thanks to constraint propagation, we can get better solutions than
the best solution known in the literature for the Apulian distribution network.
We believe that the area of the so-called hydroinformatics can benefit from
the techniques developed in Constraint Logic Programming and possibly from
other areas of logic programming, such as Answer Set Programming.Comment: Best paper award at the 27th International Conference on Logic
Programming - ICLP 2011; Theory and Practice of Logic Programming, (ICLP'11)
Special Issue, volume 11, issue 4-5, 201
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