Tag Archives: Semantic Search

Andreas Blumauer

Has Google hi-jacked the Semantic Web?

by , Category: Search Engines

Just recently Google has launched the ‘Knowledge Graph‘ (GKG) which “understands real-world entities and their relationships to one another: things, not strings.” Has Google hi-jacked the idea of the ‘Semantic Web’ or at least its vocabulary?

Sean Golliher has compared the most central concepts of the SemWeb community to the wording of Google in his blog post, for instance: Google doesn´t talk about ‘Linked data’ or ‘URIs’ but rather about ‘things and their relationships’. We don´t know if Google uses standards like RDF but obviously a lot of concepts and ideas developed by the SemWeb community in recent years were implemented in GKG. Some people complain that Google should clearly state that this is an implementation of the ‘Semantic Web’ (which was not invented by Google), others say that most concepts like ‘taxonomies’ have been around for hundreds of years anyway.

I believe that both sides have now a great chance to work together: Whether Google’s goal, to “build the next generation of search, which taps into the collective intelligence of the web and understands the world a bit more like people do”, can be reached or not is a matter of the intelligence of the employees. A lot of potential can be found within the semantic web community: If Google gives credit where it is due, semantic web people will be a bit more inspired to support an eco-system built around GKG – and it won´t last long until an ‘Open Knowledge Graph’ will fit together with Google´s revenue model.

Andreas Blumauer

Introducing SKOSsy – generate thesauri on the fly!

by , Category: Linked Data & Open Data, Ontology Engineering, Search Engines, Text Mining, Tools & Software

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?

Let me introduce you to SKOSsy. 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. Not any domain but nearly any: 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.

Let me give you an example: Imagine you are working for a company which is an international plant builder and you would like to index several thousands of documents the “semantic way”. You have to walk through the following steps:

  1. Identify proper categories in Wikipedia/DBpedia which describe best what your business or your domain is all about. Those categories should contain pages / resources which are related to the documents you would like to index. For example: http://dbpedia.org/resource/Category:Metalworking or http://dbpedia.org/resource/Category:Industrial_automation
  2. After you have selected proper categories SKOSsy will traverse DBpedia for you and collect all resources, their hierarchical and non-hierarchical relations, alternative labels, definitions and other properties and put them together as a valid SKOS thesaurus; this step will last a couple of minutes. (Find the resulting vocabulary here)
  3. Load the resulting thesaurus into PPT, explore it, improve it and enrich it with additional facts.
  4. After you´re done you can generate a tailor-made text extractor by using PoolParty Extractor (PPX) which is the second component of PoolParty product family
  5. With PPX and its extraction model especially curated for your special use case you can extract named entities from your documents automatically and index your documents in a meaningful way.
  6. After a few seconds your semantic search engine is ready to be used. PoolParty Semantic Search (PPS) which is the third PoolParty component will offer some nice facilities like categorized auto-complete, faceted search, content recommendation (similarity search) and smart search suggestions to ease your life as a knowledge worker.

We have constantly discussed the application of thesauri and other knowledge models to improve search over the last years. Many people understood straight away why thesaurus based search is most often much 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”.

With SKOSsy in place those kinds of arguments become weaker and weaker. 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

If you are interested in the results produced by SKOSsy, just send us a short note about your domain or your project and we will send you an invitation as beta-tester or prepare a demo for you.

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Andreas Blumauer

Florian Bauer: I like to view “linked data” as a “single worldwide API”

by , Category: Linked Data & Open Data, Open Government Data, Search Engines, Tools & Software

Florian BauerFlorian Bauer is REEEP’s Operations and IT Director, responsible for the overall operational management of the organisation, the product management of reegle (the search engine for renewable energy and energy efficiency) and the management of the IT landscape of REEEP.

PoolParty Team had the chance to talk with Florian about reegle – information gateway on clean energy.

Could you please give us a brief overview over reegle – what are the targets you are pursuing with this platform?

The main aim of the reegle information gateway (http://www.reegle.info) is to provide a one-stop gateway to comprehensive, high-quality and up-to-date information on clean energy. By making this information accessible to stakeholders in the field around the world, and by presenting it in a user-friendly and intuitive format, reegle directly helps to facilitate the transition to low-carbon energy.

The website provides information on renewable energy, energy efficiency and climate change and their various sub-sectors at a global level, and some reegle services actually combine raw data sets from several different sources, put these datasets into context and thus provide enriched information.

reegle is an offshoot of the Renewable Energy & Energy Efficiency Partnership (REEEP), a non-profit, specialist change agent aiming to catalyze the market for renewable energy and energy efficiency, with a primary focus on emerging markets and developing countries.

The new reegle data portal (data.reegle.info), launched in 2011, has established reegle as a publisher and consumer of Linked Open Data in the energy sector. It provides key clean energy datasets free for re-use using Linked Open Data W3C standards.

reegle consists of two components: one is the semantic search engine (http://www.reegle.info/), the other is the linked data portal (http://data.reegle.info/) – What are your target groups, and which typical problems of the clean energy domain can you solve with these services?

For reegle.info, our target groups are primarily project developers, financiers and government policy-makers. These users can access high-quality information on clean energy-related issues with the set of tools we provide: a special web search, a catalogue of more than 1700 key stakeholders, a map view for geographical browsing, a clean energy glossary, and an energy country profiles function.

The energy country profiles are typical of what we’re trying to achieve. Here, we take information from many different providers and combine it all to present one comprehensive information dossier on renewable energy and energy efficiency in that particular country. This means that in one location you have the country’s most important energy-related information ranging from key statistics, and current regulations to key players in the energy field in both public and private sectors.

For our data portal, the target group is a more technical one: primarily IT developers and open data specialists who want to create new mash-ups and integrate data from reegle into other websites. One of the first using these reegle data sets is the OpenEI.org website, another key portal in the energy field.

Open data is not the same as linked open data. Why did you choose to build your services around W3C´s linked data paradigm and/or standards like RDF?

Tim Berners-Lee once mentioned that he likes to compare the progressive ways of offering data with the “stars system” used to rate hotels. You get:

* for making data public (in any format)
** for machine-readable formats (structured data)
*** if the data is offered in a non-proprietary format
**** if you use URIs to identify things, so people can point to your datasets
***** for linking to other people’s data to provide context

So, as you can imagine, our goal is for reegle to be firmly in the 5-star category, and to establish reegle as an avant-garde tool in energy data.
I also like to view “linked data” as a “single worldwide API”. If the old web was like a huge book, the new semantic web is like a huge database, and SPARQL is the way to ask for information – by sending a query through the SPARQL Endpoint. RDF is the language that offers all possibilities to describe a given dataset with all of the necessary information, including any links to other datasets. Therefore RDF data and SPARQL endpoints provide a powerful tool to find and filter datasets and are crucial, base parts of the semantic web’s architectural layers. On reegle the SPARQL endpoint and the description of the structure of our RDF files is online on our clean energy open data portal.

You also decided to build a SKOS based domain thesaurus for clean energy which now plays an important role to improve the search experience at reegle.
Which experiences have you gained so far from this effort? Which obstacles did you have to overcome?

The SKOS-based renewable energy thesaurus can be seen as the “heart” of reegle as it provides the basis for a lot of related services in reegle, including the refinement suggestions for search results, the auto-completion options and the glossary links between defined terms and their synonyms and related terms.

We decided to use SKOS because we think it is the best language for building a formal and controlled vocabulary for thesauri in a semantic web context, without adding too much complexity. Although it is a simple language, you really still need IT experts to use it to build a thesaurus – domain experts with additional IT skills (hard to find!).

So in our case, we decided to use a scalable and easy-to-use thesaurus server called “PoolParty”. Using this system drastically reduced the complexity, and allowed us to concentrate on the actual building of the thesaurus with our domain experts, and to spend less time on transferring the knowledge into data sets.

What are your future plans with reegle?

Currently we’re working on restructuring the site to better highlight our new added-value services such as the clean energy country profiles. We are also planning to further develop our thesaurus to include climate-compatible development terms and we’ll soon release a wordpress plug-in to insert this thesaurus into clean energy blogs. One of the most exciting projects we are actually working on is the development of “dossier pages”, where we will provide relevant information to several topics mashed up on one page using semantic web technologies. This is part of the EU funded SCMS (“semantic content management system”) project.

Andreas Blumauer

Why Wolfram Alpha won´t replace Google

by , Category: Search Engines

If Nova Spivack and Doug Lenat are positive with what they have seen from Wolfram Alpha, I am also close of being convinced that the internet community won´t be dissapointed by Alpha´s first release. Just remember, which hype was caused by Cuil´s PR-strategy of spreading news about their first release throughout the blogosphere, and scarcely anybody would talk about this engine anymore.

After all what I have read about Wolfram Alpha, one thing obviously can be stated: Wolfram Alpha will be a perfect addition to traditional search engines like Google, but will never replace it. For example: In the first paragraph of this blog I have used Google Services like “Google Blog Search” or “Google Trends” to prove some of my statements (in a broader sense: to give answers to those, who want to know, why this is my opinion). Such services Alpha won´t deliver, but it will do other things much better than Google. Doug Lenat:

At one extreme is, say, Google, which responds to almost anything like a faithful puppy bringing in the morning newspaper without understanding much of anything it’s fetching (recognizing words in what it returns, often leading to amusing or hair-raising inappropriate “ads” being displayed, and leading to tons of false positives and false negatives).  At the other extreme is, say, Cyc, which only can answer a small fraction of user queries, but can answer ones that require common sense (not just common sense queries like “Do surgeons often operate on themselves?”, but ones where the logical application of such knowledge is required to correctly disambiguate and parse the user’s query containing pronouns, elisions, ambiguous words, ellipsis, and so on) and where every piece of the query and every piece of the answer is as deeply understood as, say, arithmetic.  Wolfram Alpha is somewhere around the geometric mean of those two extremes.

Search engines or question answering machines (QA) which understand the meaning of the query and/or of the result are not completely new and some of them are really useful like good old START.

But the point is: In many cases of information demand people can´t express the right question.

Why didn´t START become the default browser if it can even answer questions? I think the USP of Alpha will be, that it can give the right answer to more questions than any other QA machine before. But still, the real “search engine revolution” won´t happen, until engines will be able to help users to formulate the proper questions and will help to interprate the right results. Therefore we need to rethink some search paradigms from scratch.