Introducing PoolParty GraphSearch: Cognitive Search based on Graphs

What is PoolParty GraphSearch?

With PoolParty GraphSearch companies can search over a variety of content types and business objects and analyze the data on a more granular level. All content and data repositories that are connected to GraphSearch are annotated with semantic metadata that makes the search, recommendation and analytics operations highly precise. GraphSearch is a front-end application put on top of a semantic infrastructure and an API providing the following features:

  • Ontology-based data access (OBDA)
  • Faceted search including hierarchies
  • Autocomplete combined with context information
  • Custom views on entity-centric and document-centric data
  • Statistical charts for the unified data repositories
  • Plug-in system for recommendation and similarity algorithms

How does it work?

Business users query knowledge assets in GraphSearch along data models. As multiple systems can be connected to GraphSearch, the variety of knowledge models are additionally linked by an ontology layer.

System administrators can define which part of the ontology and corresponding entities in the taxonomy should be used in the GraphSearch front-end application. That way, they define specific views on data. They can also provide multiple search spaces within GraphSearch and enable the user to switch between them. A search space is a customized search configuration over a specific data set. The selected search facets for each search space are derived from the knowledge graph.

GraphSearch can be enhanced with recommendation algorithms. These can work with similarity-based recommendations, or for some use cases, a matchmaking algorithm is more suitable. The research team of Semantic Web Company has a strong focus on machine learning and is continuously extending the library of machine learning algorithms in GraphSearch.

Data analytics functionalities support the business user to derive even more granular insights. Search facets can be combined into statistical charts and display which kind of data is actually available for specific topics.

Agile Data Management and Integration

The implementation of PoolParty GraphSearch is the beginning of consolidating data silos without data migration. Various functional roles have to work together in order to deliver a unified data environment. PoolParty takes the heterogeneous technical backgrounds of the involved professionals into consideration.

Specific user-friendly solutions support the whole knowledge management team in their collaborative work processes:

  • Subject matter experts can define a semantic data layer to describe the meaning of metadata in the PoolParty taxonomy management tool.
  • Knowledge engineers can link separate taxonomies and maintain the knowledge graph in the same tool.
  • Information architects and developers can link various content and data repositories with the semantic metadata via the PoolParty API.
  • Data scientists can adapt embedded machine-learning algorithms to finetune the search, classification, and recommendation results that are mainly derived through the knowledge graph.
  • This semi-automatic knowledge engineering approach sustains that the query results will gradually get more precise and applicable to a continuously growing data environment.
  • On top of that, GraphSearch enables business users to search over data repositories and analyze available information.

Want to learn more?

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SKOS as a Key Element in Enterprise Linked Data Strategies

The challenges in implementing linked data technologies in enterprises are not limited to technical issues only. Projects like these deal also with organisational hurdles to be crossed, for instance the development of employee skills in the area of knowledge modelling and the implementation of a linked data strategy which foresees a cost-effective and sustainable infrastructure of high-quality and linked knowledge graphs. SKOS is able to play a key role in enterprise linked data strategies due to its relative simplicity in parallel with its ability to be mapped and extended by other controlled vocabularies, ontologies, entity extraction services and linked open data.

Read the full paper >>>

Open Data for Enterprises

I gave a short talk at yesterday´s “Open Governemt Data BusinessDay” in Vienna. I was talking about “Open Data for Enterprises” pointing out the different needs enterprises have than typical “app-makers”.

Open data is obviously socio-politically relevant and helps to reduce administrative costs. It is kind of an infrastructure which is „invisible“ for the business community. But to make it attractive for enterprises, open/external data obviously should be integrable with internal databases. Are linked data and open semantic web standards the solution for this challenge?

My first experiences with Twine

Today finally I logged in to Twine the first time. I was reading yesterday about some shortcomings of the system, so I was keen on trying out the system by myself to get my own impression.

It´s true that the system isn´t as easy to understand as del.icio.us or other bookmarking tools. It takes a while until you get used to all those additional ways you can navigate through the system. Remember: “Twine looks at content and parses it automatically for the names of people, places, organizations and other subject tags. Users are then able to navigate between related content, view recommended content and connect with recommended people with related interests.” – But the “shortcoming” mentioned by Marshall Kirkpatrick that “… it’s hard to keep track of all the levels and types of information available” I can´t agree with: This has only to do with a general problem, which arises whenever semantic technologies should enhance the user experience. Either you stay with “simple” user-interfaces like Google or del.icio.us or you spend 5 minutes or so to learn a new piece of software which will help you to save time in the future and which helps you to find related information automatically.
On the other hand I was very surprised, that the automatic recommendations Twine makes on how to annotate or describe a new resource is really unsatisfying. Users will only spend time to tag their bookmarks if the machine comes up with some intelligent suggestions. And it´s true, as Marshall says, “most of the web is made up of ugly, non-standard pages.”

So hopefully Twine will add that feature before it will open up to the public (isn´t there a plan to integrate OpenCalais or something similar?), otherwise there will be no “first mainstream semantic web application” but only another prototype of a yet another semweb-app.

Navigating Wikipedia with a little help from a visualisation

One more reason to buy an Apple is Pathway: This program helps to navigate through Wikipedia in a bit more structured way. It visualises pathways through wikipedia and proposes interesting links to other wiki pages. After a while navigating through wikipedia users normally find themselves “totally lost in myriads of loosely related pages. What I needed, was an application that could easily archive the path I follow through Wikipedia pages”, says the author of Pathway.

Thanks to Thomas Fundneider for pointing that out!