With recent regulatory advances, modern enterprises have to not only comply with regulations but have to be prepared to provide explanation of proof of (non-)compliance. On top of compliance checking, this necessitates modeling concepts from regulations and enterprise operations so that stakeholder-specific and close to natural language explanations could be generated. We take a step in this direction by using Semantics of Business Vocabulary and Rules to model and map vocabularies of regulations and operations of enterprise. Using these vocabularies and leveraging proof generation abilities of an existing compliance engine, we show how such explanations can be created. Basic natural language explanations that we generate can be easily enriched by adding requisite domain knowledge to the vocabularies.
Rulelog is in process of industry standardization via RuleML and W3C:
RIF-Rulelog specification, version of of May 24, 2013, Michael Kifer, ed. RIF-Rulelog is a powerful dialect of W3C Rule Interchange Format (RIF) that is in draft as a submission from RuleML to W3C.
Several industry standards in the areas are based heavily on our team’s contributions to the authoring/editing of the specifications and conducting the underlying research and earlier-phase standards design. These include most notably the two most important industry standards on rules knowledge:
W3C Rule Interchange Format (RIF), which is primarily based on the RuleML standards design (semantic web rules)
W3C OWL 2 RL Profile (rule-based web ontologies)
The team has also contributed to the development of W3C SPARQL and ISO Common Logic, and been strongly involved in other related standardization efforts at OMG and Oasis.
Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-RuleML
UML class diagrams (UCDs) are a widely adopted formalism
for modeling the intensional structure of a software system. Although
UCDs are typically guiding the implementation of a system, it is common
in practice that developers need to recover the class diagram from an
implemented system. This process is known as reverse engineering. A
fundamental property of reverse engineered (or simply re-engineered)
UCDs is consistency, showing that the system is realizable in practice.
In this work, we investigate the consistency of re-engineered UCDs, and
we show is pspace-complete. The upper bound is obtained by exploiting
algorithmic techniques developed for conjunctive query answering under
guarded Datalog+/-, that is, a key member of the Datalog+/- family
of KR languages, while the lower bound is obtained by simulating the
behavior of a polynomial space Turing machine.
RuleML2015: Rule Generalization Strategies in Incremental Learning of Disjunc...RuleML
Symbolic Machine Learning systems and applications, especially
when applied to real-world domains, must face the problem of
concepts that cannot be captured by a single definition, but require several
alternate definitions, each of which covers part of the full concept
extension. This problem is particularly relevant for incremental systems,
where progressive covering approaches are not applicable, and the learning
and refinement of the various definitions is interleaved during the
learning phase. In these systems, not only the learned model depends
on the order in which the examples are provided, but it also depends on
the choice of the specific definition to be refined. This paper proposes
different strategies for determining the order in which the alternate definitions
of a concept should be considered in a generalization step, and
evaluates their performance on a real-world domain dataset.
Explanation of Proofs of Regulatory (Non-)Compliance Using Semantic VocabulariesDr.-Ing. Sagar Sunkle
With recent regulatory advances, modern enterprises have to not only comply with regulations but have to be prepared to provide explanation of proof of (non-)compliance. On top of compliance checking, this necessitates modeling concepts from regulations and enterprise operations so that stakeholder-specific and close to natural language explanations could be generated. We take a step in this direction by using Semantics of Business Vocabulary and Rules to model and map vocabularies of regulations and operations of enterprise. Using these vocabularies and leveraging proof generation abilities of an existing compliance checking technique, we show how such explanations can be created. Basic natural language explanations that we generate can be easily enriched by adding requisite domain knowledge to the vocabularies.
Rulelog is in process of industry standardization via RuleML and W3C:
RIF-Rulelog specification, version of of May 24, 2013, Michael Kifer, ed. RIF-Rulelog is a powerful dialect of W3C Rule Interchange Format (RIF) that is in draft as a submission from RuleML to W3C.
Several industry standards in the areas are based heavily on our team’s contributions to the authoring/editing of the specifications and conducting the underlying research and earlier-phase standards design. These include most notably the two most important industry standards on rules knowledge:
W3C Rule Interchange Format (RIF), which is primarily based on the RuleML standards design (semantic web rules)
W3C OWL 2 RL Profile (rule-based web ontologies)
The team has also contributed to the development of W3C SPARQL and ISO Common Logic, and been strongly involved in other related standardization efforts at OMG and Oasis.
Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-RuleML
UML class diagrams (UCDs) are a widely adopted formalism
for modeling the intensional structure of a software system. Although
UCDs are typically guiding the implementation of a system, it is common
in practice that developers need to recover the class diagram from an
implemented system. This process is known as reverse engineering. A
fundamental property of reverse engineered (or simply re-engineered)
UCDs is consistency, showing that the system is realizable in practice.
In this work, we investigate the consistency of re-engineered UCDs, and
we show is pspace-complete. The upper bound is obtained by exploiting
algorithmic techniques developed for conjunctive query answering under
guarded Datalog+/-, that is, a key member of the Datalog+/- family
of KR languages, while the lower bound is obtained by simulating the
behavior of a polynomial space Turing machine.
RuleML2015: Rule Generalization Strategies in Incremental Learning of Disjunc...RuleML
Symbolic Machine Learning systems and applications, especially
when applied to real-world domains, must face the problem of
concepts that cannot be captured by a single definition, but require several
alternate definitions, each of which covers part of the full concept
extension. This problem is particularly relevant for incremental systems,
where progressive covering approaches are not applicable, and the learning
and refinement of the various definitions is interleaved during the
learning phase. In these systems, not only the learned model depends
on the order in which the examples are provided, but it also depends on
the choice of the specific definition to be refined. This paper proposes
different strategies for determining the order in which the alternate definitions
of a concept should be considered in a generalization step, and
evaluates their performance on a real-world domain dataset.
Explanation of Proofs of Regulatory (Non-)Compliance Using Semantic VocabulariesDr.-Ing. Sagar Sunkle
With recent regulatory advances, modern enterprises have to not only comply with regulations but have to be prepared to provide explanation of proof of (non-)compliance. On top of compliance checking, this necessitates modeling concepts from regulations and enterprise operations so that stakeholder-specific and close to natural language explanations could be generated. We take a step in this direction by using Semantics of Business Vocabulary and Rules to model and map vocabularies of regulations and operations of enterprise. Using these vocabularies and leveraging proof generation abilities of an existing compliance checking technique, we show how such explanations can be created. Basic natural language explanations that we generate can be easily enriched by adding requisite domain knowledge to the vocabularies.
presentation from my thesis defense on text summarization, discusses already existing state of art models along with efficiency of AMR or Abstract Meaning Representation for text summarization, we see how we can use AMRs with seq2seq models. We also discuss other techniques such as BPE or Byte Pair Encoding and its effectiveness for the task. Also we see how data augmentation with POS tags and AMRs effect the summarization with s2s learning.
Schema-agnositc queries over large-schema databases: a distributional semanti...Andre Freitas
The evolution of data environments towards the growth in the size, complexity, dy-
namicity and decentralisation (SCoDD) of schemas drastically impacts contemporary
data management. The SCoDD trend emerges as a central data management concern
in Big Data scenarios, where users and applications have a demand for more complete
data, produced by independent data sources, under different semantic assumptions and
contexts of use. Most Database Management Systems (DBMSs) today target a closed
communication scenario, where the symbolic schema of the database is known a priori
by the database user, which is able to interpret it in an unambiguous way. The context
in which the data is consumed and produced is well-defined and it is typically the
same context in which the data was created. In contrast, data management under the
SCoDD conditions target an open communication scenario where the symbolic system of
the database is unknown by the user and multiple interpretation contexts are possible.
In this case the database can be created under a different context from the database
user. The emergence of this new data environment demands the revisit of the semantic
assumptions behind databases and the design of data access mechanisms which can
support semantically heterogeneous (open communication) data environments.
This work aims at filling this gap by proposing a complementary semantic model for
databases, based on distributional semantic models. Distributional semantics provides a
complementary perspective to the formal perspective of database semantics, which supports
semantic approximation as a first-class database operation. Differently from models
which describe uncertain and incomplete data or probabilistic databases, distributional-
relational models focuses on the construction of conceptual approximation approaches
for databases, supported by a comprehensive semantic model automatically built from
large-scale unstructured data external to the database, which serves as a semantic/com-
monsense knowledge base. The semantic model can be used to support schema-agnosticqueries, i.e. abstracting the data consumer from a specific conceptualization behind the
data.
The proposed distributional-relational semantic model is supported by a distributional
structured vector space model, named τ −Space, which represents structured data under
a distributional semantic model representation which, in coordination with a query plan-
ning approach, supports a schema-agnostic query mechanism for large-schema databases.
The query mechanism is materialized in the Treo query engine and is evaluated using
schema-agnostic natural language queries.
The evaluation of the query mechanism confirms that distributional semantics provides
a high-recall, medium-high precision, and low maintainability solution to cope with
the abstraction and conceptual-level differences in schema-agnostic queries over largeschema/
schema-less open domain dataset
"Bilingual Terminology Extraction from TMX. A state-of-the-art overview." Presentation at Translating Europe Forum 2016. Focus on translation technology.
A simple web-based interface for advanced SNOMED CT queriesSnow Owl
SNOMED CT – as the most comprehensive biomedical ontology – has the potential to utilize semantic query methods that operate on the defining attributes of the concepts. This type of semantic querying is widely used, and some of the query languages already extended the attribute constraints with the option for limited lexical and metadata search criteria.
Since the introduction of RF2 the expressibility of SNOMED CT can increase, and various national extensions make use of this extensibility by adding specific description logic features that are relevant for their content.
An example for this is the Singapore Drug Dictionary that is based on the SNOMED CT concept model, but applies additional attribute types. The standard query languages are not powerful enough for such content.
This demonstration introduces a search interface that allows querying both standard SNOMED CT content as well as pharmaceutical extensions that utilize optional description logic extensions. These advanced queries are created by terminologists with an understanding of SNOMED CT. End-users can then use these queries to browse relevant subsets of the terminology appropriate for their use case. For example, clinicians can browse only drugs that are clinically relevant, while regulators can constrain their searches to controlled substances.
The tool also allows early validation of intensional reference set content, without having to implement and publish the reference sets. Practical examples using an online browser (Snow Owl Web) will highlight challenges and lessons learnt when working with real-world clinicians and regulators lacking SNOMED CT training.
Please see our website http://b2i.sg for further information.
Implementation of Urdu Probabilistic ParserWaqas Tariq
The implementation of Urdu probabilistic parser is the main contribution of this research work. In the beginning, a lot of Urdu text was collected from different sources. The sentences in the text were subsequently tagged. The tagged sentences were then parsed by a chart parser to formulate the rules. In the next step, probabilities were assigned to these rules to get a Probabilistic Context Free Grammar. For Urdu probabilistic parser, the idea of shift-reduce multi-path strategy is used. The developed software performs the syntactic analysis of a sentence, using a given set of probabilistic phrase structure rules. The parse with the highest probability is selected, as the most suitable one from a set of possible parses produced by this parser. The structure of each sentence is represented in the form of successive rules. This parser parses sentences with 74% accuracy.
RuleML2015: Input-Output STIT Logic for Normative SystemsRuleML
In this paper we study input/output STIT logic. We introduce the semantics, proof theory and prove the completeness theorem. Input/output STIT logic has more expressive power than Makinson and van der Torre’s input/output logic. We show that input/output STIT logic is decidable and free from Ross’ paradox.
presentation from my thesis defense on text summarization, discusses already existing state of art models along with efficiency of AMR or Abstract Meaning Representation for text summarization, we see how we can use AMRs with seq2seq models. We also discuss other techniques such as BPE or Byte Pair Encoding and its effectiveness for the task. Also we see how data augmentation with POS tags and AMRs effect the summarization with s2s learning.
Schema-agnositc queries over large-schema databases: a distributional semanti...Andre Freitas
The evolution of data environments towards the growth in the size, complexity, dy-
namicity and decentralisation (SCoDD) of schemas drastically impacts contemporary
data management. The SCoDD trend emerges as a central data management concern
in Big Data scenarios, where users and applications have a demand for more complete
data, produced by independent data sources, under different semantic assumptions and
contexts of use. Most Database Management Systems (DBMSs) today target a closed
communication scenario, where the symbolic schema of the database is known a priori
by the database user, which is able to interpret it in an unambiguous way. The context
in which the data is consumed and produced is well-defined and it is typically the
same context in which the data was created. In contrast, data management under the
SCoDD conditions target an open communication scenario where the symbolic system of
the database is unknown by the user and multiple interpretation contexts are possible.
In this case the database can be created under a different context from the database
user. The emergence of this new data environment demands the revisit of the semantic
assumptions behind databases and the design of data access mechanisms which can
support semantically heterogeneous (open communication) data environments.
This work aims at filling this gap by proposing a complementary semantic model for
databases, based on distributional semantic models. Distributional semantics provides a
complementary perspective to the formal perspective of database semantics, which supports
semantic approximation as a first-class database operation. Differently from models
which describe uncertain and incomplete data or probabilistic databases, distributional-
relational models focuses on the construction of conceptual approximation approaches
for databases, supported by a comprehensive semantic model automatically built from
large-scale unstructured data external to the database, which serves as a semantic/com-
monsense knowledge base. The semantic model can be used to support schema-agnosticqueries, i.e. abstracting the data consumer from a specific conceptualization behind the
data.
The proposed distributional-relational semantic model is supported by a distributional
structured vector space model, named τ −Space, which represents structured data under
a distributional semantic model representation which, in coordination with a query plan-
ning approach, supports a schema-agnostic query mechanism for large-schema databases.
The query mechanism is materialized in the Treo query engine and is evaluated using
schema-agnostic natural language queries.
The evaluation of the query mechanism confirms that distributional semantics provides
a high-recall, medium-high precision, and low maintainability solution to cope with
the abstraction and conceptual-level differences in schema-agnostic queries over largeschema/
schema-less open domain dataset
"Bilingual Terminology Extraction from TMX. A state-of-the-art overview." Presentation at Translating Europe Forum 2016. Focus on translation technology.
A simple web-based interface for advanced SNOMED CT queriesSnow Owl
SNOMED CT – as the most comprehensive biomedical ontology – has the potential to utilize semantic query methods that operate on the defining attributes of the concepts. This type of semantic querying is widely used, and some of the query languages already extended the attribute constraints with the option for limited lexical and metadata search criteria.
Since the introduction of RF2 the expressibility of SNOMED CT can increase, and various national extensions make use of this extensibility by adding specific description logic features that are relevant for their content.
An example for this is the Singapore Drug Dictionary that is based on the SNOMED CT concept model, but applies additional attribute types. The standard query languages are not powerful enough for such content.
This demonstration introduces a search interface that allows querying both standard SNOMED CT content as well as pharmaceutical extensions that utilize optional description logic extensions. These advanced queries are created by terminologists with an understanding of SNOMED CT. End-users can then use these queries to browse relevant subsets of the terminology appropriate for their use case. For example, clinicians can browse only drugs that are clinically relevant, while regulators can constrain their searches to controlled substances.
The tool also allows early validation of intensional reference set content, without having to implement and publish the reference sets. Practical examples using an online browser (Snow Owl Web) will highlight challenges and lessons learnt when working with real-world clinicians and regulators lacking SNOMED CT training.
Please see our website http://b2i.sg for further information.
Implementation of Urdu Probabilistic ParserWaqas Tariq
The implementation of Urdu probabilistic parser is the main contribution of this research work. In the beginning, a lot of Urdu text was collected from different sources. The sentences in the text were subsequently tagged. The tagged sentences were then parsed by a chart parser to formulate the rules. In the next step, probabilities were assigned to these rules to get a Probabilistic Context Free Grammar. For Urdu probabilistic parser, the idea of shift-reduce multi-path strategy is used. The developed software performs the syntactic analysis of a sentence, using a given set of probabilistic phrase structure rules. The parse with the highest probability is selected, as the most suitable one from a set of possible parses produced by this parser. The structure of each sentence is represented in the form of successive rules. This parser parses sentences with 74% accuracy.
RuleML2015: Input-Output STIT Logic for Normative SystemsRuleML
In this paper we study input/output STIT logic. We introduce the semantics, proof theory and prove the completeness theorem. Input/output STIT logic has more expressive power than Makinson and van der Torre’s input/output logic. We show that input/output STIT logic is decidable and free from Ross’ paradox.
Keynote for the initial PyCon AU, 26 June 2010 at the Sydney Masonic Center. This is the grand unveiling of the Plexus project - plexus.relationalspace.org.
Pioneers of Information Science in Europe: The Oeuvre of Norbert HenrichsWolfgang Stock
In this presentation we discuss the works and influence of Norbert Henrichs (born 1935), a pioneer of Information Science in Europe. In the context of philosophy documentation, Henrichs developed in the 1960s a dictionary-independent method of indexing: the Text-Word Method. This method works exclusively with the term material of the documents to be indexed. It starts by using a variant of syntactic indexing, viz. the formation of thematic chains. Documents indexed via the Text-Word Method form the basis for relatively ballast-free information retrieval, but also for studies in the history of ideas. Henrichs was a leading contributor to the formulation and realization of the German Information & Documentation (I&D) program (1974 – 1977). This widely noted political program planned for the world’s entire scientific and technical literature to be made available in 20 specialized information centers. Henrichs served as scientific executive director of the central German infrastructure provision within the I&D program, the “Society for Information and Documentation” (GID), from 1980 to 1985. Over the course of the 1980s, the I&D program broke down—mainly due to a lack of financing. At the Heinrich-Heine-University in Düsseldorf, Henrichs successfully developed a curriculum for information science, which—typically for Germany in the 1980s and 1990s—had no strong ties to either library science or computer science.
The eXtensible Markup Language (XML) is not a language itself, but rather a meta-language used to create markup languages to suit whatever purpose you may have. In this session you will learn the basic rules of XML and the philosophy behind it. You will also be introduced to the basics of the popular XML editor, oxygen.
II Konferencja Naukowa : Nauka o informacji (informacja naukowa) w okresie zmian, Warszawa, 15-16.04.2013 r. Instytut Informacji Naukowej i Studiów Bibliologicznych, Uniwersytet Warszawski
The 2nd Scientific Conference : Information Science in an Age of Change, April 15-16, 2013. Institute of Information and Book Studies, University of Warsaw
C++ open positions and popularity remain high as media has recently, and there is a reason for that: from the many languages and platforms that developers have available today, C++ features uncontested capabilities in power and performance, allowing innovation outside the box (just think on action games, natural user interfaces or augmented reality, to mention some). In this talk you’ll see the new features and technologies that are coming with Visual C++ vNext, helping you build compelling applications with a renewed developer experience. Don’t miss it!!
Solving Semantic Disparity and Explanation Problems in Regulatory Compliance Dr.-Ing. Sagar Sunkle
Modern enterprises increasingly face the challenge of keeping pace with regulatory compliances. Semantic disparity between regulation texts, their interpretations, and operational specifics of enterprise often leads enterprises to situations where it becomes difficult for them to establish what compliance means, how they are supposed to affect it in the operational practices, and how to prove that they comply when asked for explanations of (non-)compliance. We take a step toward reducing the semantic disparity by using semantic vocabularies to map regulations with available operational details of enterprise and utilize them in enacting compliance. We also propose to provide explanations of proofs of (non-)compliance. We report our ongoing work in this regard using the design science research (DSR) paradigm. Initial iterations of design cycle from DSR have been useful to us in identifying and matching stakeholder-specific goals in solving these problems.
Generative AI and Regulatory ComplianceDenis Gagné
Generative AI can aid businesses, especially in the banking and finance industry, to meet regulatory compliance challenges by extracting important terms, creating concept models, and generating code to align with specified obligations. By utilizing a knowledge entity model (KEM), organizations can achieve traceable implementations, reduce errors, and minimize subjective interpretations when integrating decision models with regulatory requirements.
Leveraging Business Rules in TIBCO BusinessEventsTim Bass
Leveraging Business Rules in TIBCO BusinessEvents, TIBCO, TUCON 2007, Tim Bass, Principal Global Architect, Director Emerging Technologies Group TIBCO Software Inc.
From Laws and Regulations to Decision AutomationDenis Gagné
Regulations are a set of obligations that apply to corporations and individuals. They can be established through laws or under the authority of a governing body. Regulations may explicitly define processes and rules, but often they prescribe outcomes or performances without detailing how to achieve them.
When an organization must comply with a regulation, it aligns its operations with the obligations specified in the regulation. Compliance is the action of ensuring this alignment. However, demonstrating compliance can be a challenge because organizations must be able to trace their implementation back to the regulation.
To create traceability, a knowledge entity model (KEM) is developed. This model represents the regulation using vocabulary, concept maps, and business rules. The KEM is derived from the text of the regulation, breaking it down into vocabulary terms, concept connections, and business rules.
Using the KEM, an automated solution can be created using decision automation and business process automation (DMN and BPMN). This solution links the business rules to the decision or process as a knowledge source, creating a traceable solution.
Taming the regulatory tiger with jwg and smartlogicAnn Kelly
From CEOs to board members to operational managers, regulatory compliance is an ongoing concern. In a rapidly changing marketplace where complex regulations come from multiple regulatory bodies, the consequences of non-compliance can be costly to the enterprise in time, money and damage to their reputation.
JWG, a London think tank, has created RegDelta – a state-of-the-art regulatory change management platform - that allows individual stakeholders to quickly understand the impact of regulations and maintain a single source of truth for their regulatory obligations.
Hear Elliot Burgess, Head of Product and Client Services at JWG and Paul Gunstone, Sales Director at Smartlogic discuss the challenges organizations face identifying and complying with relevant regulations, JWG’s approach to taming the regulatory tiger with semantics and see a demo of the JWG RegDelta platform.
All of us must have heard this proverb umpteen number of times in our lives. However, when it comes to elicitation, we tend to forget the same.Elicitation is possibly the most important job we business analysts do. I am surprised that many of us understand only few facets of elicitation such as requirements gathering and recording.Elicitation is much more than requirements gathering and recording. A good elicitation activity can significantly reduce effort in changes in requirements and subsequent changes to design, construction and testing activities.Here is an attempt to make our elicitation exercises more effective.
Oracle software can be tricky to manage and maintain a level of compliance. These slides offer key areas to review within your organisation and best practice guidelines to get better value from your investments.
Toward Better Mapping between Regulations and Operational Details of Enterpri...Dr.-Ing. Sagar Sunkle
Industry governance, risk, and compliance (GRC) solutions stand to gain from various analyses offered by formal compliance checking approaches. Such adoption is made difficult by the fact that most formal approaches assume that a mapping between concepts of regulations and models of operational specifics exists. We propose to use Semantics of Business Vocabularies and Rules along with similarity measures to create an explicit mapping between concepts of regulations and models of operational specifics of enterprises. We believe that this proposal takes a step toward adapting and leveraging formal compliance checking approaches in industry GRC solutions.
Industry@RuleML2015: Automated Decision Support for Financial Regulatory/Pol...RuleML
We present a novel technological approach, based on Textual Rulelog,
to automated decision support for financial regulatory/policy compliance, via
a case study on banking Regulation W from the US Federal Reserve. Legal
regulations and related bank operational policies in English documents are encoded
relatively inexpensively by authors into Rulelog, a highly expressive logical
knowledge representation. Key compliance queries are automatically answered
accurately and fully explained in English, understandable to non-IT compliance
staff and auditors. The prospective business impact of our approach over
the next decade or two is significantly increased productivity and systemic stability,
industry-wide, worth many billions of dollars.
Asset finance systems projects guide 101David Pedreno
You are starting, or have already started, an asset finance and leasing system implementation what are the typical pain points ahead? In this “101" guide and tips, Richmond Consulting Group looks at the key areas that will need attention if the journey is to be a smooth one.
Optimizing order to-cash (e-business suite) with GRC Advanced ControlsOracle
Mark Stebleton, Oracle GRC Advanced Controls Product Management and Daryl Geryol, Navillus Partners explain how to optimize your Order to Cash process.
Business Rules In Practice - An Empirical Study (IEEE RE'14 Paper)Walid Maalej
Business rules represent constraints in a domain, which need to be taken into account either during the development or the usage of a system. Motivated by the knowledge reuse potentials when developing systems within the same domain, we studied business rules in a large software company. We interviewed 11 experienced practitioners on how they understand, capture, and use business rules. We also studied the role of business rules in requirements engineering in the host organization. We found that practitioners have a very broad perception for this term, ranging from flows of business processes to directives for calling external system interfaces. We identified 27 types of rules, which are typically captured as a free text in requirements documents and other project documentation. Practitioners stated the need to capture this tacit form of domain knowledge and to trace it to other artifacts as it impacts all activities in a software engineering project. We distill our results in 17 findings and discuss the implications for researchers and practitioners.
Similar to RuleML2015: Explanation of proofs of regulatory (non-)complianceusing semantic vocabularies (20)
Aggregates in Recursion: Issues and SolutionsRuleML
Aggregates are commonplace in database query languages. It is natural to include them also into logic programming. However, doing so raises a number of issues, in particular when aggregates are used in conjunction with recursive definitions. This talk will shed some light on the underlying issues and some of the solutions proposed in the literature so far.
A software agent controlling 2 robot arms in co-operating concurrent tasksRuleML
TeleoR is a major extension of Nilsson’s Teleo-Reactive (TR)
rule based robotic agent programming language. Programs comprise sequences of guarded action rules grouped into parameterised procedures.
The guards are deductive queries to a set of rapidly changing percept and other dynamic facts in the agent’s Belief Store. The actions are either tuples of primitive actions for external robotic resources, to be executed in parallel, or a single call to a TeleoR procedure, which can be a recursive call. The guards form a sub-goal tree routed at the guard of the first rule. When partially instantiated by the arguments of some call, this guard is the goal of the call.
TeleoR extends TR in being typed and higher order, with extra forms of rules that allow finer control over sub-goal achieving task behaviour.
Its Belief Store inference language is a higher order logic+function rule language, QuLog. QuLog also has action rules and primitive actions for updating the Belief Store and sending messages. The action of a TeleoR rule may be a combination of the action of a TR rule and a sequence of
QuLog actions. TeleoR’s most important extension of TR is the concept of task atomic procedures, some arguments of which belong to a special but application specific resource type. This allows the high level programming of multitasking agents using multiple robotic resources. When two or more tasks
need to use overlapping resources their use is alternated between task atomic calls in each task, in such a way that there is no interference, deadlock or task starvation.
This multi-task programming is illustrated by giving the essentials of a program for an agent controlling two robotic arms in multiple block tower assembly tasks. It has been used to control both a Python interactive graphical simulation and a Baxter robot building real block towers, in each case with help or hindrance from a human. The arms move in parallel whenever it can be done without risk of clashing.
Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...RuleML
The Decision Management (DM) Community Challenge of
March 2016 consisted of creating decision models from ten English Port Clearance Rules inspired by the International Ship and Port Facility Security
Code. Based on an analysis of the moderately controlled English
rules and current online solutions, we formalized the rules in PositionalSlotted,
Object-Applicative (PSOA) RuleML. This resulted in: (1) a
reordering, subgrouping, and explanation of the original rules on the
specialized decision-model expressiveness level of (deontically contextualized)
near-Datalog, non-recursive, near-deterministic, ground-queried,
and non-subpredicating rules; (2) an object-relational PSOA RuleML
rulebase which was complemented by facts to form a knowledge base queried in PSOATransRun for decision-making. Thus, the DM and logical formalizations get connected, which leads to generalized decision models with Hornlog, recursive, non-deterministic, non-ground-queried, and subpredicating rules.
Big data, with its four main characteristics (Volume, Velocity,
Variety, and Veracity) pose challenges to the gathering, management, analytics, and visualization of events. These very same four characteristics, however, also hold a great promise in unlocking the story behind data. In this talk, we focus on the observation that event creation is guided by processes. For example, GPS information, emitted by buses in an urban setting follow the bus scheduled route. Also, RTLS information about the whereabouts of patients and nurses in a hospital is guided by the predefined schedule of work. With this observation at hand, we thoroughly seek a method for mining, not the data, but rather the rules that guide data creation and show how, by knowing such rules, big data tasks become more efficient and more effective. In particular, we demonstrate how, by knowing the rules that govern event creation, we can detect complex events sooner and make use of historical data to predict future behaviors.
RuleML 2015: Ontology Reasoning using Rules in an eHealth ContextRuleML
Traditionally, nurse call systems in hospitals are rather simple:
patients have a button next to their bed to call a nurse. Which specific
nurse is called cannot be controlled, as there is no extra information
available. This is different for solutions based on semantic knowledge:
if the state of care givers (busy or free), their current position, and for
example their skills are known, a system can always choose the best
suitable nurse for a call. In this paper we describe such a semantic nurse
call system implemented using the EYE reasoner and Notation3 rules.
The system is able to perform OWL-RL reasoning. Additionally, we use
rules to implement complex decision trees. We compare our solution to
an implementation using OWL-DL, the Pellet reasoner, and SPARQL
queries. We show that our purely rule-based approach gives promising
results. Further improvements will lead to a mature product which will
significantly change the organization of modern hospitals.
RuleML 2015: Semantics of Notation3 Logic: A Solution for Implicit Quantifica...RuleML
Since the development of Notation3 Logic, several years have
passed in which the theory has been refined and used in practice by different reasoning engines such as cwm, FuXi or EYE. Nevertheless, a clear model-theoretic definition of its semantics is still missing. This leaves room for individual interpretations and renders it difficult to make clear
statements about its relation to other logics such as DL or FOL or even about such basic concepts as correctness. In this paper we address one of the main open challenges: the formalization of implicit quantification.
We point out how the interpretation of implicit quantifiers differs in two of the above mentioned reasoning engines and how the specification, proposed in the W3C team submission, could be formalized. Our formalization is then put into context by integrating it into a model-theoretic definition of the whole language. We finish our contribution by arguing why universal quantification should be handled differently than currently
prescribed.
Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...RuleML
In order to enhance interoperability and productivity in the develop-ment of situation-aware applications for disaster management, proper mecha-nisms and guidelines are required. They must address the lack of semantics in modelling emergency situations. In addition, the ever-changing and unpredicta-ble nature of disaster scenarios present challenges for information processing and collaboration. This paper proposes a framework that combines the follow-ing elements: (i) a foundational ontology for temporal conceptualization; (ii) well-founded specifications of structural and behavioral models; (iii) a CEP en-gine based on a distributed rule-based platform for situation management; (iv) a model-driven approach. We illustrate the operation of the framework with a scenario for monitoring tuberculosis epidemy.
Rule Generalization Strategies in Incremental Learning of Disjunctive ConceptsRuleML
Symbolic Machine Learning systems and applications, especially when applied to real-world domains, must face the problem of concepts that cannot be captured by a single definition, but require several alternate definitions, each of which covers part of the full concept extension. This problem is particularly relevant for incremental systems, where progressive covering approaches are not applicable, and the learning and refinement of the various definitions is interleaved during the learning phase. In these systems, not only the learned model depends on the order in which the examples are provided, but it also depends on
the choice of the specific definition to be refined. This paper proposes different strategies for determining the order in which the alternate definitions of a concept should be considered in a generalization step, and
evaluates their performance on a real-world domain dataset.
RuleML 2015 Constraint Handling Rules - What Else?RuleML
Constraint Handling Rules (CHR) is both a versatile theoretical formalism based on logic and an efficient practical high-level programming language based on rules and constraints.
Procedural knowledge is often expressed by if-then rules, events and actions are related by reaction rules, change is expressed by update rules. Algorithms are often specified using inference rules, rewrite rules, transition rules, sequents, proof rules, or logical axioms. All these kinds of rules can be directly written in CHR. The clean logical semantics of CHR facilitates non-trivial program analysis and transformation. About a dozen implementations of CHR exist in Prolog, Haskell, Java, Javascript and C. Some of them allow to apply millions of rules per second. CHR is also available as WebCHR for online experimentation with more than 40 example programs. More than 200 academic and industrial projects worldwide use CHR, and about 2000 research papers reference it.
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box RuleML
The traditional semantics for First Order Logic (sometimes called Tarskian semantics) is based on the notion of interpretations of constants. Herbrand semantics is an alternative semantics based directly on truth assignments for ground sentences rather than interpretations of constants. Herbrand semantics is simpler and more intuitive than Tarskian semantics; and, consequently, it is easier to teach and learn. Moreover, it is more expressive. For example, while it is not possible to finitely axiomatize integer arithmetic with Tarskian semantics, this can be done easily with Herbrand Semantics. The downside is a loss of some common logical properties, such as compactness and completeness. However, there is no loss of inferential power. Anything that can be proved according to Tarskian semantics can also be proved according to Herbrand semantics. In this presentation, we define Herbrand semantics; we look at the implications for research on logic and rules systems and automated reasoning; and and we assess the potential for popularizing logic.
Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...RuleML
Data distribution
•Public and private
•Data complexity
•Rich in attributes and location based
•Time dimension
•Example of data model from the Norwegian Mapping Authority
RuleML2015: Binary Frontier-guarded ASP with Function SymbolsRuleML
It has been acknowledged that emerging Web applications
require features that are not available in standard rule languages like
Datalog or Answer Set Programming (ASP), e.g., they are not powerful
enough to deal with anonymous values (objects that are not explicitly
mentioned in the data but whose existence is implied by the background
knowledge). In this paper, we introduce a new rule language based on
ASP extended with function symbols, which can be used to reason about
anonymous values. In particular, we define binary frontier-guarded programs
(BFG programs) that allow for disjunction, function symbols, and
negation under the stable model semantics. In order to ensure decidability,
BFG programs are syntactically restricted by allowing at most
binary predicates and by requiring rules to be frontier-guarded. BFG programs
are expressive enough to simulate ontologies expressed in popular
Description Logics (DLs), capture their recent non-monotonic extensions,
and can simulate conjunctive query answering over many standard DLs.
We provide an elegant automata-based algorithm to reason in BFG programs,
which yields a 3ExpTime upper bound for reasoning tasks like
deciding consistency or cautious entailment. Due to existing results, these
problems are known to be 2ExpTime-hard.
RuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge PlatformsRuleML
API4KP (API for Knowledge Platforms) is a standard
development effort that targets the basic administration services as
well as the retrieval, modification and processing of expressions in
machine-readable languages, including but not limited to knowledge
representation and reasoning (KRR) languages, within heterogeneous
(multi-language, multi-nature) knowledge platforms. KRR languages of
concern in this paper include but are not limited to RDF(S), OWL,
RuleML and Common Logic, and the knowledge platforms may support
one or several of these. Additional languages are integrated using mappings
into KRR languages. A general notion of structure for knowledge
sources is developed using monads. The presented API4KP metamodel,
in the form of an OWL ontology, provides the foundation of an abstract
syntax for communications about knowledge sources and environments,
including a classification of knowledge source by mutability, structure,
and an abstraction hierarchy as well as the use of performatives (inform,
query, ...), languages, logics, dialects, formats and lineage. Finally, the
metamodel provides a classification of operations on knowledge sources
and environments which may be used for requests (message-passing).
RuleML2015: Rule-Based Exploration of Structured Data in the BrowserRuleML
We present Dexter, a browser-based, domain-independent
structured-data explorer for users. Dexter enables users to explore data
from multiple local and Web-accessible heterogeneous data sources such
as files, Web pages, APIs and databases in the form of tables. Dexter’s
users can also compute tables from existing ones as well as validate
the tables (base or computed) through declarative rules. Dexter enables
users to perform ad hoc queries over their tables with higher expressivity
than that is supported by the underlying data sources. Dexter evaluates
a user’s query on the client side while evaluating sub-queries on remote
sources whenever possible. Dexter also allows users to visualize and share
tables, and export (e.g., in JSON, plain XML, and RuleML) tables along
with their computation rules. Dexter has been tested for a variety of data
sets from domains such as government and apparel manufacturing. Dexter
is available online at http://dexter.stanford.edu.
RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...RuleML
Data quality assessment and data cleaning are context
dependent activities. Starting from this observation, in previous work
a context model for the assessment of the quality of a database was
proposed. A context takes the form of a possibly virtual database or
a data integration system into which the database under assessment is
mapped, for additional analysis, processing, and quality data extraction.
In this work, we extend contexts with dimensions, and by doing so, multidimensional
data quality assessment becomes possible. At the core of
multidimensional contexts we find ontologies written as Datalog
±
programs
with provably good properties in terms of query answering. We
use this language to represent dimension hierarchies, dimensional constraints,
dimensional rules, and specifying quality data. Query answering
relies on and triggers dimensional navigation, and becomes an important
tool for the extraction of quality data.
RuleML2015: Compact representation of conditional probability for rule-based...RuleML
Context-aware systems gained huge popularity in recent
years due to rapid evolution of personal mobile devices. Equipped with
variety of sensors, such devices are sources of a lot of valuable information
that allows the system to act in an intelligent way. However, the
certainty and presence of this information may depend on many factors
like measurement accuracy or sensor availability. Such a dynamic
nature of information may cause the system not to work properly or
not to work at all. To allow for robustness of the context-aware system
an uncertainty handling mechanism should be provided with it. Several
approaches were developed to solve uncertainty in context knowledge
bases, including probabilistic reasoning, fuzzy logic, or certainty
factors. In this paper, we present a representation method that combines
strengths of rules based on the attributive logic and Bayesian networks.
Such a combination allows efficiently encode conditional probability distribution
of random variables into a reasoning structure called XTT2.
This provides a method for building hybrid context-aware systems that
allows for robust inference in uncertain knowledge bases.
RuleML2015: Learning Characteristic Rules in Geographic Information SystemsRuleML
We provide a general framework for learning characterization
rules of a set of objects in Geographic Information Systems (GIS) relying
on the definition of distance quantified paths. Such expressions specify
how to navigate between the different layers of the GIS starting from
the target set of objects to characterize. We have defined a generality
relation between quantified paths and proved that it is monotonous with
respect to the notion of coverage, thus allowing to develop an interactive
and effective algorithm to explore the search space of possible rules. We
describe GISMiner, an interactive system that we have developed based
on our framework. Finally, we present our experimental results from a
real GIS about mineral exploration.
RuleML2015: Using Substitutive Itemset Mining Framework for Finding Synonymou...RuleML
Over the last two decades frequent itemset and association
rule mining has attracted huge attention from the scientific community
which resulted in numerous publications, models, algorithms, and optimizations
of basic frameworks. In this paper we introduce an extension
of the frequent itemset framework, called substitutive itemsets. Substitutive
itemsets allow to discover equivalences between items, i.e., they
represent pairs of items that can be used interchangeably in many contexts.
In the paper we present basic notions pertaining to substitutive
itemsets, describe the implementation of the proposed method available
as a RapidMiner plugin, and illustrate the use of the framework for mining
substitutive object properties in the Linked Data.
RuleML2015: User Extensible System to Identify Problems in OWL Ontologies and...RuleML
The Semantic Web uses ontologies to associate meaning to
Web content so machines can process it. One inherent problem to this
approach is that, as its popularity increases, there is an ever growing
number of ontologies available to be used, leading to difficulties in
choosing appropriate ones. With that in mind, we created a system that
allows users to evaluate ontologies/rules. It is composed by the Metadata
description For Ontologies/Rules (MetaFOR), an ontology in OWL, and
a tool to convert any OWL ontology to MetaFOR. With the MetaFOR
version of an ontology, it is possible to use SWRL rules to identify anomalies
in it. These can be problems already documented in the literature or
user defined ones. SWRL is familiar to users, so it is easier to define new
project specific anomalies. We present a case study where the system
detects 9 problems, from the literature, and two user defined ones
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
RuleML2015: Explanation of proofs of regulatory (non-)complianceusing semantic vocabularies
1. Explanation of Proofs of Regulatory (Non-)Compliance
Using Semantic Vocabularies
Sagar Sunkle, Deepali kholkar, and Vinay Kulkarni
Tata Consultancy Services Research, India
2. Regulatory Compliance
o Increasing spend on compliance in Billions of $
o Demand for governance, risk, and compliance (GRC) growing worldwide-
• Canada, Japan, India, Australia, South Africa, and members of EU having a number of
domain- and geography-specific regulations
o Non-compliance is penalized severely;
• Compliance difficult to achieve since it is uncertain in many cases what constitutes
compliance and how it will affect the business-as-usual
Explanation of Proof of Regulatory (Non-) Compliance
o Increasing demand to prove and explain (non-)compliance in a way tailored to specific
stakeholders
o Should be useful in regulatory negotiations as well as in fulfillment of business objectives
o Requirements:
Requires access to diagnostic information in compliance checking
Relevant concepts in both regulations and operational practices need to be modeled
Motivation
3. Use existing compliance engine- We use DR-Prolog
o Compliance engine based on modal defeasible logic
o Possible to access diagnostic information from Prolog trace- prior work by others exists on
proof generation using DR-Prolog
Domain-specific compliance
o Our engagements reveal that stakeholder-specific proof explanations are in demand
o Difficult for business/operational stakeholders to interpret technical proofs
o Close to natural language explanation deemed a starting point to make formal proofs
relevant
Semantics of Business Vocabulary and Rules
o Express meaning of concepts
o Two sets of concepts- legal and business
o Can accommodate natural language representation/information of concepts
Tailor proofs so that only the relevant rules and facts are separated out
Basics of the Approach
4. Manual
Specification
Implementation Technology in
boldface
Specification Language/format in
Italics
Legal Text
Business
Process Models
Vocabulary
EMF Ecore
SBVR Editor
Assurance
Workbench TCS
Rules Facts
OMG SBVR
Metamodel
BPMN 2.0
DR-Prolog
TuProlog
DR-Prolog
TuProlog
Metainterpreter in Prolog
Interpretation Trace
TuProlog
Java
Procedure Box
Abstraction in Trace
Success Rules
and Facts
Failure Rules
and Facts
Natural
Language
Explanation
Queries with
Apache
Metamodel API
XML
Representation
of SBVR
FreeMarker API
Natural Language
Templates
Implementation Architecture
5. Tailoring Proofs using Metainterpreter
Defeasible Metaprogram
o A logic metaprogram simulates the proof theory of modal defeasible logic and reasons over
the theory
• The problem theory is expressed in terms of the metaprogram predicates
• The metaprogram is a Prolog program
Trace using metainterpreter- leveraging procedure box abstraction
o The metaprogram and problem theory is meta-interpreted to reveal procedure box for given
query
o Predicate invocation type- one of CALL, EXIT, FAIL, REDO
o To obtain relevant rules and facts in a given successful and failed procedure, treat the box
differently
6. Accessing the Trace
Meta-interpreter produces trace that minimally contains three pieces of
information
1. Depth of predicate invocation
2. Invocation type which is one of CALL, EXIT,FAIL, and REDO
3. Current predicate being processed
Example Trace
0’CALL ’defeasibly(client_account_data(17,open_account),obligation)
1’CALL ’strictly(client_account_data(17,open_account),obligation)
2’CALL ’fact(obligation(client_account_data(17,open_account)))
2’FAIL ’fact(obligation(client_account_data(17,open_account)))
…
Meaning of innovation types-
o CALL= predicate is entered/invoked
o EXIT= successfully returned from
o FAIL= completely failed
o REDO= failed but backtracked
7. Processing the Procedure Box Abstraction
Successful Procedure
o We are interested in CALL EXIT pairs as
shown on left
o Remove successive CALL FAIL pairs
indicating failed invocations
o Failed invocations may occur at various
depths, so recursively look for them and
remove them
Failed Procedure
o We are interested in CALL FAIL pairs as
shown on right
o Keep only successive CALL FAIL pairs and
remove the rest
o No need to recurse
8. Building the Vocabularies- I
Business vocabulary
o Semantic community and sub-
communities owning the regulation and to
which the regulation applies
o Shared understanding of an area, i.e., body
of shared meanings
Meanings and characteristics
o Categorical concepts with specific details as
characteristics
9. Building the Vocabularies- II
Body of guidance
o Logical formulations based on logical
operations
Terminological dictionary
o Designations or alternate names for
various concepts, definitions for concepts
and natural language statements for
policies stated in the regulation
o capture the vocabulary used by the
enterprise in its business processes
Mapping rules to processes
o Every verb concept in the regulation body of concepts is mapped to corresponding verb concept
wording from the process terminological dictionary.
o This mapping is used to look up consequent terms of rules and the corresponding process entity is
treated as a placeholder for compliance implementation of the rule
10. Manual
Specification
Implementation Technology in
boldface
Specification Language/format in
Italics
Legal Text
Business
Process Models
Vocabulary
EMF Ecore
SBVR Editor
Assurance
Workbench TCS
Rules Facts
OMG SBVR
Metamodel
BPMN 2.0
DR-Prolog
TuProlog
DR-Prolog
TuProlog
Metainterpreter in Prolog
Interpretation Trace
TuProlog
Java
Procedure Box
Abstraction in Trace
Success Rules
and Facts
Failure Rules
and Facts
Natural
Language
Explanation
Queries with
Apache
Metamodel API
XML
Representation
of SBVR
FreeMarker API
Natural Language
Templates
Revisiting Implementation Architecture
11. Reserve Bank of India’s
Know Your Customer
regulations for a salaried
employee at a private
employer opening an
account at an Indian Bank
An example of banking domain regulation
12. Success Facts for Client_ID 17
[
fact(client_data(17,ind,pse)).,
fact(pse_data(17,approvedCorporate)).,
fact(pse_KYC_document_data(17,acceptApprovedCor
pCertificate,pse_kyc_document_set)).
]
Success Rule r3
Client_ID 17 fulfills all
Obligatory requisites.
The processed trace
shows facts in
the successful invocation of
rule r3.
13. Success Facts for Client_ID 17
[
fact(client_data(17,ind,pse)).,
fact(pse_data(17,approvedCorporate)).,
fact(pse_KYC_document_data(17,acceptApprovedCor
pCertificate,pse_kyc_document_set)).
]
Success Rule r3
<containsConcepts
xsi:type="SBVR.MeaningandRepresentationVocabulary:generalconcept">
<Id>pse</Id>
<representation>pse_data</representation>
<characteristic>notApprovedCorporate</characteristic>
<characteristic>approvedCorporate</characteristic>
<moreGeneralConcept>ind</moreGeneralConcept>
</containsConcepts>
</includesBodyOfConcepts>
<includesBodyOfConcepts Id="RBI_KYCRegulationConcepts">
Business Vocabulary
with Characteristics
Concept pse and its
characteristics such as
approvedCorporate are
defined in the business
context and also in the
meaning and
representation vocabulary.
14. Success Facts for Client_ID 17
[
fact(client_data(17,ind,pse)).,
fact(pse_data(17,approvedCorporate)).,
fact(pse_KYC_document_data(17,acceptApprovedCor
pCertificate,pse_kyc_document_set)).
]
Success Rule r3
<includesBodyOfGuidance Id="RBI_KYCRules">
<includesElementsOfGuidance Id="r3">
<Id>r3</Id>
<isMeantBy xsi:type="SBVR.LogicalFormulationofSemanticsVocabulary:obligationformulation">
<antecedent xsi:type="SBVR.LogicalFormulationofSemanticsVocabulary:conjunction">
<logicalOperand xsi:type="SBVR.LogicalFormulationofSemanticsVocabulary:atomicformulation">
<Id>ind</Id>
<isBasedOn>client_is_ind</isBasedOn>
</logicalOperand>
…
</isMeantBy>
</includesElementsOfGuidance>
</includesBodyOfGuidance>
Business
Rules
Vocabulary
The rules vocabulary
notes the rules and
concepts involved.
15. Success Facts for Client_ID 17
[
fact(client_data(17,ind,pse)).,
fact(pse_data(17,approvedCorporate)).,
fact(pse_KYC_document_data(17,acceptApprovedCor
pCertificate,pse_kyc_document_set)).
]
Success Rule r3
<SBVR.VocabularyforDescribingBusinessVocabularies:ComplianceModel>
<contains Id="RBI_reference">
<presentsVocabulary Id="RBI_RegulationVocabulary"/>
<expressesBodyOfMeanings Id="RBI_KYCRegulation"/>
<includes xsi:type="SBVR.VocabularyforDescribingBusinessVocabularies:owneddefinition">
<Id>approvedCorporate</Id>
<expression>Employer_is_a_corporate_approved_by_the_bank</expression>
<meaning>approvedCorporate</meaning>
</includes>
<includes xsi:type="SBVR.VocabularyforDescribingBusinessRules:rulestatement"><Id>r3_stmt</Id
<expression>It_is_obligatory_for_bank_to_obtain_requisite_documents_Including
_approved_employer_certificate_and_additionally_at_least_one_valid_
document_ from_individual_who_is_a_private_salaried_employee
_in_order_to_open_account”
</expression>
<meaning>r3</meaning>
</SBVR.VocabularyforDescribingBusinessVocabularies:ComplianceModel>
Terminological
Dictionary
The terminological
dictionary contains
the natural
language
representation of
the rule in addition
to process
concepts.
16. SBVR model is in XML which needs to be queried to project values of requisite
concepts in the explanation
We use Apache Metamodel to query the vocabularies
o Type-safe SQL-like API for querying any data store
o XML files are hierarchical and MetaModel tables are tabular, so some mapping overhead;
carried out with XPath expressions
The projected results are filled into templates
This templates is filled in with
o Rule ID, rule statement [From the terminological dictionary and rules vocabulary
respectively],
o Type of concept (in the case study, a banking customer), specific instance, description, and its
ID [From the business context and meaning and representation vocabulary]
Constructing Natural Language Explanation- I
As per rule _, _. For current _that is _; _. Therefore compliance
is achieved for current _ _.
17. This gives a natural language statement like the following-
Similar statement can be constructed whenever obligations are violated in
specific instances.
Constructing Natural Language Explanation- II
18. Summary and Future Work
Summary
o Using vocabularies of legal and operational concepts and existing compliance
engine, we were able to construct simple natural language explanations
Ongoing- Stakeholder-specific explanations [such as business/legal stakeholders]
o Currently general explanation
o Stakeholder-specific interpretations of business context vocabulary can be
represented in meaning and representation vocabularies and terminological
dictionaries
In near future- Elaborating business/legal reasons
o Ideally reasons for enterprises actions should be recorded in the explanations
o For this, business/legal goals need to be modeled separately and related with
the concepts in the business context vocabulary