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?

• Download White Paper (PDF): The Next Generation of Knowledge Discovery Enabled by the Fusion of Search, Recommendation and Analytics Enabled by the Fusion of Search, Recommendation and Analytics
• Try it out: http://vocabulary.semantic-web.at/GraphSearch/

SPARQL is the new King of all Data Scientist’s tools

Inspired by the development of semantic technologies in recent years, in statistical analysis field the traditional methodology of designing, publishing and consuming statistical datasets is evolving to so-called “Linked Statistical Data” by associating semantics with dimensions, attributes and observation values based on Linked Data design principles.

The representation of datasets is no longer a combination of magic words and numbers. Everything is becoming meaningful when URIs replace their positions as dereferencable resources, which further establishes the relations between resources implicitly and automatically. Different datasets are no longer isolated and all datasets share a globally, uniquely and uniformly defined structure.

With “RDF Data Cube Vocabulary” (http://www.w3.org/TR/vocab-data-cube/) there is already a W3C Recommendation available for linked statistical data . At this point, it is time to start building data-oriented applications and services with the traditional statistical computing languages such as R, while benefiting from the omnipotent semantic power of the SPARQL query language.

Most of the statistical analysis functions are set operations performed on subsets of a dataset (i.e., a slice, a facet, etc.). Calculation is dull machine work but how to group and create a subset is actually the innovation point to produce analytics. Thanks to SPARQL, now this subset can be created from a semantic perspective instead of mathematics and statistics.

Compared to the traditional filtering way using SQL queries, SPARQL queries eliminate the boundaries of data among datasets and among databases.

An example query can be “list the bestsellers of a supermarket for category science fiction movie in year 2014”. Someone may point out that it is also feasible with SQL if the database schema consists of all relevant fields. Well, it is absolutely correct. But what if there are more conditions such as “during weekends, directed by an American director, casted by European actors”? Is it necessary for a supermarket to maintain such data sets? Assume that there is a supermarket of which the boss is a movie fans and he would like to maintain such data and SQL is working perfectly so far. Can we reuse this query, and accordingly the Web application for another supermarket? Here we have good reasons why we use SPARQL. Any accessible resource can be used to construct the query results.

SPARQL is the new King of all Data Scientist’s tools because …

1. SPARQL is close to how human beings actually think about the world.
2. With SPARQL you can query knowledge graphs.
3. SPARQL is based on knowledge models that can combine mindsets of subject-matter experts, data engineers and information architects.
4. SPARQL is to the Semantic Web and the Web in general what SQL is to relational databases.
5. SPARQL is a W3C recommendation and is supported by many different database vendors, so it doesn’t cause lock-in effects as we’ve become used to with various types of SQL engines (which are not standardized at all).
6. With SPARQL you benefit from the potential to make a collection of data sources look and query like one big database.
7. SPARQL provides pattern based search functionality. With such search capabilities you can find out unknown linkages or non-obvious patterns that give you new insights into your data.
8. Not only is SPARQL a standardized query language, also the access via web interfaces is standardized (this is called a SPARQL endpoint). This makes the integration of different data sources a lot easier.
9. SPARQL is also a standardized update and graph traversal language.
10. SPARQL is a standardized protocol producing standardized results, thus making it a complete API alleviating developers from the necessity to reinvent an API with every single application.
11. With SPARQL you can query over structured and unstructured information as a whole.
12. SPARQL allows you to explore data. In contrast to traditional ways to query databases where knowledge about the database schema/content is necessary SPARQL allows you to ask “tell me what is there”.
13. SPARQL property paths offer completely new ways to explore a data set, e.g. by detecting ‘hidden links’ between business objects.
14. With SPARQL you can define inference rules to gain new information from existing facts.

Three shades of metadata

Search terms are like keys to documents or to any other type of content. In our days, not only the volume of available documents is increasing rapidly, but also the size of the keychain is growing.

Before we start to discuss how to organize/index the documents themselves, we should first talk about methods to organize the keys.

Take a look at these three approaches:

On the left-hand side you can see a nice example of implicit semantics. The old receptionist knows exactly which key fits into which lock. This knowledge is implicit and can be remembered by the shapes of the keys. Before the old receptionist has retired and the knowledge would have gone, labels have been attached to every key. The semantics of the keys has become more explicit, but this is still quite ambiguous (as visualised in the center column). There is no index of all the labels and colours being used, neither an explicit methodology how new keys should be labeled. As the hotel grows, the labeling system becomes quickly a mess. On the right-hand side the solution for this problem is offered: Not only the semantics of the keys becomes more explicit, but also the semantics of the semantics. For instance, the position of a key represents the position of the room, which can be unlocked by this. The number of the row of the key cabinet represents the storey of the room being unlocked, etc.

This methodology in order to organise keys helps to orientate and to remember; it can be explained with low effort to any new receptionist and it can be scaled-up in case your hotel should grow in the next few years.

Why semantic knowledge graphs matter

Most information professionals already know: separation of content and presentation helps to manage and deliver complex information. This can only be done by using enriched structured content. Some call this intelligent content.

But why exactly is metadata per document (some call it “tagging”) not enough?

Here is a very brief slide-deck, which explains the difference between the traditional approach and the graph-based approach to develop not only a metadata layer seperated from the content layer, but also a knowledge layer on top of it.

Why semantic knowledge graphs matter

10 Business Solutions based on Linked Data and Semantic Technologies

2013: Linked data technologies have matured, linked data community has grown, interest both from private industry and from the government is considerable.

A key questions that I get asked again and again is: which business solutions can be built based on linked data? Here is ‘our’ list of 10 solutions:

Semantic FAQ-System

PoolParty has its brandnew FAQ-system to find answers for frequently asked questions instantly. The system helps to find questions, answers and corresponding pages in the documentation – with a little help from semantics: the FAQ-system is based on PoolParty PowerTagging and automatic content recommender for Confluence and can be seen as a ‘general approach’ for digital assistants in the fields of self-servicing and e-care especially for complex services and products.

The PoolParty approach for efficient knowledge modeling

The PoolParty approach for efficient knowledge modeling is based on methods from

• text analytics and text mining
• linked data management
• SKOS thesaurus modeling
• ontology engineering and
• semantic wikis

and recombines these techniques to a unique approach to create complex knowledge models which can be further used for all of the above mentioned tasks, semantic search, and knowledge discovery in big data sets.

Using DBpedia to generate SKOS thesauri

In recent years, we have constantly discussed the application of thesauri and other knowledge models to improve search. Many people understand that thesaurus based search is in many cases better than search algorithms purely based on statistics. Of course the big contra always was, “the costs are too high to establish a good-enough thesaurus or even a high-quality one”.

Imagine you could generate any thesaurus you would like for nearly any knowledge domain you can think of with quite a good quality! Sounds impossible? Reminds you of all the promises made by text mining software which generates “semantic nets” from scratch?

Here at the Semantic Web Company we have been working on SKOSsy for a while. I will explain what this web service can do for you:

SKOSsy generates SKOS based thesauri in German or in English for a domain you are interested in. SKOSsy extracts data from DBpedia, so it can cover anything which is in DBpedia. Thus, SKOSsy works well whenever a first seed thesaurus should be generated for a certain organisation or project. If you load the automatically generated thesaurus into an editor like PoolParty Thesaurus Manager (PPT) you can start to enrich the knowledge model by additional concepts, relations and links to other LOD sources. But you don´t have to start in the open countryside with your thesaurus project.

With SKOSsy in place custom-tailored text extractors can be produced with low effort. To sum up,

• SKOSsy makes heavy use of Linked Data sources, especially DBpedia
• SKOSsy can generate SKOS thesauri for virtually any domain within a few minutes
• Such thesauri can be improved, curated and extended to one´s individual needs but they serve usually as “good-enough” knowledge models for any semantic search application you like
• SKOSsy based semantic search usually outperform search algorithms based on statistics since they contain high-quality information about relations, labels and disambiguation
• SKOSsy works perfectly together with PoolParty product family

Which domains are you interested in? Give it a try!

Linked Data for the Masses

Leipziger Semantic Web Days 2011 take place today and tomorrow, and I like this year´s motto: “Linked Data for the Masses”. I think it´s time to dispel the myth that the “semantic web will never become reality”.

Thousands of people including myself have been working on the development of the semantic web in recent years, and just to give a short example of applications and companies which use this mature technology stack in 2011 for various purposes I have prepared a keynote talk I will give tomorrow in Leipzig:

Another way how to make the power of linked data comprehensible to newcomers are short screencasts, take a look at the latest video of the PoolParty team about semantic search:

Some videos, mind maps and podcasts about the Social Semantic Web

In 2010 I gave some talks about hot topics around the Social Semantic Web, and some of them were recorded and streamed over the web (two of them are in German, three in English). All of them were exciting events for me. Again, I learned a lot when preparing the presentations and when listening to the other presentators who I met at the following events:

April 2010 – Digitalks #13 Semantic Web
Auf der Suche nach der besseren Suche: Wie semantische Suche und Semantic Web unsere Arbeitsweisen verändert

August 2010 – GLOBArt 2010
Erwartungen an ein ZukunftsWeb

http://www.ustream.tv/recorded/9039123

September 2010 – ISKO UK Linked Data
PoolParty: SKOS Thesaurus Management utilizing Linked Data

Audio MP3 and Slides

November 2010 – TEDxVienna
Open or closed? Intranets in the Age of the Internet

December 2010 – European Semantic Technology Conference 2010
The role of SKOS in a Web of Data – some business use cases

Link: http://videolectures.net/estc2010_blumauer_drswd/