Interpretation I

Session 2.5

Time: 
Tuesday, September 12, 2017 - 16:00 to 17:30
Place: 
Room 10

Talks

Sebastian Bader and Jan Oevermann

Research & Innovation

Semantic Annotation of Heterogeneous Data Sources: Towards an Integrated Information Framework for Service Technicians

Service technicians in the domain of industrial maintenance require extensive technical knowledge and experience to complete their tasks. Some of the needed knowledge is made available as document-based technical manuals or service reports from previous deployments. Unfortunately, due to the great amount of data, service technicians spend a considerable amount of working time searching for the correct information. Another challenge is posed by the fact that valuable insights from operation reports are not yet considered due to insufficient textual quality and content-wise ambiguity. In this work we propose a framework to annotate and integrate these heterogeneous data sources to make them available as information units with Linked Data technologies. We use machine learning to modularize and classify information from technical manuals together with ontology-based autocompletion to enrich service reports with clearly defined concepts. By combining these two approaches we can provide an unified and structured interface for both manual and automated querying. We verify our approach by measuring precision and recall of information for typical retrieval tasks for service technicians, and show that our framework can provide substantial improvements for service and maintenance processes.

Najmeh Mousavi Nejad, Simon Scerri and Sören Auer

Research & Innovation

Semantic Similarity based Clustering of License Excerpts for Improved End-User Interpretation

With the omnipresent availability and use of cloud services, software tools, Web portals or services, legal contracts in the form of license agreements or terms and conditions regulating their use are of paramount importance. Often the textual documents describing these regulations comprise many pages and can not be reasonably assumed to be read and understood by humans. In this work, we describe a method for extracting and clustering relevant parts of such documents, including permissions, obligations, and prohibitions. The clustering is based on semantic similarity employing a distributional semantics approach on large word embeddings database. An evaluation shows that it can significantly improve human comprehension and that improved feature-based clustering has a potential to further reduce the time required for EULA digestion. Our implementation is available as a Web service, which can directly be used to process and prepare legal usage contracts.

Isaiah Onando Mulang', Kuldeep Singh and Fabrizio Orlandi

Research & Innovation

Matching Natural Language Relations to Knowledge Graph Properties for Question Answering

Research has seen considerable achievements concerning the translation of natural language patterns into formal queries for Question Answering based on Knowledge Graphs (KG). The main challenge exists on how to identify which property within a Knowledge Graph matches the predicate found in a Natural Language (NL) relation. Current approaches for formal query generation attempt to resolve this problem mainly by first retrieving the named entity from the KG together with a list of its predicates, then filtering out one from all the predicates of the entity. We attempt an approach to directly match an NL predicate to KG properties that can be employed within QA pipelines. In this paper, we specify a systematic approach as well as providing a tool that can be employed to solve this task. Our approach models KB relations with their underlying parts of speech, we then enhance this with extra attributes obtained from Wordnet and Dependency parsing characteristics. From a question, we model a similar representation of query relations. We then define distance measurements between the query relation and the properties representations from the KG to identify which property is referred to by the relation within the query. We report substantive recall values and considerable accuracy from our evaluation.