The RacerPro knowledge representation and reasoning system

Haarslev, Volker; Hidde, Kay; Möller, Ralf; Wessel, Michael

doi:10.3233/sw-2011-0032

Cited by 108 publications

(59 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In reality, there is a fine line where the ontology ends and the knowledge base begins [36,37]. In this work, the ontology (created using Racer Pro [38]) describes the interpretation of the visibility index as defined in the previous section (see Figure 4). Six atomic concepts represent the six possible interpretations, badWorG, badW, badG, badWandG, bad and good.…”

Section: An Ontology For Metric-based Decision Makingmentioning

confidence: 99%

“…Derived values from this information are sent to the reasoner via a web (in-knowledge-base viz test-viz) (signature :atomic-concepts (badWorG badWandG badW badG bad good cond1 cond2 cond3 cond4 cond5 cond6 cond7 cond8 cond9) :attributes ( (real WG ) (real W max 1 )) (real W max G ) (real W1 ) ) :individuals( p ) :objects( cWG cW max 1 cW max G cW1 ) ) (equivalent bad (< W max 1 ) 0.67 ) ) (equivalent good (> WG 0.67 ) ) (equivalent cond1 (< WG 0.67 ) ) (equivalent cond3 (> W max G 0.67 ) ) (equivalent cond4 (and cond3 (> W1 0.67 ) ) ) (equivalent badWorG (and cond4 cond1 ) ) (equivalent cond5 (< W max G 0.67 ) ) (equivalent cond6 (< W1 0.67 ) ) (equivalent cond7 (and cond5 cond6 ) ) (equivalent badWandG (and cond2 cond7 ) ) (equivalent cond8 (and cond1 cond6 ) ) (equivalent badW (and cond8 cond3 ) ) (equivalent cond9 (and cond1 cond5 ) ) (equivalent badG (and cond9 (> W1 0.67 ) ) ) (constrained p cWG WG ) (constrained p cW max 1 W max 1 ) (constrained p cW max G W max G ) (constrained p cW1 W1 ) Figure 4: Ontology that contains the interpretation of the visibility metric values. This ontology was created using RacerPro [38].…”

Section: Implementation and Use Casesmentioning

confidence: 99%

See 1 more Smart Citation

Automatic suggestions to improve the quality of scatterplots during its creation

Urribarri

Larrea

Castro

2019

JC&ST

View full text Add to dashboard Cite

The process of creating a visualization is a very complex exploration activity and, even for skilled users, it can be difficult to produce an effective visualization. Since the result of such a process depends on the user's decisions along it, one way to improve the probability of achieving a useful outcome is to assist the user in the configuration and preparation of the visualization. Our proposal consists in live suggestions on how to improve the visualization. These live suggestions are based on the user decisions, and are achieved by the integration of semantic reasoning into the visualization process. In this paper, we present a case study for scatterplots visualization that combines ontologies with a semantic reasoner and helps the user in the generation of an effective visualization.Keywords: ontology, scatterplot, semantic-based visualization, semantic reasoner, visualization quality prediction binación de ontologías con un razonador semántico, para logar una visualización efectiva.Palabras claves: ontología, predicción de calidad, razonador semántico, scatterplot, visualizaciones basadas en semántica -ORIGINAL ARTICLE -

show abstract

Section: An Ontology For Metric-based Decision Makingmentioning

confidence: 99%

Section: Implementation and Use Casesmentioning

confidence: 99%

Automatic suggestions to improve the quality of scatterplots during its creation

Urribarri

Larrea

Castro

2019

JC&ST

View full text Add to dashboard Cite

show abstract

“…Besides general tools, there are implementations addressing specific aspects (e.g., reasoning with CDR [Liu et al 2010]) or tailored to specific problems (e.g., Phalanx for sparse RCC-8 QCSPs [Sioutis and Condotta 2014]). In the contact area of qualitative and logical reasoning, the DL reasoners Racer [Haarslev et al 2012] and PelletSpatial [Stocker and Sirin 2009] offer support for handling a selection of qualitative formalisms. For logical reasoning about qualitative domain representations, the tools Hets , SPASS [Weidenbach et al 2002], and Isabelle [Nipkow et al 2002] have been applied, supporting the first-order Common Algebraic Specification Language CASL [Astesiano et al 2002] as well as its higher-order variant HasCASL (see [Wölfl et al 2007]).…”

Section: Tools To Facilitate Qualitative Reasoningmentioning

confidence: 99%

A Survey of Qualitative Spatial and Temporal Calculi

et al. 2017

View full text Add to dashboard Cite

Qualitative Spatial and Temporal Reasoning (QSTR) is concerned with symbolic knowledge representation, typically over infinite domains. The motivations for employing QSTR techniques range from exploiting computational properties that allow efficient reasoning to capture human cognitive concepts in a computational framework. The notion of a qualitative calculus is one of the most prominent QSTR formalisms. This article presents the first overview of all qualitative calculi developed to date and their computational properties, together with generalized definitions of the fundamental concepts and methods, which now encompass all existing calculi. Moreover, we provide a classification of calculi according to their algebraic properties.

show abstract

“…The Tbox in Fig. 2 introduces concepts like Cancer which is a particular type of Disease and BreastCancer is a particular type of Cancer (axioms 7,8). A disease has symptoms, presented by the role manifestedSymptom that has as range the concept Symptom (axiom 9).…”

Section: B Breast Cancer Ontologymentioning

confidence: 99%

Mining arguments from cancer documents using Natural Language Processing and ontologies

Groza

Popa

2016

2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP)

View full text Add to dashboard Cite

Abstract-In the medical domain, the continuous stream of scientific research contains contradictory results supported by arguments and counter-arguments. As medical expertise occurs at different levels, part of the human agents have difficulties to face the huge amount of studies, but also to understand the reasons and pieces of evidences claimed by the proponents and the opponents of the debated topic. To better understand the supporting arguments for new findings related to current state of the art in the medical domain we need tools able to identify arguments in scientific papers. Our work here aims to fill the above technological gap.Quite aware of the difficulty of this task, we embark to this road by relying on the well-known interleaving of domain knowledge with natural language processing. To formalise the existing medical knowledge, we rely on ontologies. To structure the argumentation model we use also the expressivity and reasoning capabilities of Description Logics. To perform argumentation mining we formalise various linguistic patterns in a rule-based language. We tested our solution against a corpus of scientific papers related to breast cancer. The run experiments show a Fmeasure between 0.71 and 0.86 for identifying conclusions of an argument and between 0.65 and 0.86 for identifying premises of an argument.

show abstract

The RacerPro knowledge representation and reasoning system

Cited by 108 publications

References 33 publications

Automatic suggestions to improve the quality of scatterplots during its creation

Automatic suggestions to improve the quality of scatterplots during its creation

A Survey of Qualitative Spatial and Temporal Calculi

Mining arguments from cancer documents using Natural Language Processing and ontologies

Contact Info

Product

Resources

About