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Project deliverable Open Access

Lynx D3.4 Intermediate information retrieval and recommender services

Maria Khvalchik

Research group(s)
Artem Revenko
Researcher(s)
Christian Sageder

The Lynx project aims at facilitating cross-border compliance for European enterprises. This requires the gathering of relevant regulations, case laws, best practices and standards, and their provision through accessible means, which enable end users (in the form of law firms, in-house lawyers, SMEs and the general public) to find answers to their regulatory related needs with ease. In order to provide such accessible means, the Lynx partners had brought together their technical expertise in the fields of Information Extraction, Semantic Web, Knowledge Management, and Document Management, to create an integrated solution.

An essential part of the solution provided by Lynx is the automatic analysis of documents in order to facilitate their discovery and retrieval by the end user. These analyses have three primary objectives:

  • To extract information contained within the documents that is relevant to user queries.
  • To put documents in context, in terms of relations they hold to other documents.
  • To make documents accessible in different languages.

In order to realize these objectives, Work Package 3 serves to develop a series of services that extract various types of information from the documents, and store the information in a well-organized, standards-compliant knowledge graph, which we call the Legal Knowledge Graph (LKG). This LKG will contain not only information about these documents, but also information extracted from them, as well as general knowledge from the compliance domain, in the form of controlled vocabularies.

The services that are reported in this deliverable are Information Retrieval and Recommender Services. Search service provides a lookup through all the corpora and retrieves related documents corresponding to a given query. Question Answering takes in a natural language question and returns the most promising answer. Semantic Similarity service adds extra knowledge that is useful for the applications mentioned above.

Being an intermediate report, not all of the services have been fully implemented. However, large amounts of work have been done in the standardization of the service architecture, deployment procedure and data interchange format. Furthermore, demonstration implementations are available for most of the services listed here, which are being used to test interoperability among them. Finally, some of these services are the result of ongoing research, and thus their initial version is already an innovation success.

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