A Method for Defining Human-Machine Micro-task Workflows for Gathering Legal Information

With the growing popularity of micro-task crowdsourcing platforms, new workflow-based micro-task crowdsourcing approaches are starting to emerge. Such workflows occur in legal, political and conflict resolution domains as well, presenting new challenges, namely in micro-task specification and human-machine interaction, which result mostly from the flow of unstructured data. Domain ontologies provide the structure and semantics required to describe the data flowing throughout the workflow in a way understandable to both humans and machines. This paper presents a method for the construction of micro-task workflows from legal domain ontologies. The method is currently being employed in the context of the UMCourt project in order to formulate information retrieval and conflict resolution workflows.

[1]  Alexis Battle,et al.  The jabberwocky programming environment for structured social computing , 2011, UIST.

[2]  José Neves,et al.  The Relationship between Stress and Conflict Handling Style in an ODR Environment , 2012, JSAI-isAI Workshops.

[3]  Lydia B. Chilton,et al.  TurKit: human computation algorithms on mechanical turk , 2010, UIST.

[4]  Timothy Chklovski,et al.  Learner: a system for acquiring commonsense knowledge by analogy , 2003, K-CAP '03.

[5]  Björn Hartmann,et al.  Turkomatic: automatic recursive task and workflow design for mechanical turk , 2011, Human Computation.

[6]  Aniket Kittur,et al.  CrowdForge: crowdsourcing complex work , 2011, UIST.

[7]  Benjamin B. Bederson,et al.  Human computation: a survey and taxonomy of a growing field , 2011, CHI.

[8]  José Neves,et al.  Using Case-Based Reasoning and Principled Negotiation to provide decision support for dispute resolution , 2012, Knowledge and Information Systems.

[9]  Diego Calvanese,et al.  The Description Logic Handbook: Theory, Implementation, and Applications , 2003, Description Logic Handbook.

[10]  Aldo Gangemi,et al.  The Computational Ontology Perspective: Design Patterns for Web Ontologies , 2011, Approaches to Legal Ontologies.

[11]  Takao Terano,et al.  New Frontiers in Artificial Intelligence , 2008, Lecture Notes in Computer Science.

[12]  Diego Calvanese,et al.  The Description Logic Handbook , 2007 .

[13]  Len Bass,et al.  User interface software , 1993 .

[14]  Erik T. Mueller,et al.  Open Mind Common Sense: Knowledge Acquisition from the General Public , 2002, OTM.

[15]  Nuno Silva,et al.  Ontology Alignment through Argumentation , 2012, AAAI Spring Symposium: Wisdom of the Crowd.

[16]  Leo Obrst,et al.  Ontologies for Corporate Web Applications , 2003, AI Mag..

[17]  Jeff Heflin,et al.  The Semantic Web – ISWC 2012 , 2012, Lecture Notes in Computer Science.

[18]  Sriram Subramanian,et al.  Talking about tactile experiences , 2013, CHI.

[19]  Elena Paslaru Bontas Simperl,et al.  CrowdMap: Crowdsourcing Ontology Alignment with Microtasks , 2012, SEMWEB.

[20]  Pompeu Casanovas,et al.  ODR, Ontologies, and Web 2.0 , 2011, J. Univers. Comput. Sci..

[21]  Zahir Tari,et al.  On the Move to Meaningful Internet Systems 2002: CoopIS, DOA, and ODBASE , 2002, Lecture Notes in Computer Science.