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2020 Journal article Open Access

Fudge: Fuzzy ontology building with consensuated fuzzy datatypes
Huitzil I., Bobillo F., Gomez-romero J., Straccia U.
An important problem in Fuzzy OWL 2 ontology building is the definition of fuzzy membership functions for real-valued fuzzy sets (so-called fuzzy datatypes in Fuzzy OWL 2 terminology). In this paper, we present a tool, called Fudge, whose aim is to support the consensual creation of fuzzy datatypes by aggregating the specifications given by a group of experts. Fudge is freeware and currently supports several linguistic aggregation strategies, including the convex combination, linguistic OWA, weighted mean and fuzzy OWA, and easily allows to build others in. We also propose and have implemented two novel linguistic aggregation operators, based on a left recursive form of the convex combination and of the linguistic OWA.Source: Fuzzy sets and systems 401 (2020): 91–112. doi:10.1016/j.fss.2020.04.001
DOI: 10.1016/j.fss.2020.04.001

2020 Report Open Access

Defeasible RDFS via rational closure
Casini G., Straccia U.
In the field of non-monotonic logics, the notion of Rational Closure (RC) is acknowledged as a prominent approach. In recent years, RC has gained even more popularity in the context of Description Logics (DLs), the logic underpinning the semantic web standard ontology language OWL 2, whose main ingredients are classes and roles. In this work, we show how to integrate RC within the triple language RDFS, which together with OWL2 are the two major standard semantic web ontology languages. To do so, we start from ?df, which is the logic behind RDFS, and then extend it to ?df?, allowing to state that two entities are incompatible. Eventually, we propose defeasible ?df? via a typical RC construction. The main features of our approach are: (i) unlike most other approaches that add an extra non-monotone rule layer on top of monotone RDFS, defeasible ?df? remains syntactically a triple language and is a simple extension of ?df? by introducing some new predicate symbols with specific semantics. In particular, any RDFS reasoner/store may handle them as ordinary terms if it does not want to take account for the extra semantics of the new predicate symbols; (ii) the defeasible ?df? entailment decision procedure is build on top of the ?df? entailment decision procedure, which in turn is an extension of the one for ?df via some additional inference rules favouring an potential implementation; and (iii) defeasible ?df? entailment can be decided in polynomial time.Source: ISTI Technical Reports 008/2020, 2020, 2020
DOI: 10.32079/isti-tr-2020/008

See at: ISTI Repository | CNR ExploRA

2020 Report Open Access

Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued Boosting
Cardillo F. A., Straccia U.
OWL ontologies are nowadays a quite popular way to describe structured knowledge in terms of classes, relations among classes and class instances. In this paper, given a target class T of an OWL ontology, we address the problem of learning fuzzy concept inclusion axioms that describe sufficient conditions for being an individual instance of T. To do so, we present Fuzzy OWL-BOOST that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL case. We illustrate its effectiveness by means of an experimentation. An interesting feature is that the learned rules can be represented directly into Fuzzy OWL 2. As a consequence, any Fuzzy OWL 2 reasoner can then be used to automatically determine/classify (and to which degree) whether an individual belongs to the target class T.Source: Research report, pp.1–26, 2020

See at: arxiv.org | ISTI Repository | CNR ExploRA

2020 Conference article Open Access

How Much Knowledge is in a Knowledge Base? Introducing Knowledge Measures (Preliminary Report)
Straccia U.
In this work we address the following question: can we measure how much knowledge a knowledge base represents? We answer to this question (i) by describing properties (axioms) that a knowledge measure we believe should have in measuring the amount of knowledge of a knowledge base (kb); and (ii) provide a concrete example of such a measure, based on the notion of entropy. We also introduce related kb notions such as (i) accuracy; (ii) conciseness; and (iii) Pareto optimality. Informally, they address the following questions: (i) how precise is a kb in describing the actual world? (ii) how succinct is a kb w.r.t. the knowledge it represents? and (iii) can we increase accuracy without decreasing conciseness, or vice-versa?Source: European Conference on Artificial Intelligence (ECAI-20), pp. 905–912, Santiago de Compostela, SPAIN, 29/08/2020 - 08/09/2020
DOI: 10.3233/faia200182

2020 Conference article Restricted

The serializable and incremental semantic reasoner fuzzyDL
Huitzil I., Straccia U., Bobed C., Mena E., Bobillo F.
Serializable and incremental semantic reasoners make it easier to reason on a mobile device with limited resources, as they allow the reuse of previous inferences computed by another device without starting from scratch. This paper describes an extension of the fuzzy ontology reasoner fuzzyDL to make it the first serializable and incremental semantic reasoner. We empirically show that the size of the serialized files is smaller than in another serializable semantic reasoner (JFact), and that there is a significant decrease in the reasoning time.Source: FUZZ-IEEE 2020 - IEEE International Conference on Fuzzy Systems, Glasgow, UK, 19-24 July 2020
DOI: 10.1109/fuzz48607.2020.9177835

2019 Report Open Access

Towards Ontology-based Explainable Classification of Rare Events
Cardillo F. A., Straccia U.
Rare events (e.g. major floods, violent conflicts) are events that have potentially widespread and/or disastrous impact on society. The overall goal is to build a framework capable to classify, predict and explain such rare events. To do so, we envisage the usage of a mixture of sub-symbolic Machine Learning (ML) and Ontology-based Statistical Relatio-nal Learning (OSRL) techniques to generate rare events classifiers and predictors, which additionally may be mapped into natural language to ease human interpretability of the decision process.Source: ISTI Working papers, pp.1–2, 2019

2019 Journal article Open Access

A fuzzy ontology-based approach for tool-supported decision making in architectural design
Di Noia T., Mongiello M., Nocera F., Straccia U.
In software development, Non-Functional Requirements (NFRs) play a crucial role in decision making procedures for architectural solutions. A strong relation exists between NFRs and design patterns, a powerful method to support the architectural design of software systems, but due to their complexity and abstraction, NFRs are rarely taken into account in software design. In fact, the knowledge on NFRs is usually owned by designers and not formalized in a structured way. We propose to structure the knowledge associated to NFRs via a Fuzzy Ontology, which we show is able to model their mutual relations and interactions. The declarative approach makes possible to represent and maintain the above mentioned knowledge by keeping the flexibility and fuzziness of modeling thanks to the use of fuzzy concepts such as high, low, fair, etc. We present a decision support system based on (i) a Fuzzy OWL 2 ontology that encodes 109 design patterns, 28 pattern families and 37 NFRs and their mutual relations, (ii) a novel reasoning service to retrieve a ranked list of pattern sets able to satisfy the Non-Functional Requirements within a system specification.Source: Knowledge and Information Systems 58 (2019): 83–112. doi:10.1007/s10115-018-1182-1
DOI: 10.1007/s10115-018-1182-1

2019 Report Open Access

Towards a forensic event ontology to assist video surveillance-based vandalism detection
Sobhani F., Straccia U.
The detection and representation of events is a critical element in automated surveillance systems. We present here an ontology for representing complex semantic events to assist video surveillance-based vandalism detection. The ontology contains the definition of a rich and articulated event vocabulary that is aimed at aiding forensic analysis to objectively identify and represent complex events. Our ontology has then been applied in the context of London Riots, which took place in 2011. We report also on the experiments conducted to support the classification of complex criminal events from video data.Source: ISTI Technical reports, pp.1–24, 2019

See at: arxiv.org | ISTI Repository | CNR ExploRA

2019 Conference article Open Access

Towards a forensic event ontology to assist video surveillance-based vandalism detection
Sobhani F., Straccia U.
The detection and representation of events is a critical element in automated surveillance systems. We present here an ontology for representing complex semantic events to assist video surveillance-based vandalism detection. The ontology contains the definition of a rich and articulated event vocabulary that is aimed at aiding forensic analysis to objectively identify and represent complex events. Our ontology has then been applied in the context of London Riots, which took place in 2011. We report also on the experiments conducted to support the classification of complex criminal events from video data.Source: Italian Conference on Computational Logic (CILC-19), pp. 30–47, Trieste, Italy, June 19-21, 2019

2019 Journal article Open Access

A Polynomial Time Subsumption Algorithm for Nominal Safe $\mathcal{ELO}_\bot$ under Rational Closure
Casini G., Straccia U., Meyer T.
Description Logics (DLs) under Rational Closure (RC) is a well-known framework for non-monotonic reasoning in DLs. In this paper, we address the concept subsumption decision problem under RC for nominal safe \ELObot, a notable and practically important DL representative of the OWL 2 profile OWL 2 EL. Our contribution here is to define a polynomial time subsumption procedure for nominal safe \ELObot~under RC that relies entirely on a series of classical, monotonic \ELbot~subsumption tests. Therefore, any existing classical monotonic \ELbot~reasoner can be used as a black box to implement our method. We then also adapt the method to one of the known extensions of RC for DLs, namely Defeasible Inheritance-based DLs without losing the computational tractability.Source: Information sciences 501 (2019): 588–620. doi:10.1016/j.ins.2018.09.037
DOI: 10.1016/j.ins.2018.09.037

2018 Journal article Open Access

Reasoning within Fuzzy OWL 2 EL Revisited
Bobillo F., Straccia U.
Description Logics (DLs) are logics with interesting representational and computational features and are at the core of the Web Ontology Language OWL 2 and its profiles among which there is OWL 2 EL. The main feature of OWL 2 EL is that instance/subsumption checking can be decided in polynomial time. On the other hand, fuzzy DLs have been proposed as an extension to classical DLs with the aim of dealing with fuzzy concepts and we focus here on Fuzzy OWL 2 EL under standard and Goedel semantics. We provide some reasoning algorithms showing that instance/subsumption checking decision problems remain polynomial time for Fuzzy OWL 2 EL. We also identify some issues in previous related work (essentially incompleteness problems).Source: Fuzzy sets and systems 351 (2018): 1–40. doi:10.1016/j.fss.2018.03.011
DOI: 10.1016/j.fss.2018.03.011

2018 Conference article Open Access

Datil: learning fuzzy ontology datatypes
Huitzil I., Straccia U., Diaz-rodriguez N., Bobillo F.
Real-world applications using fuzzy ontologies are increasing in the last years, but the problem of fuzzy ontology learning has not received a lot of attention. While most of the previous approaches focus on the problem of learning fuzzy subclass axioms, we focus on learning fuzzy datatypes. In particular, we describe the Datil system, an implementation using unsupervised clustering algorithms to automatically obtain fuzzy datatypes from different input formats. We also illustrate the practical usefulness with an application: semantic lifestyle profiling.Source: IPMU 2018 - International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 100–112, Cádiz, Spain, 11-15 June, 2018
DOI: 10.1007/978-3-319-91476-3_9

2018 Contribution to conference Open Access

An introduction to fuzzy & annotated semantic web languages
Straccia U.
We present the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions.Source: A2IC 2018 - Artificial Intelligence International Conference, pp. 203–240, Barcelona, Spain, 21-23 November 2018
DOI: 10.1007/978-3-319-49493-7_6

2018 Contribution to book Open Access

Multimedia Information Retrieval Model
Meghini C., Sebastiani F., Straccia U.
Given a collection of multimedia documents, the goal of multimedia information retrieval (MIR) is to find the documents that are relevant to a user information need. A multimedia document is a complex information object, with components of different kinds, such as text, images, video and sound, all in digital form.Source: Encyclopedia of Database Systems (2nd ed.), edited by Liu, Ling and Özsu, M. Tamer (eds.), pp. 2390–2394. Cham, Heidelberg, New York, Dordrecht, London: Springer, 2018

2017 Contribution to book Open Access

From fuzzy to annotated semantic web languages
Straccia U., Bobillo F.
The aim of this talk is to present a detailed, self-contained and comprehensive account of the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions.Source: Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering, edited by Jeff Z. Pan, Diego Calvanese, Thomas Eiter, Ian Horrocks, Michael Kifer, Fangzhen Lin, Yuting Zhao, pp. 203–240, 2017
DOI: 10.1007/978-3-319-49493-7_6

2017 Journal article Open Access

Generalizing Type-2 Fuzzy Ontologies and Type-2 Fuzzy Description Logics
Bobillo F., Straccia U.
In the last years, we are witnessing an increase of real-world applications of fuzzy ontologies. Most fuzzy ontologies are based on type-1 fuzzy logic, and type-2 fuzzy ontologies have not yet received such attention so far. Furthermore, there exists an important gap between type-2 knowledge representation formalisms (type-2 Description Logics) and type-2 fuzzy ontology applications. In this paper, we propose a formal framework for type-2 fuzzy ontologies taking into account the needs of existing applications. Essentially, our approach makes it possible to manage some uncertainty in the fuzzy membership functions used in the fuzzy datatypes and in the degrees of truth of the axioms. We define a type-2 Description Logic, a reasoning algorithm, and give a Fuzzy OWL 2 specification of it.Source: International journal of approximate reasoning 87 (2017): 40–66. doi:10.1016/j.ijar.2017.04.012
DOI: 10.1016/j.ijar.2017.04.012

2017 Contribution to conference Open Access

Fuzzy Semantic Web Languages and Beyond
Straccia U.
The aim of this talk is to present the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions.Source: 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2017), pp. 3–8, Arras, France, 2017
DOI: 10.1007/978-3-319-60042-0_1

2017 Conference article Open Access

Integration of Deep Web Sources: A Distributed Information Retrieval Approach
Calì A., Straccia U.
The Deep Web consists of those structured data that are available as dynamically generated pages, typically requested through HTML forms. Deep Web pages cannot be indexed by search engines, and are notoriously difficult to query and integrate due to the limited access that they offer. We propose a novel framework for integrating Deep Web sources by means of a mediated schema that represent the underlying, distributed sources. Our goal is to compute answers to queries posed on the mediated schema. To this aim, we propose the use of techniques from the area of Distributed Information Retrieval. We discuss a novel approach to automated sampling, size estimation and selection of Deep Web sources, as well as a technique for merging result lists.Source: 7th International Conference on Web Intelligence, Mining and Semantics (WIMS-17), pp. 33:1–33:4, 19-22 June 2017
DOI: 10.1145/3102254.3102291

2017 Conference article Open Access

Couch potato or gym addict? Semantic lifestyle profiling with wearables and knowledge graphs
Díaz Rodríguez N., Harma A., Huitzil I., Bobillo F., Straccia U, Helaoui R.
Automatic lifestyle profiling to categorize users according to their daily routine-based lifestyles is an unexplored area. Despite the current trends on having wearable devices that generate large amounts of heterogeneous data, figuring out the lifestyle patterns of people is not a trivial task. We present Lifestyles-KG, a knowledge graph (fuzzy ontology) for semantic reasoning from wearable sensors. It can serve as a pre-processing taxonomical step that can be integrated into further prediction techniques for intuitively categorizing fuzzy lifestyle concepts, treats or profiles. The ultimate aim is to help tasks such as long-term human behavior classification and consequently, improve virtual coaching or customize lifestyle recommendation and intervention programs from free form non-labelled sensor data.Source: AKBC-17 - 6th Workshop on Automated Knowledge Base Construction, colocated with Thirty-First Annual Conference on Neural Information Processing Systems (NIPS-17), Long Beach, California, USA, December 8th, 2017

See at: CNR ExploRA | www.akbc.ws

2016 Journal article Open Access

The Fuzzy Ontology Reasoner fuzzyDL
Bobillo F., Straccia U.
Classical, two-valued, ontologies have been successfully applied to represent the knowledge in many domains. However, it has been pointed out that they are not suitable in domains where vague or imprecise pieces of information play an important role. To overcome this limitation, several extensions to classical ontologies based on fuzzy logic have been proposed. We believe, however, that the success of fuzzy ontologies strongly depends on the availability of effective reasoners able to deal with fuzzy ontologies. In this paper we describe fuzzyDL, an expressive fuzzy ontology reasoner with some unique features. We discuss its possibilities for fuzzy ontology representation, the supported reasoning services, the different interfaces to interact with it, some implementation details, a comparison with other fuzzy ontology reasoners, and an overview of the main applications that have used it so far.Source: Knowledge-based systems 95 (2016): 12–34. doi:10.1016/j.knosys.2015.11.017
DOI: 10.1016/j.knosys.2015.11.017